diff --git a/locustfile.py b/locustfile.py
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+++ b/locustfile.py
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+"""Locust load-test for QuickTicket gateway.
+
+Runs in-cluster as a Job so traffic goes through kube-proxy and is
+load-balanced across all gateway replicas. The task mix mirrors real
+traffic: listing events dominates, some checkout, some health-polling.
+
+Reserves are spread across events 3 and 5 (500 + 80 tickets) so a 60s
+run at 50-100 users doesn't exhaust a single event's inventory and
+pollute the error count with 409 Conflicts.
+"""
+import random
+from locust import HttpUser, task, between
+
+
+class QuickTicketUser(HttpUser):
+    wait_time = between(0.5, 2.0)
+
+    @task(7)
+    def list_events(self):
+        self.client.get("/events")
+
+    @task(2)
+    def reserve(self):
+        event = random.choice([3, 3, 3, 5])
+        self.client.post(
+            f"/events/{event}/reserve",
+            json={"quantity": 1},
+            headers={"Content-Type": "application/json"},
+        )
+
+    @task(1)
+    def health(self):
+        self.client.get("/health")
diff --git a/submissions/lab10.md b/submissions/lab10.md
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+++ b/submissions/lab10.md
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+# QuickTicket Reliability Review
+
+## 1. SLO Compliance
+| SLO | Target | Observed | Status |
+| --- | ------ | -------- | ------ |
+| Gateway 5xx Error Rate | < 0.5% | 20.57% (at 100 users) | Missed |
+| Gateway p99 Latency | < 500ms | 2100 ms (at 100 users) | Missed |
+
+## 2. Load Test Results
+| Users | Ramp |   RPS |   p50 |    p95 |    p99 | 5xx error rate | 409 (inventory) |
+| ----: | ---: | ----: | ----: | -----: | -----: | -------------: | --------------: |
+|    10 |  2/s |  7.60 |  19ms |  100ms |  480ms |          0.00% |               0 |
+|    50 |  5/s | 35.96 |  14ms |   63ms |  100ms |          0.00% |              23 |
+|   100 | 10/s | 58.02 | 210ms | 1300ms | 2100ms |        19.27%* |              45 |
+
+*\*Note: 5xx error rate calculated by subtracting 45 (409 Conflict) from 713 total failures = 668 actual 5xx errors (19.27% of total 3467 requests).*
+
+**Breaking Point Analysis:**
+The system's capacity ceiling is approximately **36-40 RPS** at **50 concurrent users**. At 100 users, the system experienced a large degradation: p99 latency reached **2100ms** and the 5xx error rate soared to **19.27%** due to a flood of `502 Bad Gateway`, `503 Service Unavailable`, and `Connection refused` errors, signaling cascading service failure.
+
+## 3. DORA Metrics
+| Metric                      | Value      | Source / Calculation                                                                       |
+| --------------------------- | ---------- | ------------------------------------------------------------------------------------------ |
+| **Deployment Frequency**    | 9 rollouts | Total count of ReplicaSets (`kubectl get rs`) across all services.                         |
+| **Lead Time for Changes**   | ~5 minutes | CI build time (~2 min) + ArgoCD poll interval (~3 min).                                    |
+| **Change Failure Rate**     | 0%         | No failed Argo Rollouts AnalysisRuns detected in the current cluster state.                |
+| **Time to Restore Service** | ~3 minutes | Time to execute `git revert` and wait for ArgoCD sync, or instant abort via Argo Rollouts. |
+
+## 4. Top 3 Reliability Risks
+1. **Ephemeral Database Storage** — Without a PVC, a single Postgres pod restart wipes all order data. *Fix: PersistentVolumeClaims.* 
+2. **Unbounded Database Connection Pool** — High user load directly spawns new Postgres connections, causing services to hit `Connection refused` and crash. *Fix: Implement PgBouncer.*
+3. **Single Point of Failure in Redis** — A single Redis instance handling all ticket reservations limits capacity and acts as a SPOF. *Fix: Deploy a Redis cluster/sentinel setup.*
+
+## 5. Toil Identification
+| Toil Task                                          | Frequency                | Proposed Automation                                              | Time Saved               |
+| -------------------------------------------------- | ------------------------ | ---------------------------------------------------------------- | ------------------------ |
+| Re-seeding DB manually after Postgres pod restarts | Every restart (Labs 1-8) | Attach a PVC so data persists across pod restarts (Lab 9 Bonus). | ~2 mins per incident     |
+| Re-establishing `kubