diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..1c77d5b Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 3c7b49c..af1c461 100644 --- a/.gitignore +++ b/.gitignore @@ -172,4 +172,4 @@ cython_debug/ .history/ # Built Visual Studio Code Extensions -*.vsix \ No newline at end of file +*.vsix.DS_Store diff --git a/README.md b/README.md deleted file mode 100644 index 5457730..0000000 --- a/README.md +++ /dev/null @@ -1,132 +0,0 @@ -# CosmicBeats Simulator -Our vision is to create a versatile space simulation platform that caters to individuals with diverse research interests, including networking, AI, computing, and more. Unlike traditional simulators tied to specific research applications, our design allows for seamless integration of various space-related research verticals. CosmicBeats originated from the paper: Shenoy and Chabra et al., [**CosMAC: Constellation-Aware Medium Access and Scheduling for IoT Satellites**](https://www.microsoft.com/en-us/research/publication/cosmac-constellation-aware-medium-access-and-scheuduling-for-iot-satellites/), ACM MobiCom, 2024. - -The current version of simulator offers the capability to simulate various facets of satellite operation and communication, encompassing orbital dynamics, wireless communication, IoT networks, computation, imaging, and more. We have included numerous example scenarios that can be simulated using these functionalities, such as direct-to-satellite IoT communication networks, distributed ground station setups, imaging satellite operations, and others. However, users are not limited to these predefined scenarios. By making simple modifications in the config file, users can simulate many additional scenarios. Moreover, the platform's codebase allows for easy integration of new capabilities, enabling users to build upon the existing features smoothly. Additionally, the simulator provides runtime APIs that allow seamless interaction with the simulator for real-time control of operations. This feature facilitates easy integration of the simulator with external programs, requiring minimal or no modifications to the simulator's codebase. For instance, by utilizing the runtime APIs, users can effortlessly interface a scheduler for the ground station network. - -We are thrilled to have you as a contributor to our project as our goal is to make this project community driven. Your valuable expertise and passion will play a vital role in shaping this space simulation platform for diverse research interests. Whether you are a developer, researcher, or enthusiast, there are plenty of opportunities to make a significant impact. Let's work together to create an exceptional and collaborative space simulation platform. - -## Design of the simulator -![Simplified core architecture](/figs/simulator_architecture.svg) - -At the core of our simulator are "nodes" and "models." A node represents a physical endpoint entity, such as satellites, ground stations, user terminals, IoT devices, among others. Each node consists of one or multiple models, each of which replicates the functionality or behavior of the corresponding node component. These models can represent software or hardware elements, such as satellite batteries or even the orbital movements of satellites. Importantly, the user can make zero-code configuration of the models within each node based on their specific simulation requirements. This customization ensures that the simulation setup remains tailored to the intended research purpose. - -Interactions between models are critical for simulating real-world scenarios accurately. For instance, a computation model might need to assess the power availability from the battery model before performing its operations. To facilitate such interactions, a model exposes public APIs accessible by other models. - -Our simulator employs a discrete-time approach, executing simulated operations at regular intervals known as "epochs". This ensures consistent and predictable execution. During each epoch, the simulator invokes the `Execute()` method of each model within the nodes, effectively simulating the desired operations associated with those models. - -With our space simulation platform, researchers can explore and contribute to a wide array of space-related research domains, thanks to its adaptable design and the ability to model diverse scenarios. You have the opportunity to contribute in various ways, such as introducing new nodes, models, SMAs, summarizers, APIs, and configs. Additionally, you can participate in fixing issues, enhancing computations, and much more. Your contributions are not limited, and we welcome your innovative ideas and improvements. - -## Installation and setup -The simulator is developed using Python. To get started, ensure you have the most recent version of Python installed. Use `pip` to install the required package listed in [requirements.txt](/requirements.txt), and you'll be ready to go. - -```bash -pip install -r requirements.txt -``` - -Anaconda can be used as well to setup the platform. Use [environment.yml](/environment.yml) to create the environment with required packages. - -## Quick start -Running the initial simulation scenario is swift and simple; just execute the [main.py](/main.py) - -```bash -python main.py -``` -Upon execution, the simulator will output a sequence of logs in your terminal. - -If you wish to experiment with an alternative simulation configuration, you can provide the path to any configuration file from the [configs](/configs/) directory as an argument to the main.py script. - -```bash -python main.py configs/config.json -``` - -## Usage -Most of the simulator's usage involves customizing the nodes and models within the config file. Therefore, it is essential to comprehend the concepts of nodes, models, and configuration to effectively utilize the simulator. - -### Node -To learn about node, please refer to [this](/src/nodes/README.md). - -### Model -To learn about model, please refer to [this](/src/models/README.md). - -### config -The configuration of the simulation setup is maintained in a JSON file. To know more on how to work with the configuration file, please refer to [this](/configs/README.md). - -### Running the simulation -The sole interface to the simulator is the [Simulator class](/src/sim/simulator.py), meaning that users can run the simulator by simply creating an instance of this class. - -```Python -from src.sim.simulator import Simulator - -_configFilePath = "configs/config.json" - -_sim = Simulator(_configFilePath) -_sim.execute() -``` -The runtime APIs can also be accessed through the Simulator class by invoking `call_RuntimeAPIs()` method. - -To learn more about the Simulator class and architecture of the simulator, please refer to [this](/src/sim/README.md). - -### Analytics -The primary objective of a simulation is to generate insights, achieved by analyzing the logs produced by the simulator. As these insights are tailored to each specific use case, reusing the code written for analytics can be challenging. However, in our design, we prioritize creating an analytics pipeline that is highly adaptable and can be easily repurposed for various scenarios. To learn more about the analytics pipeline, please refer to [this](/src/analytics/README.md). - -### Examples -We will attempt to execute a series of end-to-end simulation examples. ** It is essential to note that the data utilized for these simulation examples may not accurately represent real-world values.** We have used public TLE files from [https://celestrak.org/](https://celestrak.org/). - -#### Satellite based IoT network -In this simulation, there are 1000 IoT devices on Earth that engage in direct communication with over 150 LEO (Low Earth Orbit) satellites. The satellites collect data from the IoT devices, store it onboard, and transmit the data to ground stations whenever an opportunity arises. Find the corresponding config file [here](/configs/examples/config_1000iot.json). - -```bash - python examples/iotnetwork.py -``` -It might take a while since we are dealing with a large number of nodes. - -Once the simulation is complete, we are good to go for analyzing the logs and generating results. We have multiple sample scripts for log analysis available [here](/examples/analytics_sample/) that use our [SMAs and Summarizers](/src/analytics/) to analyze the logs and generate insights. For this example, we will use [analyze_datalayer.py](examples/analytics_sample/analyze_datalayer.py) to find end-to-end delay of our simulated IoT network. This script analyzes the logs stored in the `exampleLogs/` temporary directory and returns the results in several metrics. - -```bash -python -Wignore examples/analytics_samples/analyze_datalayer.py exampleLogs/ -``` - -#### Image satellite -In this example, we simulate a constellation of satellites capturing earth image, called Earth Observation satellites. Find the corresponding config file [here](/configs/examples/config_imagesat.json). - -```bash - python examples/imagesatellite.py -``` -It dumps the log in the `imagingLogs/` directory. Once the simulation is complete, we run analysis on the logs by running an example analyzer script that returns the power profile of satellites in the constellation. - -```bash -python -Wignore examples/analytics_samples/analyze_power.py imagingLogs/ -``` - -### Test -We have provided several test cases [here](/src/test/). You can use `pytest` package to evaluate the tests. -```bash -pytest -Wignore src/test/ -``` - -## License -Please refer to [LICENSE.txt](/LICENSE.txt) - -## Security -Please refer to [SECURITY.md](/SECURITY.md) - -## Support -Please refer to [SUPPORT.md](/SUPPORT.md) - -## Contributing - -This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. - -When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. - -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or -contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. - -## Trademarks - -This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft -trademarks or logos is subject to and must follow -[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). -Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. -Any use of third-party trademarks or logos are subject to those third-party's policies. diff --git a/Relatorio_ED2_241327.docx b/Relatorio_ED2_241327.docx new file mode 100644 index 0000000..2703bf5 Binary files /dev/null and b/Relatorio_ED2_241327.docx differ diff --git a/SECURITY.md b/SECURITY.md deleted file mode 100644 index e138ec5..0000000 --- a/SECURITY.md +++ /dev/null @@ -1,41 +0,0 @@ - - -## Security - -Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). - -If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below. - -## Reporting Security Issues - -**Please do not report security vulnerabilities through public GitHub issues.** - -Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). - -If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey). - -You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). - -Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: - - * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) - * Full paths of source file(s) related to the manifestation of the issue - * The location of the affected source code (tag/branch/commit or direct URL) - * Any special configuration required to reproduce the issue - * Step-by-step instructions to reproduce the issue - * Proof-of-concept or exploit code (if possible) - * Impact of the issue, including how an attacker might exploit the issue - -This information will help us triage your report more quickly. - -If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs. - -## Preferred Languages - -We prefer all communications to be in English. - -## Policy - -Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd). - - diff --git a/cenarios.txt b/cenarios.txt new file mode 100644 index 0000000..a4aeada --- /dev/null +++ b/cenarios.txt @@ -0,0 +1,25 @@ +1. Monitoramento de Desastres (Urgência vs. Volume): + +Cenário: Sensores IoT na Amazônia detectam um foco de incêndio. + +Desafio: O sensor envia 100 fotos. Enviar tudo para a Terra demora 10 minutos (lento demais). + +Solução MEC: O satélite processa as fotos, confirma o fogo e envia apenas um alerta de 1KB para a Terra em 5 segundos. + +Dificuldade para o Scheduler: Priorizar essa tarefa crítica acima de tarefas de rotina (ex: atualização de software de um trator). + +2. Agricultura de Precisão (Gargalo de Energia): + +Cenário: Um enxame de drones sobre uma plantação envia dados hiperespectrais para a constelação. + +Desafio: Os satélites estão passando pela "zona de eclipse" (sombra da Terra), onde não geram energia solar e dependem da bateria. + +Dificuldade para o Scheduler: O algoritmo não pode apenas olhar para a CPU livre. Ele tem que prever: "Se eu mandar essa tarefa pesada para o Satélite A agora, a bateria dele vai acabar antes de ele sair da sombra". O LLM é ótimo para ponderar múltiplas variáveis assim. + +3. Federação de Aprendizado (Privacidade): + +Cenário: Navios militares enviam dados de radar. + +Desafio: Os dados não podem ser enviados para uma estação terrestre em território estrangeiro. Devem ser processados lá em cima. + +Dificuldade para o Scheduler: Restrições geográficas e de segurança. O scheduler deve saber onde o satélite está sobrevoando antes de alocar a tarefa. \ No newline at end of file diff --git a/config_generators/.DS_Store b/config_generators/.DS_Store new file mode 100644 index 0000000..16813fe Binary files /dev/null and b/config_generators/.DS_Store differ diff --git a/configs/.DS_Store b/configs/.DS_Store new file mode 100644 index 0000000..e36d21d Binary files /dev/null and b/configs/.DS_Store differ diff --git a/configs/config.json b/configs/config.json index 6eb1376..c85bd44 100644 --- a/configs/config.json +++ b/configs/config.json @@ -22,7 +22,7 @@ }, { - + "type": "SAT", "iname": "SatelliteBasic", "nodeid": 2, @@ -36,6 +36,20 @@ } ] }, + { + "type": "SAT", + "iname": "SatelliteBasic", + "nodeid": 15, + "loglevel": "all", + "tle_1": "1 50985U 22002B 22290.71715197 .00032099 00000+0 13424-2 0 9994", + "tle_2": "2 50985 97.4784 357.5505 0011839 353.6613 6.4472 15.23462773 42039", + "additionalargs": "", + "models":[ + { + "iname": "ModelOrbit" + } + ] + }, { "type": "GS", @@ -198,12 +212,15 @@ "simtime": { "starttime": "2022-11-14 12:00:00", - "endtime": "2022-11-14 12:10:00", + "endtime": "2022-11-14 12:20:00", "delta": 5.0 }, "simlogsetup": { "loghandler": "LoggerCmd", "logfolder": "" + }, + "mec": { + "anomaly_rate": 0.10 } } \ No newline at end of file diff --git a/logs/mec_metrics_BASELINE.csv b/logs/mec_metrics_BASELINE.csv new file mode 100644 index 0000000..2d96e54 --- /dev/null +++ b/logs/mec_metrics_BASELINE.csv @@ -0,0 +1,70 @@ +task_id,region,anomaly,arrival_time_s,decision_time_s,latency_ms,joules_cost,decision_sat_id,success,semantic_compliant,engine +1,EUROPE,,30.0,30.0,0.02,0.001,2,1,1,BASELINE +2,USA,,35.0,35.0,0.015,0.001,15,1,1,BASELINE +3,BRAZIL,,60.0,60.0,0.023,0.001,1,1,1,BASELINE +4,EUROPE,,75.0,75.0,0.016,0.001,2,1,1,BASELINE +5,EUROPE,,80.0,80.0,0.013,0.001,2,1,1,BASELINE +6,EUROPE,,85.0,85.0,0.012,0.001,2,1,1,BASELINE +7,EUROPE,,110.0,110.0,0.013,0.001,2,1,1,BASELINE 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mode 100644 index 0000000..6e0d312 --- /dev/null +++ b/logs/mec_metrics_SLM.csv @@ -0,0 +1,70 @@ +task_id,region,anomaly,arrival_time_s,decision_time_s,latency_ms,joules_cost,decision_sat_id,success,semantic_compliant,engine +1,EUROPE,,30.0,35.0,50.0,0.1,2,1,1,SLM +2,USA,,35.0,40.0,50.0,0.1,15,1,1,SLM +3,BRAZIL,,60.0,65.0,50.0,0.1,1,1,1,SLM +4,EUROPE,,75.0,80.0,50.0,0.1,2,1,1,SLM +5,EUROPE,,80.0,85.0,50.0,0.1,2,1,1,SLM +6,EUROPE,,85.0,90.0,50.0,0.1,2,1,1,SLM +7,EUROPE,,110.0,115.0,50.0,0.1,2,1,1,SLM +8,EUROPE,,130.0,135.0,50.0,0.1,2,1,1,SLM +9,BRAZIL,,140.0,145.0,50.0,0.1,1,1,1,SLM +10,EUROPE,,155.0,160.0,50.0,0.1,2,1,1,SLM +11,EUROPE,,165.0,170.0,50.0,0.1,2,1,1,SLM +12,USA,falha_hardware_camera_esq,190.0,195.0,50.0,0.1,,0,1,SLM +13,EUROPE,,220.0,225.0,50.0,0.1,2,1,1,SLM +14,USA,,235.0,240.0,50.0,0.1,15,1,1,SLM +15,EUROPE,,245.0,250.0,50.0,0.1,2,1,1,SLM +16,EUROPE,,250.0,255.0,50.0,0.1,2,1,1,SLM +17,BRAZIL,,275.0,280.0,50.0,0.1,1,1,1,SLM +18,EUROPE,,280.0,285.0,50.0,0.1,2,1,1,SLM 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+61,USA,,1060.0,1065.0,50.0,0.1,15,1,1,SLM +62,BRAZIL,,1065.0,1070.0,50.0,0.1,1,1,1,SLM +63,EUROPE,,1085.0,1090.0,50.0,0.1,2,1,1,SLM +64,EUROPE,,1090.0,1095.0,50.0,0.1,2,1,1,SLM +65,EUROPE,,1115.0,1120.0,50.0,0.1,,0,1,SLM +66,EUROPE,,1120.0,1125.0,50.0,0.1,2,1,1,SLM +67,EUROPE,,1155.0,1160.0,50.0,0.1,2,1,1,SLM +68,BRAZIL,,1160.0,1165.0,50.0,0.1,1,1,1,SLM +69,BRAZIL,,1190.0,1195.0,50.0,0.1,1,1,1,SLM diff --git a/logs/mec_summary_BASELINE.json b/logs/mec_summary_BASELINE.json new file mode 100644 index 0000000..0e8f655 --- /dev/null +++ b/logs/mec_summary_BASELINE.json @@ -0,0 +1,21 @@ +{ + "engine": "BASELINE", + "total_tasks": 69, + "success_count": 69, + "success_rate": 1.0, + "drop_count": 0, + "drop_rate": 0.0, + "avg_latency_ms": 0.015, + "total_joules": 0.069, + "joules_per_decision": 0.001, + "anomaly_task_count": 8, + "semantic_compliance_rate": 0.913, + "anomaly_compliance_rate": 0.25, + "effective_success_count": 63, + "effective_success_rate": 0.913, + "correct_drop_count": 0, + "avg_ram_utilization_pct": 12.2, + "sat_1_final_battery_pct": 61.9, + "sat_2_final_battery_pct": 21.8, + "sat_15_final_battery_pct": 53.4 +} \ No newline at end of file diff --git a/logs/mec_summary_DRL.json b/logs/mec_summary_DRL.json new file mode 100644 index 0000000..70a3d19 --- /dev/null +++ b/logs/mec_summary_DRL.json @@ -0,0 +1,21 @@ +{ + "engine": "DRL", + "total_tasks": 69, + "success_count": 61, + "success_rate": 0.8841, + "drop_count": 8, + "drop_rate": 0.1159, + "avg_latency_ms": 0.475, + "total_joules": 0.345, + "joules_per_decision": 0.005, + "anomaly_task_count": 8, + "semantic_compliance_rate": 0.9275, + "anomaly_compliance_rate": 0.375, + "effective_success_count": 61, + "effective_success_rate": 0.8841, + "correct_drop_count": 3, + "avg_ram_utilization_pct": 12.2, + "sat_1_final_battery_pct": 67.9, + "sat_2_final_battery_pct": 27.8, + "sat_15_final_battery_pct": 57.4 +} \ No newline at end of file diff --git a/logs/mec_summary_LLM.json b/logs/mec_summary_LLM.json new file mode 100644 index 0000000..81525fa --- /dev/null +++ b/logs/mec_summary_LLM.json @@ -0,0 +1,21 @@ +{ + "engine": "LLM", + "total_tasks": 69, + "success_count": 66, + "success_rate": 0.9565, + "drop_count": 3, + "drop_rate": 0.0435, + "avg_latency_ms": 6111.897, + "total_joules": 345.0, + "joules_per_decision": 5.0, + "anomaly_task_count": 8, + "semantic_compliance_rate": 1.0, + "anomaly_compliance_rate": 1.0, + "effective_success_count": 66, + "effective_success_rate": 0.9565, + "correct_drop_count": 3, + "avg_ram_utilization_pct": 12.2, + "sat_1_final_battery_pct": 63.9, + "sat_2_final_battery_pct": 21.8, + "sat_15_final_battery_pct": 57.4 +} \ No newline at end of file diff --git a/logs/mec_summary_SLM.json b/logs/mec_summary_SLM.json new file mode 100644 index 0000000..4df3240 --- /dev/null +++ b/logs/mec_summary_SLM.json @@ -0,0 +1,21 @@ +{ + "engine": "SLM", + "total_tasks": 69, + "success_count": 62, + "success_rate": 0.8986, + "drop_count": 7, + "drop_rate": 0.1014, + "avg_latency_ms": 50.0, + "total_joules": 6.9, + "joules_per_decision": 0.1, + "anomaly_task_count": 8, + "semantic_compliance_rate": 0.971, + "anomaly_compliance_rate": 0.75, + "effective_success_count": 62, + "effective_success_rate": 0.8986, + "correct_drop_count": 5, + "avg_ram_utilization_pct": 12.2, + "sat_1_final_battery_pct": 65.9, + "sat_2_final_battery_pct": 27.8, + "sat_15_final_battery_pct": 57.4 +} \ No newline at end of file diff --git a/main.py b/main.py index c96d776..3c2eda2 100644 --- a/main.py +++ b/main.py @@ -2,31 +2,48 @@ // Copyright (c) Microsoft Corporation. // Licensed under the MIT license. ''' -from src.sim.simulator import Simulator import sys import time import random +import os +import argparse # <-- A biblioteca mágica para terminais +from src.sim.simulator import Simulator if __name__ == "__main__": - random.seed(0) - _filepath = '' - - #look for the config file path in the command line arguments - - if(len(sys.argv) > 1): - _filepath = sys.argv[1] - else: - _filepath = "configs/config.json" - - _sim = Simulator(_filepath) + # Semente fixa: Garante que os mesmos eventos acontecem para TODOS os motores + random.seed(0) + + # ================================================================= + # CONFIGURAÇÃO DO TERMINAL (CLI) + # ================================================================= + parser = argparse.ArgumentParser(description="CosmicBeats - Simulador NTN-MEC") + + # Argumento 1: O Motor (com opções restritas para evitar erros de digitação) + parser.add_argument('--engine', type=str, default="LLM", + choices=["LLM", "BASELINE", "SLM", "DRL"], + help="Escolha o cérebro: LLM, BASELINE, SLM, ou DRL") + + # Argumento 2: O arquivo de configuração JSON + parser.add_argument('--config', type=str, default="configs/config.json", + help="Caminho para o arquivo config.json") + + args = parser.parse_args() + + # ================================================================= + # INJEÇÃO DA CHAVE SELETORA + # ================================================================= + os.environ["MEC_ENGINE"] = args.engine + + print("\n" + "="*50) + print(f"🚀 INICIANDO COSMICBEATS") + print(f"🧠 Motor Cognitivo : {args.engine}") + print(f"📂 Arquivo Config : {args.config}") + print("="*50 + "\n") + + _sim = Simulator(args.config) _startTime = time.perf_counter() - - # Now, let's start the simulation - _sim.execute() - _endTime = time.perf_counter() - print(f"[Simulator Info] Time required to run the simulation: {_endTime-_startTime} seconds.") - + print(f"\n[Simulator Info] Tempo real de execução: {_endTime-_startTime:.4f} segundos.") \ No newline at end of file diff --git a/models/best_model.zip b/models/best_model.zip new file mode 100644 index 0000000..b3dbb5e Binary files /dev/null and b/models/best_model.zip differ diff --git a/models/drl_agent.zip b/models/drl_agent.zip new file mode 100644 index 0000000..b4c6ca9 Binary files /dev/null and b/models/drl_agent.zip differ diff --git a/plot_results.py b/plot_results.py new file mode 100644 index 0000000..5649d39 --- /dev/null +++ b/plot_results.py @@ -0,0 +1,58 @@ +# plot_results.py +import json +import pandas as pd +import matplotlib.pyplot as plt +import glob +import os + +# 1. Encontra o log mais recente +list_of_files = glob.glob('logs/*.jsonl') +latest_file = max(list_of_files, key=os.path.getctime) +print(f"Plotting results from: {latest_file}") + +# 2. Carrega dados +data = [] +with open(latest_file, 'r') as f: + for line in f: + data.append(json.loads(line)) + +df = pd.DataFrame(data) + +# 3. Processamento para Gráficos + +# --- Gráfico A: Bateria ao Longo do Tempo --- +telemetry = df[df['type'] == 'TELEMETRY'].copy() +telemetry['sat_id'] = telemetry['details'].apply(lambda x: x['sat_id']) +telemetry['battery'] = telemetry['details'].apply(lambda x: x['battery']) + +plt.figure(figsize=(10, 5)) +for sat in telemetry['sat_id'].unique(): + sat_data = telemetry[telemetry['sat_id'] == sat] + plt.plot(sat_data['timestamp'], sat_data['battery'], label=f"Sat {sat}") + +plt.axhline(y=20, color='r', linestyle='--', label='Safe Mode (20%)') +plt.title("Battery Level over Time") +plt.xlabel("Simulation Time (s)") +plt.ylabel("Battery %") +plt.legend() +plt.grid(True) +plt.savefig("logs/plot_battery.png") +print("Saved logs/plot_battery.png") + +# --- Gráfico B: Status das Tarefas --- +# Conta eventos de sucesso vs falha +completed = len(df[df['type'] == 'TASK_COMPLETED']) +dropped = len(df[df['type'] == 'TASK_DROPPED']) # OOM +rejected = len(df[df['type'] == 'TASK_REJECTED']) # Safe Mode +sched_fail = len(df[df['type'] == 'SCHEDULER_REJECT']) # LLM não achou ninguém + +labels = ['Completed', 'OOM Dropped', 'Safe Mode Rejected', 'No Route'] +values = [completed, dropped, rejected, sched_fail] + +plt.figure(figsize=(8, 8)) +plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=140) +plt.title("Task Outcome Distribution") +plt.savefig("logs/plot_outcomes.png") +print("Saved logs/plot_outcomes.png") + +plt.show() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 8c2c6b4..963ebe1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,95 @@ -astropy==5.3.1 -dask==2023.7.1 -geopy==2.3.0 -gradio==3.39.0 -numpy==1.25.1 -pandas==2.0.3 -plotly==5.15.0 -skyfield==1.46 +aiofiles==24.1.0 +annotated-doc==0.0.3 +annotated-types==0.7.0 +anyio==4.11.0 +astropy +astropy-iers-data==0.2025.11.3.0.38.37 +brotli==1.2.0 +cachetools==6.2.2 +certifi==2025.10.5 +charset-normalizer==3.4.4 +click==8.3.0 +cloudpickle==3.1.2 +dask==2025.11.0 +fastapi==0.121.0 +ffmpy==0.6.4 +filelock==3.20.0 +fsspec==2025.10.0 +geographiclib==2.1 +geopy==2.4.1 +google-ai-generativelanguage==0.6.15 +google-api-core==2.28.1 +google-api-python-client==2.187.0 +google-auth==2.43.0 +google-auth-httplib2==0.2.1 +google-generativeai==0.8.5 +googleapis-common-protos==1.72.0 +gradio==5.49.1 +gradio_client==1.13.3 +groovy==0.1.2 +grpcio==1.76.0 +grpcio-status==1.71.2 +h11==0.16.0 +hf-xet==1.2.0 +httpcore==1.0.9 +httplib2==0.31.0 +httpx==0.28.1 +huggingface_hub==1.1.2 +idna==3.11 +Jinja2==3.1.6 +jplephem==2.23 +locket==1.0.0 +markdown-it-py==4.0.0 +MarkupSafe==3.0.3 +mdurl==0.1.2 +narwhals==2.10.2 +numpy +orjson==3.11.4 +packaging==25.0 +pandas==2.3.3 +partd==1.4.2 +patsy==1.0.2 +pillow==11.3.0 +plotly==6.4.0 +proto-plus==1.26.1 +protobuf==5.29.5 +pyarrow==22.0.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pydantic==2.11.10 +pydantic_core==2.33.2 +pydub==0.25.1 +pyerfa==2.0.1.5 +Pygments==2.19.2 +pyparsing==3.2.5 +python-dateutil==2.9.0.post0 +python-multipart==0.0.20 +pytz==2025.2 +PyYAML==6.0.3 +requests==2.32.5 +rich==14.2.0 +rsa==4.9.1 +ruff==0.14.4 +safehttpx==0.1.7 +scipy +semantic-version==2.10.0 +sgp4==2.25 +shellingham==1.5.4 +simpy==4.1.1 +six==1.17.0 +skyfield==1.53 +sniffio==1.3.1 +starlette==0.49.3 +statsmodels==0.14.5 +tomlkit==0.13.3 +toolz==1.1.0 +tqdm==4.67.1 +typer==0.20.0 +typer-slim==0.20.0 +typing-inspection==0.4.2 +typing_extensions==4.15.0 +tzdata==2025.2 +uritemplate==4.2.0 +urllib3==2.5.0 +uvicorn==0.38.0 +websockets==15.0.1 diff --git a/scripts/patch_docx.py b/scripts/patch_docx.py new file mode 100644 index 0000000..0e28a2d --- /dev/null +++ b/scripts/patch_docx.py @@ -0,0 +1,159 @@ +""" +Aplica correções de texto ao Relatorio_ED2_241327.docx. +Corrige seções desatualizadas após a implementação do SB3 DQN e runs reais de SLM/LLM. +Uso: python scripts/patch_docx.py +""" +import json +import sys +from pathlib import Path +from docx import Document + +ROOT = Path(__file__).parent.parent + + +def load_summary(engine): + with open(ROOT / "logs" / f"mec_summary_{engine}.json") as f: + return json.load(f) + + +def replace_para(doc, old_text, new_text, label=""): + for p in doc.paragraphs: + if old_text in p.text: + full = p.text.replace(old_text, new_text) + # Preserve runs as much as possible — clear and rewrite first run + for run in p.runs: + run.text = "" + if p.runs: + p.runs[0].text = full + else: + p.add_run(full) + print(f" ✓ {label or old_text[:60]}") + return True + print(f" ✗ NOT FOUND: {label or old_text[:60]}") + return False + + +def main(): + doc_path = ROOT / "Relatorio_ED2_241327.docx" + doc = Document(str(doc_path)) + + b = load_summary("BASELINE") + d = load_summary("DRL") + s = load_summary("SLM") + l = load_summary("LLM") + + print("=== Aplicando correções de texto ===\n") + + # ------------------------------------------------------------------ + # Seção 2 — intro: "três fatores" (DRL NÃO pivotou, SB3 funcionou) + # ------------------------------------------------------------------ + replace_para(doc, + "O plano de trabalho original previa o uso de bibliotecas de DRL de propósito geral (Stable Baselines3, PPO/DQN) via Gym Wrapper. Durante a implementação, três fatores motivaram pivôs metodológicos relevantes:", + "O plano de trabalho original previa o uso de bibliotecas de DRL de propósito geral (Stable Baselines3, DQN) via Gym Wrapper. A integração foi implementada com sucesso. Três adaptações metodológicas adicionais — SLM, bateria orbital e premissa ISL — foram necessárias nos demais componentes:", + "Seção 2 intro (três/quatro fatores)") + + # ------------------------------------------------------------------ + # Seção 2.1 — título e dois parágrafos (reescrever para SB3 success) + # ------------------------------------------------------------------ + replace_para(doc, + "2.1. DRL: Q-Learning Tabular em Vez de PPO/DQN", + "2.1. DRL: SB3 DQN com Ambiente Gymnasium Customizado", + "2.1 título") + + replace_para(doc, + "A integração do Stable Baselines3 ao loop do SimPy do CosmicBeats revelou incompatibilidade estrutural: bibliotecas de DRL profundo assumem um ambiente stateless e vetorizado (Gym API), enquanto o simulador é um sistema de eventos discretos com estado compartilhado entre satélites. Adaptar o wrapper implicaria reescrever o motor de simulação.", + "A integração do Stable Baselines3 foi implementada com sucesso mediante separação entre treinamento e inferência. Um ambiente Gymnasium customizado (NTNMECEnv) gera episódios sintéticos cobrindo a distribuição real de estados da simulação, resolvendo a incompatibilidade estrutural entre o Gym API (stateless, vetorizado) e o loop SimPy (stateful, eventos discretos): o agente DQN treina offline e o modelo carregado executa inferência determinística durante a simulação.", + "2.1 parágrafo 1 (incompatibilidade)") + + replace_para(doc, + "Optou-se por implementar SB3 DQN (pré-treinado, 50k steps), que aprende durante a própria simulação sem pré-treinamento externo. Esta escolha tem justificativa científica adicional: o espaço de estados é compacto (36 estados: 3 regiões × 3 bins de bateria × 2 RAM-ok × 2 anomalia), tornando Q-tabular não só viável como adequado para a janela de 20 minutos simulados (240 steps).", + "O agente DQN (MlpPolicy, γ=0, 50.000 timesteps) foi treinado com observações sintéticas de 13 features: região da tarefa (one-hot), has_anomaly e estado de bateria/RAM/solar de cada satélite. O invariante científico é preservado: o DQN sabe que há anomalia mas não conhece seu tipo (GDPR, soberania, hardware), justificando a menor anomaly_compliance_rate vs. SLM/LLM.", + "2.1 parágrafo 2 (Q-tabular justificativa)") + + # ------------------------------------------------------------------ + # Seção 3.4.2 — título e descrição do DRL + # ------------------------------------------------------------------ + replace_para(doc, + "3.4.2. DRL (Q-Learning Tabular)", + "3.4.2. DRL (SB3 DQN)", + "3.4.2 título") + + replace_para(doc, + "Agente de Reinforcement Learning online que aprende durante a simulação. Estado: (region_idx, bat_bin, ram_ok, has_anomaly) — 36 estados. Ações: PREFER_BATTERY | PREFER_RAM | PREFER_BALANCED | DROP. Exploração ε-greedy com decaimento linear (0.30 → 0.05 em 200 decisões). Recompensa: +1.0 por roteamento bem-sucedido (bônus +0.1 × bat/100), -0.5 por drop forçado, -1.0 por drop voluntário com candidatos disponíveis.", + "Agente SB3 DQN pré-treinado em 50.000 episódios sintéticos via NTNMECEnv (Gymnasium). Observação: vetor de 13 features — região da tarefa (one-hot 3D), has_anomaly, bateria/RAM/solar dos 3 satélites. Ações: Discrete(4) → SAT-1 | SAT-2 | SAT-15 | DROP. Recompensa: +1.0 por roteamento válido (bônus proporcional à bateria), -1.0 por drop voluntário indevido. Inferência determinística na simulação: ~0.6 ms (MLP forward pass).", + "3.4.2 descrição DRL") + + # ------------------------------------------------------------------ + # Seção 3.4.4 — LLM: Gemini 2.5 Flash → Gemini 3.1 Flash Lite + # ------------------------------------------------------------------ + replace_para(doc, + "Modelo Gemini 2.5 Flash via API, representando um orquestrador centralizado na estação terrestre. Mesmo prompt e regras semânticas do SLM. Latência inclui round-trip LEO↔GS (3.67 ms de propagação + tempo de API). Consome 5 J/decisão (transmissão + inferência em datacenter), 5000× mais que o BASELINE.", + "Modelo Gemini 3.1 Flash Lite via API, representando um orquestrador centralizado na estação terrestre. Mesmo prompt e regras semânticas do SLM. Rate limiter: janela deslizante de 10 RPM. Latência real medida: ~6 s/decisão (round-trip + rate limiting). Consome 5 J/decisão, 5000× mais que o BASELINE.", + "3.4.4 LLM Gemini model") + + # ------------------------------------------------------------------ + # Seção 4 — Desafio 2: DRL stub (atualiza menção a Q-learning) + # ------------------------------------------------------------------ + replace_para(doc, + "DRL stub sem aprendizado: A implementação inicial usava pesos fixos (0.6×bat + 0.4×RAM) sem Q-table, sem exploração e sem atualização de recompensa. Substituído por SB3 DQN (Stable Baselines3, 50k steps) com ε-greedy, TD(0) update e recompensa por qualidade energética.", + "DRL stub sem aprendizado: A implementação inicial usava pesos fixos (0.6×bat + 0.4×RAM) sem exploração. Substituído por SB3 DQN (Stable Baselines3) com ambiente Gymnasium customizado, treinado em 50.000 episódios sintéticos e inferência determinística durante a simulação.", + "Seção 4 item 2 DRL stub") + + # ------------------------------------------------------------------ + # Seção 5 intro — remover "run pendente" + # ------------------------------------------------------------------ + replace_para(doc, + "Os experimentos foram executados com seed determinístico (random.seed(0)), garantindo que todos os engines recebam a mesma sequência de tarefas. A janela de simulação é de 20 minutos (1200 s), com steps de 5 s (240 ticks). Nota: o engine DRL altera o estado do gerador aleatório durante as decisões ε-greedy, resultando em sequência ligeiramente diferente de chegadas de Poisson. Os engines SLM e LLM estão com resultados parciais — SLM e LLM precisam ser re-executados com a nova arquitetura (nova run pendente por limite de taxa da API).", + "Os experimentos foram executados com seed determinístico (random.seed(0)), garantindo que todos os engines recebam a mesma sequência de tarefas. A janela de simulação é de 20 minutos (1200 s), com steps de 5 s (240 ticks). Nota: o engine DRL usa random.random() internamente para inferência MLP, resultando em 65 tarefas vs. 69 nos demais engines — efeito esperado do design.", + "Seção 5 intro (run pendente)") + + # ------------------------------------------------------------------ + # Seção 5.1 — footnotes (remover *, **) + # ------------------------------------------------------------------ + replace_para(doc, + "† DRL usa random.random() para ε-greedy, deslocando a seed do processo de Poisson. * SLM: dados do run anterior (arquitetura antiga com link_quality sinusoidal — drop alto era pela sinusoide, não por bateria). Re-run com nova arquitetura pendente. ** LLM: run acidental sem --engine flag atingiu rate limit da API; todas as tarefas foram dropadas por HTTP 429. Re-run pendente.", + "† DRL: engine usa random.random() internamente, deslocando a seed do gerador de Poisson — resultando em 65 tarefas vs. 69 nos demais engines. SLM: taxa de sucesso limitada por instabilidade da API Gemma 4 em fase de protótipo (HTTP 500 → drops).", + "Seção 5.1 footnotes") + + # ------------------------------------------------------------------ + # Seção 5.3 — DRL: atualiza números 83.6%/16.7% + # ------------------------------------------------------------------ + replace_para(doc, + "BASELINE obteve 91.3% de compliance geral, mas apenas 25% nas tarefas anômalas — acerto por sorte geográfica, não por raciocínio. DRL, com sua 'caixa-preta' intencional para tipos de anomalia, obteve 83.6% geral e 16.7% em anomalias — pior que BASELINE porque o ε-greedy pode escolher ações sub-ótimas durante a fase de exploração (ainda em ε=0.22 no passo 60, longe do ε_min=0.05).", + f"BASELINE obteve 91.3% de compliance geral, mas apenas 25% nas tarefas anômalas — acerto por sorte geográfica, não por raciocínio. DRL (SB3 DQN), com sua caixa-preta intencional para tipos de anomalia, obteve {d['effective_success_rate']:.1%} de effective_success_rate e {d['anomaly_compliance_rate']:.1%} de compliance em anomalias. A melhora em relação ao BASELINE (25%) decorre do DQN aprender um comportamento marginalmente mais conservador em tarefas anômalas, sem contudo conhecer as regras semânticas.", + "Seção 5.3 DRL números") + + # ------------------------------------------------------------------ + # Seção 5.3 — SLM/LLM: substituir texto pendente por resultados reais + # ------------------------------------------------------------------ + replace_para(doc, + "SLM (Gemma 4 26B) obteve 75.0% de compliance em anomalias — superior a BASELINE e DRL — confirmando a hipótese de que raciocínio semântico via prompt supera heurísticas para regras geopolíticas. Com a nova arquitetura (sem link_quality sinusoidal que causava drops espúrios), espera-se que SLM demonstre compliance ainda maior com taxa de sucesso próxima de 100%.", + f"SLM (Gemma 4 26B) obteve {s['anomaly_compliance_rate']:.1%} de compliance em anomalias e {s['effective_success_rate']:.1%} de effective_success_rate. A taxa de sucesso menor que BASELINE/DRL reflete instabilidade da API Gemma 4 em fase de protótipo (erros HTTP 500 → drops forçados), não uma limitação cognitiva: nos casos em que o modelo respondeu, o raciocínio semântico foi consistente. O LLM (Gemini 3.1 Flash Lite) alcançou {l['anomaly_compliance_rate']:.1%} de compliance em anomalias e {l['effective_success_rate']:.1%} de effective_success_rate — confirmando a hipótese central: modelos de linguagem superam heurísticas em 3–4× para raciocínio semântico geopolítico.", + "Seção 5.3 SLM/LLM resultados reais") + + # ------------------------------------------------------------------ + # Seção 6.1 — DRL bullet: atualiza para SB3 DQN + # ------------------------------------------------------------------ + replace_para(doc, + "DRL com SB3 DQN real (substituindo o stub da primeira etapa), com invariante científico preservado: DRL é caixa-preta para semântica de anomalias.", + "DRL com SB3 DQN pré-treinado em 50.000 episódios (NTNMECEnv Gymnasium): o agente aprende roteamento por recursos mas não conhece o tipo de anomalia — preservando o invariante científico que justifica a comparação com SLM/LLM.", + "Seção 6.1 DRL bullet") + + # ------------------------------------------------------------------ + # Seção 6.2 — DRL trade-off: remover referência a ε=0.22 (não aplica) + # ------------------------------------------------------------------ + replace_para(doc, + "▪ Aprendizado online vs. regras explícitas: DRL pode potencialmente superar BASELINE após convergência (muitas horas/runs), mas em 20 minutos simulados ainda está em exploração ativa (ε=0.22). SLM/LLM aplicam regras zero-shot sem treinamento prévio no domínio específico.", + f"▪ Aprendizado offline vs. regras explícitas: DRL (SB3 DQN) pré-treinado atingiu {d['effective_success_rate']:.1%} de effective_success_rate — comparável ao BASELINE ({b['effective_success_rate']:.1%}) — mas sem capacidade semântica ({d['anomaly_compliance_rate']:.1%} vs. {l['anomaly_compliance_rate']:.1%} do LLM). SLM/LLM aplicam regras zero-shot sem treinamento no domínio específico.", + "Seção 6.2 DRL trade-off") + + # ------------------------------------------------------------------ + # Salva + # ------------------------------------------------------------------ + out_path = ROOT / "Relatorio_ED2_241327.docx" + doc.save(str(out_path)) + print(f"\n✓ Relatório salvo: {out_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/rewrite_report_v2.py b/scripts/rewrite_report_v2.py new file mode 100644 index 0000000..ddc75de --- /dev/null +++ b/scripts/rewrite_report_v2.py @@ -0,0 +1,284 @@ +""" +Reescrita completa do Relatorio_ED2_241327.docx: cenário/motivação mais claros, +métricas de compliance como KPI principal, e todos os números finais desta sessão +(DRL com reward shaping; SLM com o bug de parsing de 'thought parts' corrigido). + +Uso: python scripts/rewrite_report_v2.py +""" +import json +from pathlib import Path +from docx import Document + +ROOT = Path(__file__).parent.parent + + +def load_summary(engine): + with open(ROOT / "logs" / f"mec_summary_{engine}.json") as f: + return json.load(f) + + +def set_text(doc, index, new_text, label=""): + p = doc.paragraphs[index] + for run in p.runs: + run.text = "" + if p.runs: + p.runs[0].text = new_text + else: + p.add_run(new_text) + print(f" P{index:<3} ✓ {label}") + + +def main(): + doc_path = ROOT / "Relatorio_ED2_241327.docx" + doc = Document(str(doc_path)) + + b = load_summary("BASELINE") + d = load_summary("DRL") + s = load_summary("SLM") + l = load_summary("LLM") + + print("=== Reescrevendo Seção 1 (Introdução/Cenário) ===") + set_text(doc, 5, + "Cenário: uma constelação de três satélites em órbita baixa (LEO) atua " + "como nós de computação de borda (MEC), cada um responsável por uma " + "região geográfica (Brasil, Europa, EUA). Tarefas de processamento " + "chegam continuamente e cada satélite decide, sozinho e em tempo real, " + "se aceita, redireciona para outro satélite ou descarta a tarefa — sob " + "restrições reais de bateria (ciclo solar/eclipse orbital) e RAM.", + "cenário") + set_text(doc, 6, + "Para enriquecer o cenário, 10% das tarefas trazem uma restrição " + "semântica do mundo real: uma lei de privacidade europeia (GDPR), uma " + "exigência de soberania de dados do Brasil, ou uma falha crítica de " + "hardware. Cumprir essas regras exige entender o significado da " + "restrição, não apenas otimizar recursos — esse é o núcleo do " + "experimento.", + "motivação do cenário") + set_text(doc, 8, + "Hipótese central: orquestradores que leem e interpretam regras em " + "linguagem natural (SLM embarcado e LLM na nuvem) tomam decisões mais " + "corretas que uma heurística fixa (BASELINE) ou um agente de " + "aprendizado por reforço puramente numérico (DRL), pagando em troca " + "mais latência e energia. O valor científico do experimento é " + "quantificar esse trade-off entre correção semântica e custo " + "computacional — uma decisão de engenharia real para sistemas " + "NTN-MEC, que operam sob orçamento de energia e banda extremamente " + "restrito.", + "valor científico") + + print("\n=== Reescrevendo Seção 2 (Adaptações) ===") + set_text(doc, 10, + "O plano original previa DRL via Stable Baselines3 (SB3) com Gym " + "Wrapper — implementado com sucesso. Três componentes evoluíram além " + "do plano original durante a implementação: o SLM, o modelo de " + "bateria, e a premissa de conectividade entre satélites (ISL).", + "2 intro") + set_text(doc, 12, + "O SB3 não pode ser conectado diretamente ao loop de eventos " + "discretos do SimPy (que mantém estado compartilhado entre " + "satélites), então o treinamento foi separado da execução: um " + "ambiente Gymnasium sintético (NTNMECEnv) gera 50.000 episódios " + "cobrindo a distribuição real de estados da simulação; o modelo " + "treinado roda apenas inferência determinística (~0.5 ms) dentro da " + "simulação.", + "2.1 p1") + set_text(doc, 13, + "Observação: 13 valores normalizados — região da tarefa, um sinal " + "binário \"há anomalia\" e o estado de bateria/RAM/luz-solar dos 3 " + "satélites. O agente nunca vê o TIPO da anomalia, só que ela existe — " + "isso preserva a comparação justa com SLM/LLM, que leem a regra em " + "texto. Durante o treino, a recompensa usa o subtipo real da anomalia " + "(oculto, nunca exposto ao agente) para ensinar a melhor estratégia " + "possível dado essa cegueira parcial.", + "2.1 p2 (reward shaping)") + set_text(doc, 15, + "Sem hardware NPU disponível para teste, o SLM é simulado via API do " + "Gemma 4 26B (modelo pequeno, classe de parâmetros equivalente a um " + "SLM embarcado). A latência usada nos KPIs é fixada em 50 ms — " + "benchmark de NPU satelital da literatura — independente da latência " + "real de rede da API, que é maior e serve apenas para diagnóstico.", + "2.2 SLM") + set_text(doc, 17, + "A bateria segue um modelo físico de eclipse orbital: a iluminação " + "solar varia como uma senoide de período 90 minutos (órbita LEO); " + "abaixo de um limiar a bateria entra em eclipse e drena por consumo " + "de housekeeping, fora do eclipse ela recarrega.", + "2.3 p1") + set_text(doc, 19, + "Os três satélites têm capacidades (75, 50 e 100 Wh) e fases " + "orbitais diferentes, simulando hardware heterogêneo real. O eclipse " + "ocupa ~53% de cada órbita (46 dos 90 min), criando pressão " + "energética real; abaixo de 20% de carga, o satélite recusa novas " + "tarefas.", + "2.3 p2") + set_text(doc, 21, + "O link entre satélites (ISL) é assumido sempre disponível, premissa " + "comum em constelações modernas com ISL óptico — elimina uma " + "variável de qualidade de sinal fora do escopo deste estudo. Drops " + "refletem exclusivamente esgotamento de bateria/RAM ou recusa " + "semântica, nunca falha de transmissão.", + "2.4 ISL") + + print("\n=== Reescrevendo Seção 3 (Arquitetura) ===") + set_text(doc, 33, + "Como detalhado na Seção 2.1, o DRL não conhece o tipo da anomalia — " + "apenas que ela existe — o que limita sua compliance semântica em " + "comparação a SLM/LLM, que leem a regra em texto.", + "3.3 invariante DRL") + set_text(doc, 38, + "Agente SB3 DQN pré-treinado em 50.000 episódios sintéticos via " + "NTNMECEnv (Gymnasium), com recompensa moldada por subtipo de " + "anomalia oculto durante o treino (nunca exposto na observação). " + "Observação: vetor de 13 features — região da tarefa (one-hot 3D), " + "has_anomaly, bateria/RAM/solar dos 3 satélites. Ações: Discrete(4) " + "→ SAT-1 | SAT-2 | SAT-15 | DROP. Inferência determinística na " + "simulação: ~0.5 ms (MLP forward pass).", + "3.4.2 DRL") + + print("\n=== Reescrevendo Seção 4 (Desafios) ===") + set_text(doc, 44, + "RAM sem recuperação: tarefas consumiam RAM permanentemente, " + "esgotando os 4096 MB após ~8 tarefas. Corrigido liberando RAM 60s " + "após a conclusão de cada tarefa (TASK_DURATION_S).", + "desafio 1") + set_text(doc, 45, + "DRL sem aprendizado real: a versão inicial usava pesos fixos " + "(0.6×bateria + 0.4×RAM), sem qualquer treinamento. Substituído pelo " + "agente SB3 DQN descrito na Seção 2.1.", + "desafio 2") + set_text(doc, 46, + "Latência do SLM media o tempo de rede da API (segundos), não o " + "hardware-alvo. Corrigido para usar a latência de benchmark de NPU " + "(50 ms) nos KPIs, mantendo o tempo de rede real apenas como " + "diagnóstico.", + "desafio 3") + set_text(doc, 47, + "O prompt do LLM não incluía as regras semânticas nem um limitador " + "de taxa, causando estouro do rate limit da API. Unificado com o " + "mesmo prompt e regras do SLM, com limitador de 10 RPM.", + "desafio 4") + set_text(doc, 48, + "O modelo de bateria estático foi substituído pelo modelo físico de " + "eclipse orbital (Seção 2.3), após constatar que ele criava drops " + "artificiais não relacionados a recursos reais.", + "desafio 5") + set_text(doc, 49, + "API do SLM retorna o \"pensamento\" (thought) e a resposta final em " + "partes separadas; o código inicial lia apenas a primeira parte " + "(quase sempre o raciocínio, não a decisão), gerando ~40% de falhas " + "de parsing que pareciam instabilidade de rede. Corrigido para usar " + "apenas as partes finais (thought=false), eliminando praticamente " + "todas as falhas espúrias (de 14 para 2 drops por causas de " + "infraestrutura, de 69 tarefas).", + "desafio 6 (bug real do thought-parts)") + + print("\n=== Reescrevendo Seção 5 (Resultados) ===") + set_text(doc, 52, + "Os experimentos foram executados com seed determinístico " + "(random.seed(0)), garantindo que todos os engines recebam a mesma " + "sequência das 69 tarefas. A janela de simulação é de 20 minutos " + "(1200 s), com steps de 5 s.", + "5 intro") + set_text(doc, 55, + "Nota: o SLM apresentou 2 quedas residuais por instabilidade de rede " + "da API (de 69 chamadas), mesmo após a correção do bug de parsing " + "(Seção 4) — não afetam a compliance semântica, que trata tarefas " + "sem anomalia como corretas independentemente de descarte.", + "5.1 footnote") + set_text(doc, 62, + "As duas métricas centrais deste estudo são semantic_compliance_rate " + "(fração de decisões corretas, incluindo descartes corretos) e " + "anomaly_compliance_rate (a mesma métrica, isolada às 8 tarefas com " + "restrição semântica explícita). Essas, não a taxa de sucesso bruta, " + "medem \"inteligência\" neste experimento: uma tarefa nunca está " + "\"perdida\" — o satélite sempre tenta executar ou redirecionar; o " + "que importa é se a decisão (incluindo descartar) foi a correta.", + "5.3 intro") + set_text(doc, 63, + f"BASELINE acerta {b['semantic_compliance_rate']:.1%} das decisões " + f"no geral, mas só {b['anomaly_compliance_rate']:.1%} (2/8) nas " + "tarefas anômalas — sorte geográfica, não raciocínio: ele nunca " + f"soube que havia uma regra a seguir. DRL (SB3 DQN) sobe para " + f"{d['semantic_compliance_rate']:.1%} geral e " + f"{d['anomaly_compliance_rate']:.1%} (3/8) em anomalias — superando " + "o BASELINE nas duas métricas após um ajuste de recompensa que o " + "ensina a reagir melhor ao sinal binário \"há anomalia\" (Seção " + f"2.1). Esse {d['anomaly_compliance_rate']:.1%} é o teto teórico: " + "das 8 anomalias reais, só as 3 falhas de hardware podem ser " + "identificadas corretamente com um único bit de informação — as " + "outras exigiriam saber qual regra específica se aplica, informação " + "que o DRL estruturalmente não recebe.", + "5.3 BASELINE/DRL") + set_text(doc, 64, + f"SLM (Gemma 4 26B) atinge {s['semantic_compliance_rate']:.1%} de " + f"compliance geral e {s['anomaly_compliance_rate']:.1%} (6/8) em " + f"anomalias, com effective_success_rate de " + f"{s['effective_success_rate']:.1%} — a diferença para 100% reflete " + "2 quedas residuais por instabilidade de rede da API (Seção 4), não " + "erro de raciocínio: quando o modelo responde, a decisão está " + f"correta. LLM (Gemini 3.1 Flash Lite) atinge " + f"{l['semantic_compliance_rate']:.1%} em ambas as métricas, " + "confirmando a hipótese central: ler a regra em linguagem natural " + "supera tanto a heurística fixa quanto o aprendizado por reforço " + "puramente numérico, na proporção exata da complexidade semântica " + "da tarefa.", + "5.3 SLM/LLM") + + print("\n=== Reescrevendo Seção 6 (Conclusão) ===") + set_text(doc, 71, + "DRL com SB3 DQN pré-treinado em 50.000 episódios (NTNMECEnv " + "Gymnasium) e recompensa moldada por subtipo de anomalia oculto " + "durante o treino: o agente supera o BASELINE em compliance geral " + f"({d['semantic_compliance_rate']:.1%} vs " + f"{b['semantic_compliance_rate']:.1%}) e em anomalias " + f"({d['anomaly_compliance_rate']:.1%} vs " + f"{b['anomaly_compliance_rate']:.1%}), sem nunca ver o tipo da " + "anomalia — preservando o invariante científico que justifica a " + "comparação com SLM/LLM.", + "6.1 DRL") + set_text(doc, 81, + "▪ Aprendizado direcionado por recompensa vs. regras explícitas: o " + "reward shaping elevou o DRL " + f"({d['semantic_compliance_rate']:.1%} compliance, " + f"{d['anomaly_compliance_rate']:.1%} em anomalias) acima do " + "BASELINE nas duas métricas — mas o teto é estrutural: sem ver o " + "tipo da anomalia, o DRL não pode chegar a 100% como o LLM. SLM/LLM " + "aplicam regras zero-shot lidas em texto, sem essa limitação de " + "informação.", + "6.2 trade-off DRL") + + print("\n=== Reescrevendo Tabelas ===") + t0 = doc.tables[0] + t0.rows[2].cells[2].text = f"~{d['avg_latency_ms']:.1f} ms (inferência MLP)" + print(" Tabela 0 ✓ latência DRL") + + t2 = doc.tables[2] + t2.rows[3].cells[2].text = ("Todos os engines, incluindo DRL — única " + "regra que o DRL aprendeu corretamente via reward shaping " + "(responsável pelos 37.5% de anomaly_compliance)") + print(" Tabela 2 ✓ quem acerta (hardware)") + + t3 = doc.tables[3] + rows_data = [ + ("KPI", "BASELINE", "DRL (SB3)", "SLM (Gemma 4)", "LLM (Gemini 3.1)"), + ("Total de tarefas", f"{b['total_tasks']}", f"{d['total_tasks']}", f"{s['total_tasks']}", f"{l['total_tasks']}"), + ("Compliance semântica", f"{b['semantic_compliance_rate']:.1%}", f"{d['semantic_compliance_rate']:.1%}", f"{s['semantic_compliance_rate']:.1%}", f"{l['semantic_compliance_rate']:.1%}"), + ("Compliance em anomalias", f"{b['anomaly_compliance_rate']:.1%}", f"{d['anomaly_compliance_rate']:.1%}", f"{s['anomaly_compliance_rate']:.1%}", f"{l['anomaly_compliance_rate']:.1%}"), + ("Taxa de sucesso (throughput)", f"{b['success_rate']:.1%}", f"{d['success_rate']:.1%}", f"{s['success_rate']:.1%}", f"{l['success_rate']:.1%}"), + ("Drop rate", f"{b['drop_rate']:.1%}", f"{d['drop_rate']:.1%}", f"{s['drop_rate']:.1%}", f"{l['drop_rate']:.1%}"), + ("Latência média", "~0 ms", f"{d['avg_latency_ms']:.2f} ms", "50 ms (NPU)", f"{l['avg_latency_ms']:.1f} ms"), + ("Energia total", f"{b['total_joules']:.3f} J", f"{d['total_joules']:.3f} J", f"{s['total_joules']:.1f} J", f"{l['total_joules']:.1f} J"), + ("Energia/decisão", f"{b['joules_per_decision']:.3f} J", f"{d['joules_per_decision']:.3f} J", f"{s['joules_per_decision']:.3f} J", f"{l['joules_per_decision']:.3f} J"), + ("RAM utilização final", f"{b['avg_ram_utilization_pct']:.1f}%", f"{d['avg_ram_utilization_pct']:.1f}%", f"{s['avg_ram_utilization_pct']:.1f}%", f"{l['avg_ram_utilization_pct']:.1f}%"), + ] + for ri, row_vals in enumerate(rows_data): + for ci, val in enumerate(row_vals): + t3.rows[ri].cells[ci].text = val + print(" Tabela 3 ✓ reescrita completa (KPIs reordenados, compliance primeiro)") + + doc.save(str(doc_path)) + print(f"\n✓ Relatório salvo: {doc_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/train_drl.py b/scripts/train_drl.py new file mode 100644 index 0000000..ad940ab --- /dev/null +++ b/scripts/train_drl.py @@ -0,0 +1,89 @@ +""" +Train DQN agent for NTN-MEC satellite routing. + +Usage: + python scripts/train_drl.py [--timesteps N] + +Saves model to models/drl_agent.zip +""" + +import sys +import os +import argparse + +# Ensure project root is on path +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from stable_baselines3 import DQN +from stable_baselines3.common.monitor import Monitor +from stable_baselines3.common.callbacks import EvalCallback +from src.schedulers.drl_gym_env import NTNMECEnv + + +def train(timesteps: int = 50_000): + print(f"=== DQN Training: NTN-MEC Satellite Routing ===") + print(f"Timesteps : {timesteps:,}") + print(f"Obs space : {NTNMECEnv().observation_space}") + print(f"Act space : {NTNMECEnv().action_space}") + print() + + os.makedirs("models", exist_ok=True) + + train_env = Monitor(NTNMECEnv()) + eval_env = Monitor(NTNMECEnv()) + + eval_callback = EvalCallback( + eval_env, + best_model_save_path="models/", + log_path="models/", + eval_freq=5_000, + n_eval_episodes=500, + deterministic=True, + verbose=1, + ) + + model = DQN( + "MlpPolicy", + train_env, + learning_rate=1e-3, + buffer_size=20_000, + learning_starts=1_000, + batch_size=128, + gamma=0.0, # single-step env — no discounting + tau=1.0, # hard target update + target_update_interval=500, + train_freq=4, + exploration_fraction=0.30, # 30% of training in exploration + exploration_final_eps=0.05, + policy_kwargs={"net_arch": [64, 64]}, + verbose=1, + seed=42, + ) + + model.learn(total_timesteps=timesteps, callback=eval_callback, progress_bar=True) + + save_path = "models/drl_agent" + model.save(save_path) + print(f"\nModelo salvo em: {save_path}.zip") + + # Quick evaluation + print("\n=== Avaliação final (1000 episódios, determinístico) ===") + obs, _ = eval_env.reset() + total_reward = 0.0 + n_eval = 1000 + for _ in range(n_eval): + action, _ = model.predict(obs, deterministic=True) + obs, reward, terminated, truncated, _ = eval_env.step(action) + total_reward += reward + if terminated or truncated: + obs, _ = eval_env.reset() + + print(f"Recompensa média: {total_reward / n_eval:.4f}") + print("Esperado > 0.80 para agente treinado (roteamento bem-sucedido)") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--timesteps", type=int, default=50_000) + args = parser.parse_args() + train(args.timesteps) diff --git a/scripts/update_report.py b/scripts/update_report.py new file mode 100644 index 0000000..a933c80 --- /dev/null +++ b/scripts/update_report.py @@ -0,0 +1,220 @@ +""" +Atualiza o Relatorio_ED2_241327.docx com os resultados finais dos 4 engines. +Uso: python scripts/update_report.py +""" +import json +import os +import sys +from pathlib import Path +from docx import Document +from docx.shared import Pt, RGBColor +from docx.enum.text import WD_ALIGN_PARAGRAPH + + +ROOT = Path(__file__).parent.parent + + +def load_summary(engine: str) -> dict: + path = ROOT / "logs" / f"mec_summary_{engine}.json" + with open(path) as f: + return json.load(f) + + +def pct(val): + if val is None: + return "—" + return f"{val*100:.1f}%" + + +def fmt(val, decimals=1): + if val is None: + return "—" + return f"{val:.{decimals}f}" + + +def set_cell_text(cell, text, bold=False, color=None): + cell.text = text + for run in cell.paragraphs[0].runs: + run.bold = bold + if color: + run.font.color.rgb = RGBColor(*color) + + +def update_table_cell(table, row_idx, col_idx, text, bold=False, color=None): + cell = table.cell(row_idx, col_idx) + p = cell.paragraphs[0] + p.clear() + run = p.add_run(text) + run.bold = bold + if color: + run.font.color.rgb = RGBColor(*color) + + +def find_and_replace_paragraph(doc, old_text, new_text): + """Replace first occurrence of old_text in any paragraph.""" + for para in doc.paragraphs: + if old_text in para.text: + for run in para.runs: + if old_text in run.text: + run.text = run.text.replace(old_text, new_text) + return True + return False + + +def main(): + doc_path = ROOT / "Relatorio_ED2_241327.docx" + if not doc_path.exists(): + print(f"ERROR: {doc_path} not found") + sys.exit(1) + + print("Carregando resultados...") + b = load_summary("BASELINE") + d = load_summary("DRL") + s = load_summary("SLM") + l = load_summary("LLM") + + print(f"BASELINE: effective={b['effective_success_rate']:.1%} anom={b.get('anomaly_compliance_rate','?')}") + print(f"DRL: effective={d['effective_success_rate']:.1%} anom={d.get('anomaly_compliance_rate','?')}") + print(f"SLM: effective={s['effective_success_rate']:.1%} anom={s.get('anomaly_compliance_rate','?')}") + print(f"LLM: effective={l['effective_success_rate']:.1%} anom={l.get('anomaly_compliance_rate','?')}") + + doc = Document(str(doc_path)) + + # ------------------------------------------------------------------ # + # Table 0 — Engine overview (latência / energia) # + # Row 2: DRL description update # + # ------------------------------------------------------------------ # + t0 = doc.tables[0] + # DRL row: "Q-learning tabular online" → "SB3 DQN (pré-treinado, 50k steps)" + t0.cell(2, 1).text = "SB3 DQN (pré-treinado, 50k steps)" + t0.cell(2, 2).text = "~0.6 ms (inferência MLP)" + # SLM row: update model name + t0.cell(3, 1).text = "Edge AI embarcada (Gemma 4 26B)" + # LLM row: update model name + t0.cell(4, 1).text = "Cloud AI centralizada (Gemini 3.1 Flash Lite)" + t0.cell(4, 2).text = "~1-6 s (round-trip, inclui rate-limit)" + + # ------------------------------------------------------------------ # + # Table 3 — KPI comparison (main results table) # + # ------------------------------------------------------------------ # + t3 = doc.tables[3] + + # Header row + t3.cell(0, 2).text = "DRL (SB3)" + t3.cell(0, 3).text = "SLM (Gemma 4)" + t3.cell(0, 4).text = "LLM (Gemini 3.1)" + + # Row 1: Total tasks + t3.cell(1, 2).text = str(d["total_tasks"]) + t3.cell(1, 3).text = str(s["total_tasks"]) + t3.cell(1, 4).text = str(l["total_tasks"]) + + # Row 2: Success rate (throughput) + t3.cell(2, 1).text = pct(b["success_rate"]) + t3.cell(2, 2).text = pct(d["success_rate"]) + t3.cell(2, 3).text = pct(s["success_rate"]) + t3.cell(2, 4).text = pct(l["success_rate"]) + + # Row 3: Drop rate + t3.cell(3, 1).text = pct(b["drop_rate"]) + t3.cell(3, 2).text = pct(d["drop_rate"]) + t3.cell(3, 3).text = pct(s["drop_rate"]) + t3.cell(3, 4).text = pct(l["drop_rate"]) + + # Row 4: Latency + t3.cell(4, 2).text = f"{fmt(d['avg_latency_ms'], 2)} ms" + t3.cell(4, 3).text = "50 ms (NPU)" + t3.cell(4, 4).text = f"{fmt(l['avg_latency_ms'])} ms" + + # Row 5: Total energy + t3.cell(5, 1).text = f"{fmt(b['total_joules'], 3)} J" + t3.cell(5, 2).text = f"{fmt(d['total_joules'], 3)} J" + t3.cell(5, 3).text = f"{fmt(s['total_joules'], 1)} J" + t3.cell(5, 4).text = f"{fmt(l['total_joules'], 1)} J" + + # Row 6: Energy/decision — fixed constants, no change needed + + # Row 7: Compliance semântico → Effective success rate + t3.cell(7, 0).text = "Eff. taxa de sucesso" + t3.cell(7, 1).text = pct(b["effective_success_rate"]) + t3.cell(7, 2).text = pct(d["effective_success_rate"]) + t3.cell(7, 3).text = pct(s["effective_success_rate"]) + t3.cell(7, 4).text = pct(l["effective_success_rate"]) + + # Row 8: Anomaly compliance + t3.cell(8, 1).text = pct(b.get("anomaly_compliance_rate")) + t3.cell(8, 2).text = pct(d.get("anomaly_compliance_rate")) + t3.cell(8, 3).text = pct(s.get("anomaly_compliance_rate")) + t3.cell(8, 4).text = pct(l.get("anomaly_compliance_rate")) + + # Row 9: RAM utilization + t3.cell(9, 1).text = f"{b.get('avg_ram_utilization_pct', 0):.1f}%" + t3.cell(9, 2).text = f"{d.get('avg_ram_utilization_pct', 0):.1f}%" + t3.cell(9, 3).text = f"{s.get('avg_ram_utilization_pct', 0):.1f}%" + t3.cell(9, 4).text = f"{l.get('avg_ram_utilization_pct', 0):.1f}%" + + # ------------------------------------------------------------------ # + # Table 4 — Battery states (BASELINE + other engines) # + # ------------------------------------------------------------------ # + t4 = doc.tables[4] + # Add DRL, SLM, LLM final battery to caption or extend table + sat1_drl = d.get("sat_1_final_battery_pct", "?") + sat2_drl = d.get("sat_2_final_battery_pct", "?") + sat15_drl = d.get("sat_15_final_battery_pct", "?") + sat1_slm = s.get("sat_1_final_battery_pct", "?") + sat2_slm = s.get("sat_2_final_battery_pct", "?") + sat15_slm = s.get("sat_15_final_battery_pct", "?") + sat1_llm = l.get("sat_1_final_battery_pct", "?") + sat2_llm = l.get("sat_2_final_battery_pct", "?") + sat15_llm = l.get("sat_15_final_battery_pct", "?") + + # Rebuild header to include all engines + t4.cell(0, 3).text = "SOC Final BASELINE" + # If table has only 4 cols, add info via paragraph replacement below + + # ------------------------------------------------------------------ # + # Text paragraph updates # + # ------------------------------------------------------------------ # + find_and_replace_paragraph(doc, "Gemma 3n via Gemini API", "Gemma 4 26B via Gemini API") + find_and_replace_paragraph(doc, "Gemini 2.0 Flash Lite", "Gemini 3.1 Flash Lite") + find_and_replace_paragraph(doc, "Q-learning tabular online", "SB3 DQN (pré-treinado, 50k steps)") + find_and_replace_paragraph(doc, "Q-learning tabular real", "SB3 DQN (Stable Baselines3, 50k steps)") + find_and_replace_paragraph(doc, "Q-learning tabular", "SB3 DQN") + + # Section 5.3 — update compliance numbers + find_and_replace_paragraph(doc, + "SLM (dados da arquitetura anterior) mostrou 37.5% de compliance em anomalias — s", + f"SLM (Gemma 4 26B) obteve {pct(s.get('anomaly_compliance_rate'))} de compliance em anomalias — s") + + # Remove "trabalhos futuros" item about re-running SLM/LLM + for para in doc.paragraphs: + if "Re-execução completa de SLM e LLM" in para.text: + para.text = ("✓ Concluído: SLM e LLM re-executados com arquitetura final " + "(eclipse orbital, ISL ideal, anomalias semânticas completas).") + break + + # Section 7: Add note about current engine versions + for para in doc.paragraphs: + if "7. Exemplo de Output" in para.text: + # Add new paragraph after section heading + break + + # ------------------------------------------------------------------ # + # Save # + # ------------------------------------------------------------------ # + out_path = ROOT / "Relatorio_ED2_241327.docx" + doc.save(str(out_path)) + print(f"\nRelatório atualizado: {out_path}") + + # Print summary for verification + print("\n=== SUMÁRIO DOS RESULTADOS ===") + engines = [("BASELINE", b), ("DRL (SB3)", d), ("SLM (Gemma 4)", s), ("LLM (Gemini 3.1)", l)] + print(f"{'Engine':<20} {'Success':>8} {'Effective':>10} {'AnomalyCmp':>12} {'Latency':>10}") + print("-" * 65) + for name, r in engines: + print(f"{name:<20} {pct(r['success_rate']):>8} {pct(r['effective_success_rate']):>10} " + f"{pct(r.get('anomaly_compliance_rate')):>12} {fmt(r['avg_latency_ms']):>8}ms") + + +if __name__ == "__main__": + main() diff --git a/src/.DS_Store b/src/.DS_Store new file mode 100644 index 0000000..bc94335 Binary files /dev/null and b/src/.DS_Store differ diff --git a/src/ai_logic.py b/src/ai_logic.py new file mode 100644 index 0000000..c279088 --- /dev/null +++ b/src/ai_logic.py @@ -0,0 +1,388 @@ +# src/ai_logic.py +import csv +import json +import math +import os +import random +import time + +from src.schedulers.llm_scheduler import LLMScheduler +from src.schedulers.baseline_scheduler import BaselineScheduler +from src.schedulers.slm_scheduler import SLMScheduler, NPU_LATENCY_MS as SLM_NPU_LATENCY_MS +from src.schedulers.drl_scheduler import DRLScheduler + +# Custo energético por decisão — parâmetros calibrados por tipo de hardware +JOULES_PER_DECISION = { + "BASELINE": 0.001, # CPU if/else, ~1μs @ 1W + "DRL": 0.005, # Q-table lookup + update, ~2ms @ 2W + "SLM": 0.100, # NPU inference, 50ms @ 2W + "LLM": 5.000, # LEO→GS TX, ~1s @ 5W +} + +# Atraso de propagação LEO↔GS round-trip (550km orbit: 2×550000/3e8 ≈ 3.67ms) +LLM_PROPAGATION_MS = 3.67 + +# Duração de processamento de uma tarefa — após este período a RAM é liberada +TASK_DURATION_S = 60.0 + +# Modelo de eclipse orbital — bateria drena quando sem luz solar (premissa ISL sempre disponível) +ORBITAL_PERIOD_S = 5400.0 # período orbital LEO (90 min) +ECLIPSE_THRESHOLD = -0.10 # sin < -0.1 → eclipse (~53% do ciclo ≈ 46 min/órbita) +SOLAR_CHARGE_RATE_PCT = 0.083 # +0.083%/step em luz solar (+1%/min) +ECLIPSE_DRAIN_PCT = 0.040 # -0.040%/step em eclipse (consumo de housekeeping) +TASK_ENERGY_COST_PCT = 2.0 # -2% SOC por tarefa aceita (consumo de processamento MEC) +BATTERY_SAFETY_PCT = 20.0 # abaixo disto, satélite recusa novas tarefas + + +class MECOrchestrator: + def __init__(self, brain=None, anomaly_rate=0.10, *args, **kwargs): + self._engine = os.environ.get("MEC_ENGINE", "BASELINE") + print(f">>> [MECOrchestrator] Inicializando com o motor: {self._engine}") + + if self._engine == "LLM": + self.brain = LLMScheduler() + elif self._engine == "SLM": + self.brain = SLMScheduler() + elif self._engine == "DRL": + self.brain = DRLScheduler() + elif self._engine == "BASELINE": + self.brain = BaselineScheduler() + else: + print(f">>> [MECOrchestrator] Motor '{self._engine}' desconhecido, usando BASELINE.") + self.brain = BaselineScheduler() + self._engine = "BASELINE" + + self.anomaly_rate = anomaly_rate + self.task_id = 0 + self.mec_satellites = [] + self.mec_tasks_dropped = 0 + self.next_task_time = -1.0 + self.lambda_rate = 4.0 / 60.0 # 4 tarefas/minuto + + self.task_log = [] # uma entrada por tarefa finalizada + self.active_tasks = [] # (completion_time, sat_id, ram_used) — para liberar RAM + self._last_step_time = 0.0 + + # ------------------------------------------------------------------ # + # Setup # + # ------------------------------------------------------------------ # + + def setup_nodes(self, all_nodes): + print(f">>> [MEC] Setup Nodes called with {len(all_nodes)} nodes.") + region_map = {0: "USA", 1: "BRAZIL", 2: "EUROPE"} + + for node in all_nodes: + name = getattr(node, 'iName', str(node)) + if "Satellite" in name or "SAT" in name: + nid = getattr(node, 'nodeID', 0) + region = region_map[nid % 3] + + node.mec_ram_total = 4096.0 + node.mec_ram_free = node.mec_ram_total + node.mec_region = region + node.mec_tasks_completed = 0 + node.mec_tasks_dropped = 0 + + # Bateria: capacidade e SOC inicial variam por hardware (nodeID % 3) + capacity_map = {0: 100.0, 1: 75.0, 2: 50.0} + soc_map = {0: 85.0, 1: 90.0, 2: 75.0} + orbital_phase_map = {0: 3600.0, 1: 0.0, 2: 1800.0} + node_idx = nid % 3 + node.mec_battery_capacity_wh = capacity_map[node_idx] + node.mec_battery_soc_pct = soc_map[node_idx] + node.mec_orbital_phase = orbital_phase_map[node_idx] + node.mec_solar_charging = True + + self.mec_satellites.append(node) + print(f">>> [MEC] Upgraded {name} (ID {nid}) -> {region} | " + f"battery={node.mec_battery_soc_pct:.0f}% " + f"({node.mec_battery_capacity_wh:.0f}Wh)") + + # ------------------------------------------------------------------ # + # Modelo de eclipse orbital / bateria # + # ------------------------------------------------------------------ # + + def _update_battery(self, current_time_sec): + """Atualiza SOC de todos os satélites a cada tick (5s): carrega em sol, drena em eclipse.""" + for s in self.mec_satellites: + phase = getattr(s, 'mec_orbital_phase', 0.0) + solar_val = math.sin(2 * math.pi * (current_time_sec + phase) / ORBITAL_PERIOD_S) + in_eclipse = (solar_val <= ECLIPSE_THRESHOLD) + if in_eclipse: + s.mec_battery_soc_pct = max(0.0, s.mec_battery_soc_pct - ECLIPSE_DRAIN_PCT) + else: + s.mec_battery_soc_pct = min(100.0, s.mec_battery_soc_pct + SOLAR_CHARGE_RATE_PCT) + s.mec_solar_charging = not in_eclipse + + # ------------------------------------------------------------------ # + # Helpers # + # ------------------------------------------------------------------ # + + def _build_fleet(self, current_time_sec): + fleet = [] + for s in self.mec_satellites: + fleet.append({ + "id": getattr(s, 'nodeID', 0), + "region": getattr(s, 'mec_region', 'UNK'), + "battery_pct": round(getattr(s, 'mec_battery_soc_pct', 100.0), 1), + "solar_charging": getattr(s, 'mec_solar_charging', True), + "ram_free": getattr(s, 'mec_ram_free', 0), + }) + return fleet + + def _release_completed_tasks(self, current_time_sec): + """Libera RAM das tarefas que concluíram processamento (após TASK_DURATION_S).""" + still_active = [] + for comp_t, sid, ram in self.active_tasks: + if comp_t <= current_time_sec: + for s in self.mec_satellites: + if getattr(s, 'nodeID') == sid: + s.mec_ram_free = min(s.mec_ram_total, s.mec_ram_free + ram) + break + else: + still_active.append((comp_t, sid, ram)) + self.active_tasks = still_active + + @staticmethod + def _semantic_compliant(task, decision_id, fleet): + """Verifica se a decisão de roteamento respeita a restrição semântica da tarefa.""" + anomaly = task.get('semantic_anomaly') + if not anomaly: + return True + + if anomaly == "restricao_gdpr_europa": + if decision_id is None: + return False + sat = next((s for s in fleet if s['id'] == decision_id), {}) + return sat.get('region') == 'EUROPE' + + if anomaly == "restricao_soberania_brasil": + if decision_id is None: + return False + sat = next((s for s in fleet if s['id'] == decision_id), {}) + return sat.get('region') == 'BRAZIL' + + if anomaly == "falha_hardware_camera_esq": + return decision_id is None # correto = drop + + return True # anomalia desconhecida — sem critério de verificação + + def _apply_decision(self, task, decision_id, current_time_sec, + latency_ms, joules_cost, fleet): + anomalia = task.get('semantic_anomaly', '') + anomalia_str = f" [anomalia={anomalia}]" if anomalia else "" + compliant = self._semantic_compliant(task, decision_id, fleet) + + if decision_id is not None: + print(f" ---> [{self._engine}] Assign Task {task['id']} to SAT {decision_id}" + f"{anomalia_str} | {latency_ms:.0f}ms | {joules_cost:.3f}J" + f"{' ✗semantic' if not compliant else ''}") + for s in self.mec_satellites: + if getattr(s, 'nodeID') == decision_id: + s.mec_tasks_completed += 1 + s.mec_ram_free = max(0, s.mec_ram_free - task.get('ram', 0)) + s.mec_battery_soc_pct = max(0.0, s.mec_battery_soc_pct - TASK_ENERGY_COST_PCT) + break + self.active_tasks.append( + (current_time_sec + TASK_DURATION_S, decision_id, task.get('ram', 0)) + ) + else: + print(f" ---> [{self._engine}] NO ROUTE Task {task['id']}" + f"{anomalia_str} | {latency_ms:.0f}ms | {joules_cost:.3f}J") + self.mec_tasks_dropped += 1 + + self.task_log.append({ + "task_id": task['id'], + "region": task.get('region', ''), + "anomaly": anomalia, + "arrival_time_s": task.get('arrival_time', current_time_sec), + "decision_time_s": current_time_sec, + "latency_ms": round(latency_ms, 3), + "joules_cost": round(joules_cost, 4), + "decision_sat_id": decision_id if decision_id is not None else "", + "success": 1 if decision_id is not None else 0, + "semantic_compliant": 1 if compliant else 0, + "engine": self._engine, + }) + + # ------------------------------------------------------------------ # + # Main loop step # + # ------------------------------------------------------------------ # + + def step(self, current_time_sec): + self._last_step_time = current_time_sec + + # 1. Libera RAM de tarefas concluídas + self._release_completed_tasks(current_time_sec) + + # 2. Atualiza bateria (drena eclipse / carrega sol a cada tick de 5s) + self._update_battery(current_time_sec) + + # 3. Processa inferências SLM concluídas neste tick + if hasattr(self.brain, 'check_completed_inferences'): + for task, decision_id in self.brain.check_completed_inferences(current_time_sec): + latency_ms = SLM_NPU_LATENCY_MS # latência NPU simulada (não wall-clock da API) + joules_cost = JOULES_PER_DECISION.get("SLM", 0.1) + fleet = task.get('slm_fleet_snapshot', []) + self._apply_decision(task, decision_id, current_time_sec, + latency_ms, joules_cost, fleet) + + # 4. Poisson: inicializa ou verifica chegada de nova tarefa + if self.next_task_time < 0: + self.next_task_time = current_time_sec + random.expovariate(self.lambda_rate) + + if current_time_sec < self.next_task_time: + return + + # 5. Gera nova tarefa + self.task_id += 1 + rand_val = random.random() + if rand_val < 0.33: + region = "BRAZIL" + elif rand_val < 0.66: + region = "USA" + else: + region = "EUROPE" + + task = {"id": self.task_id, "region": region, "ram": 500, + "arrival_time": current_time_sec} + + if random.random() < self.anomaly_rate: + task["semantic_anomaly"] = random.choice([ + "restricao_gdpr_europa", + "restricao_soberania_brasil", + "falha_hardware_camera_esq", + ]) + + fleet = self._build_fleet(current_time_sec) + anomalia_str = f" [anomalia={task['semantic_anomaly']}]" if task.get('semantic_anomaly') else "" + region_bat = next((f"{s['battery_pct']:.0f}%" for s in fleet if s['region'] == region), '?') + solar_icon = next(("☀" if s['solar_charging'] else "◑" for s in fleet if s['region'] == region), '') + print(f"\n[MEC T+{current_time_sec:.1f}] New Task {self.task_id} ({region} BAT={region_bat}{solar_icon}){anomalia_str}") + + if hasattr(self.brain, 'receive_task'): + # SLM: async + self.brain.receive_task(task, current_time_sec, fleet) + else: + # LLM / BASELINE / DRL: sync + t_wall = time.perf_counter() + decision_id = self.brain.decide(task, fleet, BATTERY_SAFETY_PCT) + wall_ms = (time.perf_counter() - t_wall) * 1000 + + latency_ms = wall_ms + LLM_PROPAGATION_MS if self._engine == "LLM" else wall_ms + joules_cost = JOULES_PER_DECISION.get(self._engine, 0.001) + self._apply_decision(task, decision_id, current_time_sec, + latency_ms, joules_cost, fleet) + + self.next_task_time = current_time_sec + random.expovariate(self.lambda_rate) + + # ------------------------------------------------------------------ # + # Relatórios # + # ------------------------------------------------------------------ # + + def print_stats(self): + print("\n" + "=" * 44) + print(" MEC ORCHESTRATOR REPORT") + print("=" * 44) + total_completed = 0 + + for s in self.mec_satellites: + completed = getattr(s, 'mec_tasks_completed', 0) + region = getattr(s, 'mec_region', 'UNK') + soc = getattr(s, 'mec_battery_soc_pct', 0.0) + cap = getattr(s, 'mec_battery_capacity_wh', 0.0) + solar_str = "Solar" if getattr(s, 'mec_solar_charging', True) else "Eclipse" + total_completed += completed + + print(f"SAT {getattr(s, 'nodeID')} ({region})") + print(f" Battery SOC: {soc:.1f}% ({cap:.0f}Wh) | {solar_str}") + print(f" RAM Free: {getattr(s, 'mec_ram_free', 0):.0f} MB") + print(f" Tasks Completed: {completed}") + print("-" * 22) + + n = len(self.task_log) + print(f"\nTOTAL TASKS GENERATED: {self.task_id}") + print(f"TOTAL COMPLETED: {total_completed}") + print(f"TOTAL REJECTED: {self.mec_tasks_dropped}") + if n > 0: + success_rate = total_completed / self.task_id * 100 + avg_lat = sum(r['latency_ms'] for r in self.task_log) / n + total_joules = sum(r['joules_cost'] for r in self.task_log) + avg_joules = total_joules / n + compliant_n = sum(r['semantic_compliant'] for r in self.task_log) + anomaly_tasks = [r for r in self.task_log if r['anomaly']] + + effective_n = sum(1 for r in self.task_log if r['success'] == 1 and r['semantic_compliant'] == 1) + correct_drops = sum(1 for r in self.task_log if r['success'] == 0 and r['semantic_compliant'] == 1) + print(f"SUCCESS RATE: {success_rate:.1f}% (throughput — tarefas roteadas)") + print(f"EFFECTIVE SUCCESS: {effective_n}/{n} ({effective_n/n*100:.1f}%) (roteadas + semanticamente corretas)") + print(f"CORRECT DROPS: {correct_drops} (drops semanticamente válidos, ex: hardware failure)") + print(f"AVG LATENCY: {avg_lat:.1f} ms") + print(f"TOTAL ENERGY: {total_joules:.3f} J") + print(f"JOULES/DECISION: {avg_joules:.4f} J") + print(f"SEMANTIC COMPLIANCE: {compliant_n}/{n} ({compliant_n/n*100:.1f}%)") + if anomaly_tasks: + anom_ok = sum(r['semantic_compliant'] for r in anomaly_tasks) + print(f"ANOMALY COMPLIANCE: {anom_ok}/{len(anomaly_tasks)}") + + if hasattr(self.brain, 'print_qtable'): + self.brain.print_qtable() + + print("=" * 44 + "\n") + + def save_metrics(self): + os.makedirs("logs", exist_ok=True) + engine = self._engine + + csv_path = f"logs/mec_metrics_{engine}.csv" + fieldnames = [ + "task_id", "region", "anomaly", + "arrival_time_s", "decision_time_s", "latency_ms", + "joules_cost", "decision_sat_id", + "success", "semantic_compliant", "engine", + ] + with open(csv_path, 'w', newline='', encoding='utf-8') as f: + writer = csv.DictWriter(f, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(self.task_log) + + n = len(self.task_log) + summary = {"engine": engine, "total_tasks": n} + if n > 0: + completed = sum(r['success'] for r in self.task_log) + anomaly_tasks = [r for r in self.task_log if r['anomaly']] + compliant = sum(r['semantic_compliant'] for r in self.task_log) + anom_compliant = sum(r['semantic_compliant'] for r in anomaly_tasks) + + summary.update({ + "success_count": completed, + "success_rate": round(completed / n, 4), + "drop_count": self.mec_tasks_dropped, + "drop_rate": round(self.mec_tasks_dropped / n, 4), + "avg_latency_ms": round(sum(r['latency_ms'] for r in self.task_log) / n, 3), + "total_joules": round(sum(r['joules_cost'] for r in self.task_log), 4), + "joules_per_decision": round(JOULES_PER_DECISION.get(engine, 0.001), 4), + "anomaly_task_count": len(anomaly_tasks), + "semantic_compliance_rate": round(compliant / n, 4), + "anomaly_compliance_rate": round(anom_compliant / len(anomaly_tasks), 4) if anomaly_tasks else None, + "effective_success_count": sum(1 for r in self.task_log if r['success'] == 1 and r['semantic_compliant'] == 1), + "effective_success_rate": round(sum(1 for r in self.task_log if r['success'] == 1 and r['semantic_compliant'] == 1) / n, 4), + "correct_drop_count": sum(1 for r in self.task_log if r['success'] == 0 and r['semantic_compliant'] == 1), + }) + # RAM utilization ao final da simulação + ram_util = [ + (s.mec_ram_total - s.mec_ram_free) / s.mec_ram_total * 100 + for s in self.mec_satellites + ] + summary["avg_ram_utilization_pct"] = round(sum(ram_util) / len(ram_util), 1) if ram_util else 0.0 + # Estado final de bateria por satélite + for s in self.mec_satellites: + summary[f"sat_{getattr(s, 'nodeID')}_final_battery_pct"] = round( + getattr(s, 'mec_battery_soc_pct', 0.0), 1 + ) + + json_path = f"logs/mec_summary_{engine}.json" + with open(json_path, 'w', encoding='utf-8') as f: + json.dump(summary, f, indent=2) + + print(f"[MEC] Metricas salvas: {csv_path} | {json_path}") diff --git a/src/core/task.py b/src/core/task.py new file mode 100644 index 0000000..cefe7e4 --- /dev/null +++ b/src/core/task.py @@ -0,0 +1,70 @@ +# src/core/task.py +''' +@desc + This module defines the Task class. + + A Task represents a unit of computational work that needs to be + scheduled and processed by a SatelliteMEC node. + + UPDATED: Now includes data size (for RAM usage) and deadlines (for QoS). +''' + +class Task: + ''' + @desc + A data class to represent a computational task with MEC requirements. + ''' + def __init__( + self, + _task_id: int, + _mips_required: float, + _creation_time: float, + _data_input_size_mb: float, + _data_output_size_mb: float, + _deadline: float = None + ): + ''' + @param[in] _task_id + A unique identifier for the task. + @param[in] _mips_required + The total computational load (Million Instructions). + Determines CPU time. + @param[in] _creation_time + The simulation time (env.now) when the task was created. + @param[in] _data_input_size_mb + The size of the data to be uploaded/processed (in MB). + Determines RAM usage and I/O time. + @param[in] _data_output_size_mb + The size of the result data (in MB). + Determines downlink/offload transmission time. + @param[in] _deadline (Optional) + The simulation time by which the task MUST be finished. + Used for failure metrics (Task Drop Rate). + ''' + self.id = _task_id + self.mips_required = _mips_required + self.creation_time = _creation_time + + # New MEC Attributes + self.data_input_size = _data_input_size_mb + self.data_output_size = _data_output_size_mb + self.deadline = _deadline + + def is_missed_deadline(self, current_time: float) -> bool: + ''' + @desc + Checks if the task has already missed its deadline. + @return + True if deadline exists and passed, False otherwise. + ''' + if self.deadline is None: + return False + return current_time > self.deadline + + def __str__(self): + ''' + @desc + String representation for logging. + ''' + return (f"Task(id={self.id}, mips={self.mips_required}, " + f"data={self.data_input_size}MB, deadline={self.deadline})") \ No newline at end of file diff --git a/src/nodes/satellite_mec.py b/src/nodes/satellite_mec.py new file mode 100644 index 0000000..294d211 --- /dev/null +++ b/src/nodes/satellite_mec.py @@ -0,0 +1,123 @@ +# src/nodes/satellite_mec.py + +import simpy +from src.nodes.satellitebasic import SatelliteBasic +from src.simlogging.ilogger import ELogType + +# ============================================================================= +# [ITEM 1] MEC ATTRIBUTES: Updated SatelliteMEC Class (VERSÃO FINAL KWARGS) +# ============================================================================= +class SatelliteMEC(SatelliteBasic): + ''' + A Satellite node with CPU, RAM, I/O limits, and Battery consumption. + ''' + # Mudança: Aceitamos **kwargs para passar os argumentos da classe pai pelo NOME + def __init__(self, _env: simpy.Environment, _mec_details, _logger, **kwargs) -> None: + + # Chamada explícita usando kwargs. Isso garante que cada valor vá para a variável certa. + super().__init__( + _nodeID=kwargs['_nodeID'], + _topologyID=kwargs['_topologyID'], + _tleline1=kwargs['_tleline1'], + _tleline2=kwargs['_tleline2'], + _timeDelta=kwargs['_timeDelta'], + _timeStamp=kwargs['_timeStamp'], + _endtime=kwargs['_endtime'], + _Logger=_logger, # Passamos o logger explicitamente + _additionalArgs=kwargs.get('_additionalArgs', "") + ) + + self.env = _env + self.logger = _logger + + # --- COMPUTATION (Processing) --- + self.cpu_capacity_mips = float(_mec_details.cpu_capacity_mips) + self.cpu_resource = simpy.Resource(self.env, capacity=1) + + # --- MEMORY (RAM/Storage) --- + self.ram_capacity_mb = float(getattr(_mec_details, 'ram_capacity_mb', 4096.0)) + self.ram_container = simpy.Container(self.env, capacity=self.ram_capacity_mb, init=self.ram_capacity_mb) + + # --- I/O (Throughput) --- + self.io_throughput_mbs = float(getattr(_mec_details, 'io_throughput_mbs', 500.0)) + + # --- POWER (Energy) --- + self.battery_capacity = float(getattr(_mec_details, 'battery_capacity_joules', 10000.0)) + self.current_battery = self.battery_capacity + self.power_idle = 5.0 + self.power_active = 20.0 + self.total_energy_consumed = 0.0 + self.battery_depleted = False + + self.tasks_completed_count = 0 + self.tasks_dropped_count = 0 + + self.env.process(self.battery_drain_process()) + + # --- (Os métodos abaixo continuam iguais) --- + def get_current_load_percentage(self) -> float: + if self.cpu_resource.capacity == 0: return 0.0 + return (self.cpu_resource.count / self.cpu_resource.capacity) * 100.0 + + def get_ram_usage_percentage(self) -> float: + used = self.ram_capacity_mb - self.ram_container.level + return (used / self.ram_capacity_mb) * 100.0 + + def get_queue_length(self) -> int: + return len(self.cpu_resource.queue) + + def process_task(self, task: Task): + current_time_obj = get_current_sim_time(self.env.now) + + if self.battery_depleted: + self.logger.write_Log(f"Task {task.id} DROPPED. Sat {self.nodeID} dead.", "LOGWARN", current_time_obj) + self.tasks_dropped_count += 1 + return + + if self.ram_container.level < task.data_input_size: + self.logger.write_Log(f"Task {task.id} DROPPED. Sat {self.nodeID} OOM (Req: {task.data_input_size}MB, Free: {self.ram_container.level}MB).", "LOGWARN", current_time_obj) + self.tasks_dropped_count += 1 + return + + yield self.ram_container.get(task.data_input_size) + + try: + current_time_obj = get_current_sim_time(self.env.now) + self.logger.write_Log(f"Task {task.id} accepted. Loading data...", "LOGINFO", current_time_obj) + + io_time = task.data_input_size / self.io_throughput_mbs + yield self.env.timeout(io_time) + + with self.cpu_resource.request() as req: + yield req + current_time_obj = get_current_sim_time(self.env.now) + + if task.is_missed_deadline(self.env.now): + self.logger.write_Log(f"Task {task.id} missed deadline in queue! Processing anyway.", "LOGWARN", current_time_obj) + + self.logger.write_Log(f"Task {task.id} processing...", "LOGINFO", current_time_obj) + processing_time = task.mips_required / self.cpu_capacity_mips + yield self.env.timeout(processing_time) + + self.tasks_completed_count += 1 + total_time = self.env.now - task.creation_time + current_time_obj = get_current_sim_time(self.env.now) + self.logger.write_Log(f"Task {task.id} ({task.type}) FINISHED. Total Time: {total_time:.2f}s", "LOGINFO", current_time_obj) + + finally: + yield self.ram_container.put(task.data_input_size) + + def battery_drain_process(self): + while True: + yield self.env.timeout(1.0) + is_active = (self.cpu_resource.count > 0) + consumption = self.power_active if is_active else self.power_idle + + self.current_battery -= consumption + self.total_energy_consumed += consumption + + if self.current_battery <= 0: + self.battery_depleted = True + self.current_battery = 0 + self.logger.write_Log(f"CRITICAL: Satellite {self.nodeID} BATTERY DEPLETED.", "LOGERROR", get_current_sim_time(self.env.now)) + break \ No newline at end of file diff --git a/src/schedulers/baseline_scheduler.py b/src/schedulers/baseline_scheduler.py new file mode 100644 index 0000000..3d77ba0 --- /dev/null +++ b/src/schedulers/baseline_scheduler.py @@ -0,0 +1,22 @@ +class BaselineScheduler: + def __init__(self): + print(">>> [ENGINE] Baseline Scheduler Initialized (Greedy — battery + RAM | ISL always available)") + + def decide(self, task_dict, fleet, battery_safety_pct=20.0): + candidatos = [] + + for sat in fleet: + if sat.get('region') != task_dict.get('region'): + continue + if sat.get('battery_pct', 100.0) <= battery_safety_pct: + continue + if sat.get('ram_free', 0) < task_dict.get('ram', 0): + continue + candidatos.append(sat) + + if not candidatos: + return None + + # Prioriza maior bateria, desempata por maior RAM livre + best = max(candidatos, key=lambda s: (s.get('battery_pct', 0), s.get('ram_free', 0))) + return best.get('id') diff --git a/src/schedulers/drl_gym_env.py b/src/schedulers/drl_gym_env.py new file mode 100644 index 0000000..214e16e --- /dev/null +++ b/src/schedulers/drl_gym_env.py @@ -0,0 +1,234 @@ +""" +NTNMECEnv — Gymnasium environment for NTN-MEC satellite routing. + +Observation (13 floats, normalized [0,1]): + [0:3] task_region_onehot (USA=0, BRAZIL=1, EUROPE=2) + [3] has_anomaly + [4] sat1_battery_pct / 100 (SAT-1, BRAZIL) + [5] sat1_ram_free / 4096 + [6] sat1_solar_charging + [7] sat2_battery_pct / 100 (SAT-2, EUROPE) + [8] sat2_ram_free / 4096 + [9] sat2_solar_charging + [10] sat15_battery_pct / 100 (SAT-15, USA) + [11] sat15_ram_free / 4096 + [12] sat15_solar_charging + +Action (Discrete 4): + 0 → route to SAT-1 (BRAZIL) + 1 → route to SAT-2 (EUROPE) + 2 → route to SAT-15 (USA) + 3 → DROP + +Reward: + Successful valid route: +1.0 + 0.1*(battery/100) + Drop with no valid cands: -0.5 (forced drop) + Invalid action (wrong -2.0 (region mismatch / battery/RAM fail) + region or resource fail) + Voluntary drop w/ cands: -1.0 +""" + +import numpy as np +import gymnasium as gym +from gymnasium import spaces + +# Satellite definitions — match simulation config +_SATS = [ + {"id": 1, "region": "BRAZIL", "capacity_wh": 75.0}, + {"id": 2, "region": "EUROPE", "capacity_wh": 50.0}, + {"id": 15, "region": "USA", "capacity_wh": 100.0}, +] +_REGION_IDX = {"USA": 0, "BRAZIL": 1, "EUROPE": 2} +_IDX_REGION = {v: k for k, v in _REGION_IDX.items()} +_RAM_TOTAL = 4096.0 +_TASK_RAM = 500.0 +_BAT_SAFETY = 20.0 +_ANOMALY_RATE = 0.10 + +# Hidden anomaly subtypes used only to SHAPE the reward signal during training. +# They are NEVER exposed in the observation (_build_obs only sees has_anomaly) — +# the agent must learn the single best "blind" fallback action for has_anomaly=1, +# the same way it would have to act in the real simulation without reading the +# anomaly's semantic meaning. This preserves the scientific invariant while still +# letting the agent do better than ignoring the flag entirely. +_ANOMALY_TYPES = ["hardware", "gdpr", "sovereignty"] + + +class NTNMECEnv(gym.Env): + """Single-step routing decision environment for NTN-MEC satellites.""" + + metadata = {"render_modes": []} + + def __init__(self): + super().__init__() + self.observation_space = spaces.Box( + low=0.0, high=1.0, shape=(13,), dtype=np.float32 + ) + self.action_space = spaces.Discrete(4) # 0=SAT1, 1=SAT2, 2=SAT15, 3=DROP + self._task = None + self._fleet = None + + # ------------------------------------------------------------------ # + # Core Gym interface # + # ------------------------------------------------------------------ # + + def reset(self, seed=None, options=None): + super().reset(seed=seed) + self._task, self._fleet = self._sample_state() + return self._build_obs(), {} + + def step(self, action): + action = int(action) + decision_id, reward = self._evaluate_action(action) + + # Single-step env — every step is terminal + obs, _ = self.reset() + return obs, reward, True, False, {"decision_id": decision_id} + + def render(self): + pass + + # ------------------------------------------------------------------ # + # State sampling # + # ------------------------------------------------------------------ # + + def _sample_state(self): + rng = self.np_random + + task_region = _IDX_REGION[rng.integers(0, 3)] + has_anomaly = float(rng.random() < _ANOMALY_RATE) + anomaly_type = _ANOMALY_TYPES[rng.integers(0, 3)] if has_anomaly else None + + fleet = [] + for sat in _SATS: + # Sample battery in range realistic for simulation + battery = float(rng.uniform(15.0, 100.0)) + ram_free = float(rng.uniform(0.0, _RAM_TOTAL)) + solar = float(rng.integers(0, 2)) + fleet.append({ + "id": sat["id"], + "region": sat["region"], + "battery_pct": battery, + "ram_free": ram_free, + "solar_charging": bool(solar), + }) + + task = { + "region": task_region, + "ram": _TASK_RAM, + "semantic_anomaly": "anomaly" if has_anomaly else None, + "_anomaly_type": anomaly_type, # hidden — reward shaping only + } + return task, fleet + + # ------------------------------------------------------------------ # + # Observation builder # + # ------------------------------------------------------------------ # + + def _build_obs(self): + region_oh = [0.0, 0.0, 0.0] + region_oh[_REGION_IDX[self._task["region"]]] = 1.0 + has_anomaly = float(bool(self._task.get("semantic_anomaly"))) + + sat_features = [] + for sat in self._fleet: + sat_features += [ + sat["battery_pct"] / 100.0, + sat["ram_free"] / _RAM_TOTAL, + float(sat["solar_charging"]), + ] + + return np.array(region_oh + [has_anomaly] + sat_features, dtype=np.float32) + + # ------------------------------------------------------------------ # + # Action evaluation # + # ------------------------------------------------------------------ # + + def _evaluate_action(self, action): + """Map action → (decision_id, reward). + + Reward is shaped by the hidden anomaly subtype (never exposed in the + observation) so the agent learns the best possible "blind" fallback + action for has_anomaly=1 — it still cannot tell GDPR from a hardware + failure, but training nudges it toward whichever single behavior + maximizes expected compliance across the real anomaly mix. + """ + task_region = self._task["region"] + task_ram = self._task["ram"] + anomaly_type = self._task.get("_anomaly_type") + + # Hardware failures must always be dropped, regardless of resources. + if anomaly_type == "hardware": + if action == 3: + return None, 1.0 + return None, -2.0 + + # GDPR tasks must be routed to EUROPE; sovereignty tasks must be routed + # to BRAZIL specifically (matches ai_logic._semantic_compliant) — only + # plain (no-anomaly) tasks use the task's own region as the target. + if anomaly_type == "gdpr": + required_region = "EUROPE" + elif anomaly_type == "sovereignty": + required_region = "BRAZIL" + else: + required_region = task_region + candidates = [ + s for s in self._fleet + if s["region"] == required_region + and s["battery_pct"] > _BAT_SAFETY + and s["ram_free"] >= task_ram + ] + + if action == 3: # explicit DROP + # Per ai_logic._semantic_compliant, drop is only ever compliant for + # hardware failures (handled above) — GDPR/sovereignty/none always + # require an actual route to be compliant, so drop is never rewarded here. + if candidates: + return None, -1.0 # voluntary drop when a valid route existed + return None, -0.5 # forced drop, no valid candidate available + + # Satellite selection: action 0→SAT[0], 1→SAT[1], 2→SAT[2] + if action >= len(self._fleet): + return None, -2.0 + + chosen = self._fleet[action] + + # Validate: region, battery, RAM + if chosen["region"] != required_region: + return None, -2.0 + if chosen["battery_pct"] <= _BAT_SAFETY: + return None, -2.0 + if chosen["ram_free"] < task_ram: + return None, -2.0 + + reward = 1.0 + 0.1 * (chosen["battery_pct"] / 100.0) + return chosen["id"], reward + + # ------------------------------------------------------------------ # + # Public helper for scheduler use # + # ------------------------------------------------------------------ # + + def build_obs_from_context(self, task_dict, fleet): + """Build observation vector from live simulation state.""" + region_oh = [0.0, 0.0, 0.0] + ridx = _REGION_IDX.get(task_dict.get("region", "USA"), 0) + region_oh[ridx] = 1.0 + has_anomaly = float(bool(task_dict.get("semantic_anomaly"))) + + # fleet must be ordered: SAT-1, SAT-2, SAT-15 (same as _SATS) + sat_order = {s["id"]: i for i, s in enumerate(_SATS)} + ordered_fleet = sorted(fleet, key=lambda s: sat_order.get(s["id"], 99)) + + sat_features = [] + for sat in ordered_fleet: + sat_features += [ + sat.get("battery_pct", 0.0) / 100.0, + sat.get("ram_free", 0.0) / _RAM_TOTAL, + float(sat.get("solar_charging", True)), + ] + + # Pad if fewer than 3 satellites + while len(sat_features) < 9: + sat_features += [0.0, 0.0, 0.0] + + return np.array(region_oh + [has_anomaly] + sat_features[:9], dtype=np.float32) diff --git a/src/schedulers/drl_scheduler.py b/src/schedulers/drl_scheduler.py new file mode 100644 index 0000000..5b42f0b --- /dev/null +++ b/src/schedulers/drl_scheduler.py @@ -0,0 +1,117 @@ +""" +DRL Scheduler — Stable Baselines3 DQN agent for NTN-MEC routing. + +Loads a pre-trained DQN model (models/best_model.zip). +Train with: python scripts/train_drl.py + +Action mapping (same as NTNMECEnv): + 0 → SAT-1 (BRAZIL) + 1 → SAT-2 (EUROPE) + 2 → SAT-15 (USA) + 3 → DROP + +Scientific invariant preserved: agent receives has_anomaly=1 but NOT the anomaly +type — it cannot learn GDPR/sovereignty semantics. This justifies lower +semantic_compliance vs SLM/LLM in comparative results. +""" + +import os +import random +import numpy as np + +_MODEL_PATH = os.path.join(os.path.dirname(__file__), "..", "..", "models", "best_model.zip") + +# Satellite order MUST match NTNMECEnv._SATS +_SAT_ORDER = [ + {"id": 1, "region": "BRAZIL"}, + {"id": 2, "region": "EUROPE"}, + {"id": 15, "region": "USA"}, +] +_REGION_IDX = {"USA": 0, "BRAZIL": 1, "EUROPE": 2} +_RAM_TOTAL = 4096.0 +_BAT_SAFETY = 20.0 + + +class DRLScheduler: + def __init__(self): + from stable_baselines3 import DQN + path = os.path.abspath(_MODEL_PATH) + if not os.path.exists(path): + raise FileNotFoundError( + f"DRL model not found at {path}. " + "Run: python scripts/train_drl.py" + ) + self.model = DQN.load(path) + random.seed(0) # DQN.load() reseeds the global RNG via SB3's set_random_seed, + # shifting the Poisson arrival sequence — restore the canonical seed here. + print(f">>> [ENGINE] DRL Scheduler Initialized (SB3-DQN | {path})") + self._step = 0 + + # ------------------------------------------------------------------ # + # Public interface # + # ------------------------------------------------------------------ # + + def decide(self, task_dict, fleet, battery_safety_pct=_BAT_SAFETY): + obs = self._build_obs(task_dict, fleet) + action, _ = self.model.predict(obs, deterministic=True) + action = int(action) + + decision_id = self._resolve_action(action, task_dict, fleet, battery_safety_pct) + + self._step += 1 + action_names = ["PREFER_SAT1(BR)", "PREFER_SAT2(EU)", "PREFER_SAT15(US)", "DROP"] + sat_str = f"SAT {decision_id}" if decision_id is not None else "None" + print(f" [DRL-DQN step={self._step}] action={action_names[action]} → {sat_str}") + + return decision_id + + # ------------------------------------------------------------------ # + # Observation builder # + # ------------------------------------------------------------------ # + + def _build_obs(self, task_dict, fleet): + region_oh = [0.0, 0.0, 0.0] + ridx = _REGION_IDX.get(task_dict.get("region", "USA"), 0) + region_oh[ridx] = 1.0 + has_anomaly = float(bool(task_dict.get("semantic_anomaly"))) + + fleet_by_id = {s["id"]: s for s in fleet} + sat_features = [] + for sat_def in _SAT_ORDER: + sat = fleet_by_id.get(sat_def["id"], {}) + sat_features += [ + sat.get("battery_pct", 0.0) / 100.0, + sat.get("ram_free", 0.0) / _RAM_TOTAL, + float(sat.get("solar_charging", True)), + ] + + return np.array(region_oh + [has_anomaly] + sat_features, dtype=np.float32) + + # ------------------------------------------------------------------ # + # Action → satellite mapping # + # ------------------------------------------------------------------ # + + def _resolve_action(self, action, task_dict, fleet, battery_safety_pct): + task_region = task_dict.get("region") + task_ram = task_dict.get("ram", 0) + + if action == 3: + return None # explicit DROP + + if action >= len(_SAT_ORDER): + return None + + target_def = _SAT_ORDER[action] + fleet_by_id = {s["id"]: s for s in fleet} + sat = fleet_by_id.get(target_def["id"]) + + if sat is None: + return None + if sat.get("region") != task_region: + return None + if sat.get("battery_pct", 0.0) <= battery_safety_pct: + return None + if sat.get("ram_free", 0.0) < task_ram: + return None + + return sat["id"] diff --git a/src/schedulers/llm_scheduler.py b/src/schedulers/llm_scheduler.py new file mode 100644 index 0000000..c5db987 --- /dev/null +++ b/src/schedulers/llm_scheduler.py @@ -0,0 +1,163 @@ +import collections +import os +import json +import re +import time +import requests +import urllib3 +from dotenv import load_dotenv + +load_dotenv() +urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) + +API_KEY = os.environ.get("GEMINI_API_KEY") +LLM_MODEL = os.environ.get("LLM_MODEL", "gemini-3.1-flash-lite") +RPM_LIMIT = int(os.environ.get("LLM_RPM_LIMIT", "10")) + +_PROMPT_TEMPLATE = """\ +You are a centralized satellite network orchestrator managing a Low-Earth-Orbit constellation. +Inter-satellite links (ISL) are always available — connectivity is never the bottleneck. +Output ONLY valid JSON — no markdown, no explanation. + +TASK: id={task_id}, region={region}, ram={ram} MB, anomaly="{anomaly}" + +AVAILABLE SATELLITES: +{fleet_lines} + +ROUTING RULES (apply in order): + 1. EU data privacy / GDPR anomaly: route to the EUROPE satellite (return its satellite_id=ID). + Ignore task origin region. Drop only if no EUROPE satellite meets battery/RAM requirements. + 2. Data sovereignty / national jurisdiction anomaly (e.g. soberania_brasil): route to the + satellite in the required country (Brazil→SAT in BRAZIL region). Drop only if unavailable. + 3. Critical hardware failure (broken sensor/camera): drop task entirely (satellite_id=null). + 4. No anomaly: route to any satellite in the task's region with battery_pct > 20% and sufficient RAM. + +Output ONLY valid JSON: +{{"satellite_id": , "reason": "one short sentence"}} +""" + + +class LLMScheduler: + def __init__(self): + if not API_KEY: + print("CRITICAL: GEMINI_API_KEY not found in .env!") + else: + print(f">>> [ENGINE] LLM Scheduler Initialized ({LLM_MODEL} | rate limit={RPM_LIMIT} RPM)") + self._call_timestamps: collections.deque = collections.deque() + + # ------------------------------------------------------------------ # + # Rate limiter (sliding window — idêntico ao SLM) # + # ------------------------------------------------------------------ # + + def _rate_limit_wait(self): + now = time.monotonic() + while self._call_timestamps and now - self._call_timestamps[0] > 60.0: + self._call_timestamps.popleft() + if len(self._call_timestamps) >= RPM_LIMIT: + wait = 61.0 - (now - self._call_timestamps[0]) + if wait > 0: + print(f" [LLM Rate Limiter] {len(self._call_timestamps)} calls/min — waiting {wait:.1f}s") + time.sleep(wait) + now = time.monotonic() + while self._call_timestamps and now - self._call_timestamps[0] > 60.0: + self._call_timestamps.popleft() + self._call_timestamps.append(time.monotonic()) + + # ------------------------------------------------------------------ # + # Construção do prompt # + # ------------------------------------------------------------------ # + + def _build_prompt(self, task_dict, fleet): + fleet_lines = "\n".join( + f" SAT {s['id']} | region={s['region']}" + f" | battery_pct={s['battery_pct']:.0f}%" + f" | solar_charging={s['solar_charging']}" + f" | ram_free={s['ram_free']} MB" + for s in fleet + ) + return _PROMPT_TEMPLATE.format( + task_id=task_dict.get("id"), + region=task_dict.get("region", "?"), + ram=task_dict.get("ram", 0), + anomaly=task_dict.get("semantic_anomaly", "none"), + fleet_lines=fleet_lines, + ) + + # ------------------------------------------------------------------ # + # Chamada API # + # ------------------------------------------------------------------ # + + def _call_api(self, prompt): + if not API_KEY: + return None + self._rate_limit_wait() + url = (f"https://generativelanguage.googleapis.com/v1beta/models/" + f"{LLM_MODEL}:generateContent?key={API_KEY}") + headers = {"Content-Type": "application/json"} + data = { + "contents": [{"parts": [{"text": prompt}]}], + "generationConfig": { + "temperature": 0.0, + "responseMimeType": "application/json", + "maxOutputTokens": 128, + }, + } + for attempt in range(3): + try: + t0 = time.perf_counter() + r = requests.post(url, headers=headers, json=data, verify=False, timeout=60) + latency_ms = (time.perf_counter() - t0) * 1000 + if r.status_code == 200: + print(f" [LLM] Resposta em {latency_ms:.0f}ms") + return r.json()["candidates"][0]["content"]["parts"][0]["text"] + elif r.status_code == 429: + wait = 30 * (attempt + 1) + print(f" [LLM Rate Limit] Tentativa {attempt+1}/3 — aguardando {wait}s") + time.sleep(wait) + continue + else: + print(f" [LLM Error] HTTP {r.status_code} — {r.text[:120]}") + break + except Exception as e: + print(f" [LLM Connection Error] Tentativa {attempt+1}/3: {e}") + continue + return None + + # ------------------------------------------------------------------ # + # Interface principal # + # ------------------------------------------------------------------ # + + def decide(self, task_dict, fleet, battery_safety_pct=20.0): + # Pré-filtro: se não há satélite na região com bateria suficiente, poupa a chamada de API + valid_exists = any( + s.get("region") == task_dict.get("region") + and s.get("battery_pct", 100.0) > battery_safety_pct + for s in fleet + ) + if not valid_exists: + return None + + prompt = self._build_prompt(task_dict, fleet) + raw = self._call_api(prompt) + if not raw: + return None + + try: + # JSON mode ativo para Flash-Lite — resposta já é JSON puro + parsed = json.loads(raw) + except json.JSONDecodeError: + # Fallback: extrai primeiro objeto JSON do texto livre + match = re.search(r'\{.*?\}', raw, re.DOTALL) + if not match: + print(f" [LLM Parse Error] Nenhum JSON encontrado: {raw[:80]}") + return None + try: + parsed = json.loads(match.group()) + except json.JSONDecodeError as e: + print(f" [LLM Parse Error] JSON inválido: {e} | raw={raw[:80]}") + return None + + sat_id = parsed.get("satellite_id") + reason = parsed.get("reason", "") + print(f" [LLM] satellite_id={sat_id} | reason={reason}") + return sat_id diff --git a/src/schedulers/slm_scheduler.py b/src/schedulers/slm_scheduler.py new file mode 100644 index 0000000..0552b10 --- /dev/null +++ b/src/schedulers/slm_scheduler.py @@ -0,0 +1,212 @@ +import collections +import os +import json +import re +import time +import requests +import urllib3 +from dotenv import load_dotenv + +load_dotenv() +urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) + +API_KEY = os.environ.get("GEMINI_API_KEY") +SLM_MODEL = os.environ.get("SLM_MODEL", "gemma-4-26b-a4b-it") +NPU_LATENCY_MS = float(os.environ.get("SLM_NPU_LATENCY_MS", "50.0")) +RPM_LIMIT = int(os.environ.get("SLM_RPM_LIMIT", "14")) # conservative under the 15 RPM cap + + +def _parse_gemma_json(text): + """ + Parse JSON from Gemma output which may include chain-of-thought tokens + between fields. Strategy: try full JSON parse first, then extract + key-value pairs and reconstruct, handling null values. + """ + # 1. Try standard JSON parse on the full text or first {...} block + for pattern in (r'\{[^{}]*\}', r'\{.*\}'): + match = re.search(pattern, text, re.DOTALL) + if match: + try: + return json.loads(match.group()) + except json.JSONDecodeError: + pass + + # 2. Extract individual key-value pairs scattered across chain-of-thought text + action = re.search(r'"action"\s*:\s*"([^"]+)"', text) + target = re.search(r'"target_region"\s*:\s*(?:"([^"]*)"|null)', text) + reason = re.search(r'"reason"\s*:\s*"([^"]*)"', text) + if action: + return { + "action": action.group(1), + "target_region": target.group(1) if target and target.group(1) else None, + "reason": reason.group(1) if reason else "", + } + return None + +_PROMPT_TEMPLATE = """\ +CONTEXT: You are an AI scheduler embedded in a Low-Earth-Orbit satellite (edge node). \ +You have strict resource constraints and must make fast routing decisions. \ +Inter-satellite links (ISL) are always available. \ +Output ONLY valid JSON — no markdown, no explanation. + +TASK: + region: {region} + ram_required: {ram} MB + anomaly: "{anomaly}" + +AVAILABLE SATELLITES: +{fleet_lines} + +ROUTING RULES (apply in order): + 1. GDPR/EU privacy anomaly: route exclusively to EUROPE. Drop if unavailable. + 2. Sovereignty/national data anomaly: route to task country region. Drop if unavailable. + 3. Critical hardware failure anomaly: drop the task entirely. + 4. No anomaly: route to satellite in task region with battery_pct > 20% and sufficient RAM. + 5. action=process when routing to task region, action=route when forwarding elsewhere, action=drop only when no valid satellite or hardware failure. + +Output ONLY valid JSON: +{{"action": "process|route|drop", "target_region": "USA|BRAZIL|EUROPE|null", "reason": "one short sentence"}} +""" + + +class SLMScheduler: + def __init__(self): + if not API_KEY: + print(">>> [SLM] WARNING: GEMINI_API_KEY not found. API calls will return None (drop).") + else: + print(f">>> [ENGINE] SLM Scheduler Initialized ({SLM_MODEL} via Gemini API | NPU latency={NPU_LATENCY_MS}ms)") + self.npu_busy_until = 0.0 + self.pending_tasks = [] + self._call_timestamps: collections.deque = collections.deque() + + # ------------------------------------------------------------------ # + # Async state machine # + # ------------------------------------------------------------------ # + + def receive_task(self, task_dict, current_time, fleet): + ready_time = max(current_time, self.npu_busy_until) + (NPU_LATENCY_MS / 1000.0) + self.npu_busy_until = ready_time + task_dict["slm_ready_time"] = ready_time + task_dict["slm_fleet_snapshot"] = list(fleet) + self.pending_tasks.append(task_dict) + return ready_time + + def check_completed_inferences(self, current_time): + completed, remaining = [], [] + for task in self.pending_tasks: + if current_time >= task.get("slm_ready_time", 0): + completed.append((task, self._decide_route(task))) + else: + remaining.append(task) + self.pending_tasks = remaining + return completed + + # ------------------------------------------------------------------ # + # Decision logic # + # ------------------------------------------------------------------ # + + def _decide_route(self, task_dict): + fleet = task_dict.get("slm_fleet_snapshot", []) + t0 = time.perf_counter() + api_result = self._call_gemini(task_dict, fleet) + result = self._resolve_action(api_result, fleet, task_dict) if api_result is not None else None + task_dict["slm_wall_latency_ms"] = (time.perf_counter() - t0) * 1000 + return result + + def _build_prompt(self, task_dict, fleet): + fleet_lines = "\n".join( + f" SAT {s['id']} | region={s['region']}" + f" | battery_pct={s['battery_pct']:.0f}%" + f" | solar_charging={s['solar_charging']}" + f" | ram_free={s['ram_free']:.0f} MB" + for s in fleet + ) + return _PROMPT_TEMPLATE.format( + region=task_dict.get("region", "?"), + ram=task_dict.get("ram", 0), + anomaly=task_dict.get("semantic_anomaly", "none"), + fleet_lines=fleet_lines, + ) + + def _rate_limit_wait(self): + """Block until making another API call stays within RPM_LIMIT calls/minute.""" + now = time.monotonic() + while self._call_timestamps and now - self._call_timestamps[0] > 60.0: + self._call_timestamps.popleft() + if len(self._call_timestamps) >= RPM_LIMIT: + wait = 61.0 - (now - self._call_timestamps[0]) + if wait > 0: + print(f" [SLM Rate Limiter] {len(self._call_timestamps)} calls/min — waiting {wait:.1f}s") + time.sleep(wait) + now = time.monotonic() + while self._call_timestamps and now - self._call_timestamps[0] > 60.0: + self._call_timestamps.popleft() + self._call_timestamps.append(time.monotonic()) + + def _call_gemini(self, task_dict, fleet): + if not API_KEY: + return None + self._rate_limit_wait() + url = (f"https://generativelanguage.googleapis.com/v1beta/models/" + f"{SLM_MODEL}:generateContent?key={API_KEY}") + headers = {"Content-Type": "application/json"} + data = { + "contents": [{"parts": [{"text": self._build_prompt(task_dict, fleet)}]}], + "generationConfig": { + "temperature": 0.0, + "maxOutputTokens": 1024, + }, + } + for attempt in range(4): + try: + t0 = time.perf_counter() + r = requests.post(url, headers=headers, json=data, verify=False, timeout=30) + latencia_ms = (time.perf_counter() - t0) * 1000 + if r.status_code == 200: + parts = r.json()["candidates"][0]["content"].get("parts", []) + # Gemma returns thinking and the final answer as separate parts + # (each tagged "thought": true/false) — use the non-thought + # parts; some thinking-heavy responses omit the flag on the + # final part, so fall back to concatenating everything. + answer_parts = [p.get("text", "") for p in parts if not p.get("thought", False)] + raw = "".join(answer_parts) if answer_parts else "".join(p.get("text", "") for p in parts) + combined = "{" + raw if not raw.lstrip().startswith("{") else raw + result = _parse_gemma_json(combined) + if result is None: + print(f" [SLM Parse Error] Tentativa {attempt+1}/4 — No JSON found in: {raw[:80]}") + continue + print(f" [SLM {SLM_MODEL} {latencia_ms:.0f}ms] action={result.get('action')}" + f" target={result.get('target_region')} reason={result.get('reason', '')}") + return result + elif r.status_code == 429: + wait = 30 * (attempt + 1) + print(f" [SLM Rate Limit] Tentativa {attempt+1}/4 — aguardando {wait}s") + time.sleep(wait) + continue + elif r.status_code == 500: + wait = 10 * (attempt + 1) + print(f" [SLM Server Error 500] Tentativa {attempt+1}/4 — aguardando {wait}s") + time.sleep(wait) + continue + else: + print(f" [SLM Error] HTTP {r.status_code} — {r.text[:120]}") + break + except Exception as e: + print(f" [SLM Connection Error] {e}") + continue + return None + + def _resolve_action(self, api_result, fleet, task_dict): + action = api_result.get("action", "drop") + target_region = api_result.get("target_region") + if action == "drop": + return None + if action == "process": + target_region = task_dict.get("region") + if target_region: + for s in fleet: + if (s.get("region") == target_region + and s.get("battery_pct", 100.0) > 20.0 + and s.get("ram_free", 0) >= task_dict.get("ram", 0)): + return s.get("id") + return None diff --git a/src/sim/managerparallel.py b/src/sim/managerparallel.py index cd3cf81..2ed6744 100644 --- a/src/sim/managerparallel.py +++ b/src/sim/managerparallel.py @@ -1,12 +1,14 @@ -''' -// Copyright (c) Microsoft Corporation. -// Licensed under the MIT license. +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. +''' Created by: Tusher Chakraborty Created on: 13 Dec 2022 @desc This module implements the ManagerParallel class of the simulator. It leverages the parallel computing capabilities offered by Python + + [MODIFIED]: Integrated GeminiBrain & MECOrchestrator directly into the simulation loop. ''' import concurrent.futures import pickle @@ -16,6 +18,10 @@ import time import numpy as np +# --- MEC INTEGRATION --- +from src.ai_logic import MECOrchestrator +# ----------------------- + from src.nodes.itopology import ITopology from src.sim.imanager import IManager, EManagerReqType from src.nodes.inode import ENodeType @@ -29,92 +35,42 @@ class ManagerParallel(IManager): __numOfSteps : int def __get_Topologies(self, **_kwargs) -> 'list[ITopology]': - ''' - @desc - List of topologies that the manager instance is handling - @return - List of ITopology - ''' return self.__topologies __reqHandlerDictionary = { EManagerReqType.GET_TOPOLOGIES : __get_Topologies } - def req_Manager(self, - _reqType: EManagerReqType, - **_kwargs): - ''' - @desc - Send a request to the manager through this method - @param[in] _reqType - Type of the request - @param[in] _kwargs - Keyworded arguments that are passed to the handler function - @return - Returns the results (if any) - ''' + def req_Manager(self, _reqType: EManagerReqType, **_kwargs): _ret = None - try: _ret = self.__reqHandlerDictionary[_reqType](self, **_kwargs) except: print("[Simulator Warning]: An unhandled request has been received in the req_Manager() method.") - return _ret - # The definition of API handlers - + # The definition of API handlers (Mantidos originais para compatibilidade) def __call_ModelAPIsByModelName(self, **_kwargs): - ''' - @desc - This method calls the APIs of a model of a particular node - @param[in] _kwargs - Keyworded arguments - @key _topologyID - ID of the topology from which the data will be downloaded - @key _nodeID - ID of the node - @key _modelName - Name of the model - @key _apiName - Name of the API - @key _apiArgs - Arguments of the API - @return - The API return - ''' - #check whether we have the keys - if ("_topologyID" not in _kwargs) or \ - ("_nodeID" not in _kwargs) or \ - ("_modelName" not in _kwargs) or \ - ("_apiName" not in _kwargs): + if ("_topologyID" not in _kwargs) or ("_nodeID" not in _kwargs) or \ + ("_modelName" not in _kwargs) or ("_apiName" not in _kwargs): raise Exception("[API: call_ModelAPIsByModelName]: The keyworded arguments are not complete for the API") - #get the topology ID _topologyID = _kwargs["_topologyID"] - #get the node ID _nodeID = _kwargs["_nodeID"] - #get the model name _modelName = _kwargs["_modelName"] - #get the API name _apiName = _kwargs["_apiName"] - #get the API arguments _apiArgs = _kwargs["_apiArgs"] - #get the node instance from the topology try: _node = self.__topologies[_topologyID].get_Node(_nodeID) except: raise Exception("[API: call_ModelAPIsByModelName]: The node instance could not be found in the topology") - #get the model instance from the node try: _model = _node.has_ModelWithName(_modelName) except Exception as e: raise Exception(f"[API: call_ModelAPIsByModelName]: The model instance could not be found in the node due to {e}") - #call the API from the model try: _ret = _model.call_APIs(_apiName, **_apiArgs) except Exception as e: @@ -123,38 +79,18 @@ def __call_ModelAPIsByModelName(self, **_kwargs): return _ret def __get_NodeInfo(self, **_kwargs): - ''' - @desc - This method returns the information of a node - @param[in] _kwargs - Keyworded arguments - @key topologyID - ID of the topology from which the data will be downloaded - @key nodeID - ID of the node - @key infoType - Type of the information - ''' - #check whether we have the keys - if ("_topologyID" not in _kwargs) or \ - ("_nodeID" not in _kwargs) or \ - ("_infoType" not in _kwargs): + if ("_topologyID" not in _kwargs) or ("_nodeID" not in _kwargs) or ("_infoType" not in _kwargs): raise Exception("[API: get_NodeInfo]: The keyworded arguments are not complete for the API") - #get the topology ID _topologyID = _kwargs["_topologyID"] - #get the node ID _nodeID = _kwargs["_nodeID"] - #get the information type _infoType = _kwargs["_infoType"] - #get the node instance from the topology try: _node = self.__topologies[_topologyID].get_Node(_nodeID) except: raise Exception("[API: get_NodeInfo]: The node instance could not be found in the topology") - # Check the node information type using the infoType argument with switch case _nodeInfo = None match _infoType: case "time": @@ -167,27 +103,10 @@ def __get_NodeInfo(self, **_kwargs): return _nodeInfo def __pause_AtTime(self, **_kwargs): - ''' - @desc - This method pauses the simulation at a particular time step. - It will return a threading.Event object which will be used to pause the simulation. - If you'd like to pause the simulation multiple times, it is recommended to do it while the simulation is paused. - This will overwrite the previous pause - @param[in] _kwargs - Keyworded arguments - @key _timestep - Time step at which the simulation will be paused (an integer) - @return - A threading.Event object which will be set when the simulation is paused. - Returns None if the pause timestep has already passed. - ''' if ("_timestep" not in _kwargs): raise Exception("[API: __pause_AtTime]: The keyworded arguments are not complete for the API") - #Let's get the pause timestep _pauseTimeStep = _kwargs["_timestep"] - - #get the current time step _currentTimeStep = self.__currentStep if _pauseTimeStep < _currentTimeStep: @@ -197,124 +116,51 @@ def __pause_AtTime(self, **_kwargs): return self.__stoppingCondition def __resume(self, **_kwargs): - """ - @desc - This method resumes the simulation. - Call this method after you have paused the simulation. - """ self.__stoppingCondition.clear() self.__resumingCondition.set() def __compute_FOVs(self, **_kwargs): - """ - @desc - Call this method to compute all the FOVs of all the nodes in the topology. - This method should be called before the simulation starts. - The idea here is to pre-compute all the FOVs then load them during the simulation. - This will save a lot of time, especially if you are running the same simulation multiple times or have a lot of cores. - @param[in] _kwargs - Keyworded arguments: - @key _outputPath - Optional path to the output file where the FOVs will be stored. - If you store the FOVs, you can use the load_FOVs method to load them during a simulation. - If you decide not to store them, the FOVs will be updated in the node instances. - @key _numProcesses - Optional number of processes to use for the computation. Default is number of existing CPUs. - """ _numProcesses = mp.cpu_count() if ("_numProcesses" in _kwargs): _numProcesses = _kwargs["_numProcesses"] - _nodeQueue = mp.Queue() #queue to store the node IDs to be processed - _fovQueue = mp.Queue() #the output queue to store the FOVs + _nodeQueue = mp.Queue() + _fovQueue = mp.Queue() - def __processMethod(): - #This is the method that will be run by each process + def __processMethod(): try: - #let's calculate how many ever FOVs we can - #If you look through the model, you will see that each one internally stores their FOVs. So, we just need to calculate then retrieve them - _lastNodeID = -1 #this is to keep track of the last node ID that was processed + _lastNodeID = -1 while True: try: _satID = _nodeQueue.get(timeout=1) - #Let's add in the skyfield model again. Look below for the reason - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _satID, - _modelName = "ModelOrbit", - _apiName = "setup_Skyfield", - _apiArgs = {} - ) - - #Let's actually compute the FOVs - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _satID, - _modelName = "ModelFovTimeBased", - _apiName = "find_Passes", - _apiArgs = { - "_targetNodeTypes" : [ENodeType.GS, ENodeType.IOTDEVICE] - }) - + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _satID, _modelName = "ModelOrbit", _apiName = "setup_Skyfield", _apiArgs = {}) + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _satID, _modelName = "ModelFovTimeBased", _apiName = "find_Passes", _apiArgs = {"_targetNodeTypes" : [ENodeType.GS, ENodeType.IOTDEVICE]}) _lastNodeID = _satID - - except queue.Empty: - #We have processed all the nodes - break - + except queue.Empty: break except Exception as _e: - #We have an exception. Let's print it and exit - print(f"[API: compute_FOVs]: An exception occurred while computing FOVs: {_e}") + print(f"[API: compute_FOVs]: An exception occurred: {_e}") exit(1) if _lastNodeID != -1: - #Let's get the FOVs and store them in the queue - _dict = self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _satID, - _modelName = "ModelFovTimeBased", - _apiName = "get_GlobalDictionary", - _apiArgs = {} - ) + _dict = self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _satID, _modelName = "ModelFovTimeBased", _apiName = "get_GlobalDictionary", _apiArgs = {}) _fovQueue.put(_dict) - - #let's exit the process except Exception as _e: - print(f"[API: compute_FOVs]: An exception occurred while computing FOVs: {_e}") + print(f"[API: compute_FOVs]: An exception occurred: {_e}") exit(1) - - #let's exit the process return - #Now back to the main process - assert len(self.__topologies) == 1, "[API: compute_FOVs]: This method is only supported for a single topology" - - #We're going to loop through all the satellites, which will then find the passes for all the ground stations/IoT devices + assert len(self.__topologies) == 1, "[API: compute_FOVs]: Only supported for single topology" _sats = self.__topologies[0].get_NodesOfAType(ENodeType.SAT) for _sat in _sats: - #We need to remove the pickle skyfield object from the node instance. - #See the API documentation for more details - #TODO: This is a hack. We need to find a better way to do this - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _sat.nodeID, - _modelName = "ModelOrbit", - _apiName = "remove_Skyfield", - _apiArgs = {} - ) - + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _sat.nodeID, _modelName = "ModelOrbit", _apiName = "remove_Skyfield", _apiArgs = {}) _nodeQueue.put(_sat.nodeID) - #let's create the processes _processes = [] for _ in range(_numProcesses): _process = mp.Process(target=__processMethod) _processes.append(_process) _process.start() - #In this main process, we need to keep checking if the processes are done and emptying the fovQueue - #We can't use the join() yet because the queue's buffer might get full and the processes might get stuck - #So, we need to keep checking if the processes are done and emptying the queue _processDicts = [] while True: _allDone = True @@ -322,93 +168,41 @@ def __processMethod(): if _process.is_alive(): _allDone = False break - if _allDone: - break + if _allDone: break else: - #Let's wait for 1 second time.sleep(1) - #Let's check if the queue is empty - while not _fovQueue.empty(): - _processDicts.append(_fovQueue.get()) + while not _fovQueue.empty(): _processDicts.append(_fovQueue.get()) - #Let's reap the processes - for _process in _processes: - _process.join() + for _process in _processes: _process.join() - #Let's combine all the dictionaries. Non-sat devices will have their FOVs spread across multiple dictionaries _outputFOV = {} for _fovDict in _processDicts: for _nodeID, _fovArray in _fovDict.items(): - if _fovArray is None or _fovArray.shape[0] == 0: - continue + if _fovArray is None or _fovArray.shape[0] == 0: continue _thisList = _outputFOV.get(_nodeID, None) - - if _thisList is None: - _thisList = _fovArray - else: - _thisList = np.concatenate((_thisList, _fovArray), axis=0) - + if _thisList is None: _thisList = _fovArray + else: _thisList = np.concatenate((_thisList, _fovArray), axis=0) _outputFOV[_nodeID] = _thisList - #Load the FOVs into the nodes - #Since we're using a single topology, we can just use the first node - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _sats[0].nodeID, - _modelName = "ModelFovTimeBased", - _apiName = "set_GlobalDictionary", - _apiArgs = { - "_globalDictionary" : _outputFOV - } - ) + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _sats[0].nodeID, _modelName = "ModelFovTimeBased", _apiName = "set_GlobalDictionary", _apiArgs = {"_globalDictionary" : _outputFOV}) - #Let's add the skyfield model again. Look above for the reason for _sat in _sats: - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = _sat.nodeID, - _modelName = "ModelOrbit", - _apiName = "setup_Skyfield", - _apiArgs = {} - ) + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = _sat.nodeID, _modelName = "ModelOrbit", _apiName = "setup_Skyfield", _apiArgs = {}) - #Now, let's save it to a file if needed if ("_outputPath" in _kwargs): - _outputPath = _kwargs["_outputPath"] - with open(_outputPath, "wb") as _f: - pickle.dump(_outputFOV, _f) + with open(_kwargs["_outputPath"], "wb") as _f: pickle.dump(_outputFOV, _f) def __load_FOVs(self, **_kwargs): - """ - @desc - This method loads the FOVs from a file and sets it to the nodes - Look in the compute_FOVs() method for details on generating the FOVs - @param[in] _kwargs - _inputPath: Path to the file containing the FOVs - """ _inputPath = _kwargs["_inputPath"] with open(_inputPath, "rb") as _f: _fovDict = pickle.load(_f) - self.__call_ModelAPIsByModelName( - _topologyID = 0, - _nodeID = 0, - _modelName = "ModelFovTimeBased", - _apiName = "set_GlobalDictionary", - _apiArgs = { - "_globalDictionary" : _fovDict - } - ) + self.__call_ModelAPIsByModelName(_topologyID = 0, _nodeID = 0, _modelName = "ModelFovTimeBased", _apiName = "set_GlobalDictionary", _apiArgs = {"_globalDictionary" : _fovDict}) def __run_OneStep(self, **_kwargs): - ''' - @desc - This method is called to run one step of the simulation. - ''' for _topology in self.__topologies: for _node in _topology.nodes: _node.Execute() - # API dictionary where API name is the key and handler function is the value __apiHandlerDictionary = { "call_ModelAPIsByModelName" : __call_ModelAPIsByModelName, "get_NodeInfo" : __get_NodeInfo, @@ -420,62 +214,45 @@ def __run_OneStep(self, **_kwargs): "run_OneStep" : __run_OneStep } - def call_APIs(self, - _api: str, - **_kwargs): - ''' - This method acts as a runtime API interface of the manager. - An API offered by the manager can be invoked through this method in runtime. - @param[in] _api - Name of the API. Each model should have a list of the API names. - @param[in] _kwargs - Keyworded arguments that are passed to the corresponding API handler - @return - The API return - ''' + def call_APIs(self, _api: str, **_kwargs): _ret = None - try: - _ret = self.__apiHandlerDictionary[_api](self, - **_kwargs) + _ret = self.__apiHandlerDictionary[_api](self, **_kwargs) except Exception as e: print(f"[Runtime API Manager]: An exeption has been raised while executing the API: {e}") - return _ret - def __init__( - self, - **_simEnv): - ''' - @desc - Constructor of the class. - @param[in] __simEnv - Simulation environment embedded in a keyworded arbitrary arguments as follows - @key topologies - List of the topologies - @key numOfSimSteps - Number of setps (epochs) we want to run the simulator - @key deltaTime - Time delta between each simulation epoch - @key numOfWorkers - Number of threads to be used for the simulation - ''' + def __init__(self, **_simEnv): self.__topologies = _simEnv["topologies"] self.__numOfSteps = int(_simEnv["numOfSimSteps"]) self.__numOfThreads = int(_simEnv["numOfWorkers"]) self.__currentStep = 0 - self.__timeStepToStop = None - # This is the threading.Condition() object that is used to pause the simulation self.__stoppingCondition = threading.Event() self.__resumingCondition = threading.Event() - # update the manager instance of all the node objects for _topology in self.__topologies: for _node in _topology.nodes: _node.add_ManagerInstance(self) + + # --- MEC INJECTION START --- + print(">>> [MEC] ManagerParallel Initialized. Setting up AI...") + # Como o MECOrchestrator agora cria o seu próprio motor, só precisas disto: + self.mec_orchestrator = MECOrchestrator() + + # Extrai nós das topologias para configurar + all_nodes = [] + if isinstance(self.__topologies, dict): + for t in self.__topologies.values(): + if hasattr(t, 'nodes'): all_nodes.extend(t.nodes) + elif isinstance(self.__topologies, list): + for t in self.__topologies: + if hasattr(t, 'nodes'): all_nodes.extend(t.nodes) + + self.mec_orchestrator.setup_nodes(all_nodes) + # --- MEC INJECTION END --- def run_Sim(self): @@ -486,37 +263,44 @@ def run_Sim(self): # To keep the nodes in sync, we ensure that the threads join at the end of each step. while self.__currentStep < self.__numOfSteps: - # Check if the simulation is to be paused. If it is, then we wait until the user resumes it + # Pause logic if self.__timeStepToStop is not None and self.__timeStepToStop == self.__currentStep: - #Let's set the stopping condition to true self.__stoppingCondition.set() - #Let's wait until the user resumes the simulation self.__resumingCondition.wait() - #Let's reset the stopping and resuming conditions self.__resumingCondition.clear() if self.__currentStep % 60 == 0: print(f"[Running Sim]: Current step: {self.__currentStep}") + # --- COSMIC BEATS PHYSICS (Original) --- if self.__numOfThreads > 1: with concurrent.futures.ThreadPoolExecutor(max_workers=self.__numOfThreads) as executor: _results = [] - #Let's execute all the nodes in parallel for _topology in self.__topologies: for _node in _topology.nodes: _result = executor.submit(_node.Execute) _results.append(_result) - - #Once all the threads are done, we can check if there are any exceptions that were raised, then we can raise them - #If we don't do this, then the exceptions will be ignored and the nodes will be out of sync for _result in _results: _result.result() else: for _topology in self.__topologies: for _node in _topology.nodes: - _node.Execute() + _node.Execute() + # --------------------------------------- + + # --- MEC ORCHESTRATION (Injected) --- + # Assume delta=5.0s (padrão no config.json) + # Precisamos do tempo absoluto em segundos para o escalonador + simulation_time = self.__currentStep * 5.0 + self.mec_orchestrator.step(simulation_time) + # ------------------------------------ + self.__currentStep += 1 - - #Just to be sure, let's raise the stopping condition - some nodes might be waiting for it - self.__stoppingCondition.set() + # --- MEC REPORT --- + if hasattr(self, 'mec_orchestrator'): + self.mec_orchestrator.print_stats() + self.mec_orchestrator.save_metrics() + # ------------------ + + self.__stoppingCondition.set() \ No newline at end of file diff --git a/src/sim/simulator.py b/src/sim/simulator.py index 71ea8a7..9b7c056 100644 --- a/src/sim/simulator.py +++ b/src/sim/simulator.py @@ -1,87 +1,16 @@ -''' -// Copyright (c) Microsoft Corporation. -// Licensed under the MIT license. - -Created by: Tusher Chakraborty -Created on: 07 Nov 2022 -@desc - This module implements the simulator class. It's the face of simulation pipeline. -''' - +# src/sim/simulator.py (Versão Limpa) from src.sim.orchestrator import Orchestrator from src.sim.imanager import IManager from src.sim.managerparallel import ManagerParallel - +import time class Simulator(): - ''' - This is the entry class to our simulation pipeline. - It invokes the orchestrator and hands over the simulation environment to the manager. - ''' - _configFilePath: str - _orchestrator: Orchestrator - _manager: IManager - - def __init__( - self, - _configfilepath: str, - _numWorkers: int = 1) -> None: - ''' - @desc - Constructor of the simulator class. - @param[in] _configfilepath - File path to the configuration file - @param[in] _numWorkers - Number of workers to be used for parallel execution - ''' + def __init__(self, _configfilepath: str, _numWorkers: int = 1) -> None: self.__configFilePath = _configfilepath - - # invoke the orchestrator to create the simulation environment self.__orchestrator = Orchestrator(self.__configFilePath) self.__orchestrator.create_SimEnv() __simEnv = self.__orchestrator.get_SimEnv() + self.__manager = ManagerParallel(topologies = __simEnv[0], numOfSimSteps = __simEnv[1], numOfWorkers = _numWorkers) - # hand over the simulation environment to the manager - self.__manager = ManagerParallel( - topologies = __simEnv[0], - numOfSimSteps = __simEnv[1], - numOfWorkers = _numWorkers - ) - - def call_RuntimeAPIs(self, - _api: str, - **_kwargs): - ''' - This method acts as a runtime API interface of the manager. - An API offered by the manager can be invoked through this method in runtime. - @param[in] _api - Name of the API. Each model should have a list of the API names. - @param[in] _kwargs - Keyworded arguments that are passed to the corresponding API handler - @return - The API return - ''' - - _ret = None - # check that manager is not None - if(self.__manager is None): - raise Exception("[Simulator]: Manager is not initialized") - - #check that the API name is not None - if(_api is None): - raise Exception("[Simulator]: API name needs to be provided") - - # call the API from the manager - try: - _ret = self.__manager.call_APIs(_api, **_kwargs) - except Exception as e: - raise Exception(f"[Simulator]: The API call returned an exception: {e}") - - return _ret - def execute(self): - ''' - @desc - Executes the simulation - ''' self.__manager.run_Sim() \ No newline at end of file diff --git a/test_api.py b/test_api.py new file mode 100644 index 0000000..e20559c --- /dev/null +++ b/test_api.py @@ -0,0 +1,88 @@ +import os +import requests +import urllib3 +from dotenv import load_dotenv + +load_dotenv() +urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) +API_KEY = os.environ.get("GEMINI_API_KEY") + +def test_connectivity(): + print("--- 1. Verificando Chave API ---") + if not API_KEY: + print("ERRO: A variável GEMINI_API_KEY não está definida.") + return + print(f"Chave encontrada: {API_KEY[:5]}...{API_KEY[-5:]}") + + print("\n--- 2. Listando Modelos Disponíveis (GET) ---") + # Este endpoint lista tudo o que sua chave tem permissão para ver + url_list = f"https://generativelanguage.googleapis.com/v1beta/models?key={API_KEY}" + + try: + response = requests.get(url_list, verify=False) # verify=False para o Netskope + + if response.status_code == 200: + data = response.json() + print("SUCESSO! Conexão estabelecida.") + print("Modelos disponíveis para você:") + available_models = [] + if 'models' in data: + for m in data['models']: + # Filtramos apenas os que servem para gerar texto + if 'generateContent' in m['supportedGenerationMethods']: + print(f" - {m['name']}") + available_models.append(m['name']) + else: + print("Nenhum modelo encontrado na lista.") + + return available_models + else: + print(f"FALHA AO LISTAR MODELOS. Código: {response.status_code}") + print(f"Resposta: {response.text}") + return [] + + except Exception as e: + print(f"ERRO CRÍTICO DE CONEXÃO: {e}") + return [] + +def test_generation(model_full_name): + print(f"\n--- 3. Testando Geração com {model_full_name} ---") + # A URL precisa ser exata. O nome do modelo já vem como "models/gemini-pro" da lista + url_gen = f"https://generativelanguage.googleapis.com/v1beta/{model_full_name}:generateContent?key={API_KEY}" + + headers = {'Content-Type': 'application/json'} + data = { + "contents": [{"parts": [{"text": "Hello, are you working?"}]}] + } + + try: + response = requests.post(url_gen, headers=headers, json=data, verify=False) + if response.status_code == 200: + print(f"SUCESSO! O modelo {model_full_name} respondeu:") + print(response.json()['candidates'][0]['content']['parts'][0]['text']) + return True + else: + print(f"ERRO NA GERAÇÃO. Código: {response.status_code}") + print(response.text) + return False + except Exception as e: + print(f"Erro na requisição: {e}") + return False + +if __name__ == "__main__": + models = test_connectivity() + + if models: + # Tenta testar o primeiro modelo da lista que pareça ser o Gemini + print("\n--- Tentando validar o primeiro modelo da lista ---") + # Preferência por gemini-1.5-flash ou gemini-pro + chosen = None + for m in models: + if "gemini-2.0-flash" in m: + chosen = m + break + if not chosen and len(models) > 0: + chosen = models[0] + + if chosen: + test_generation(chosen) \ No newline at end of file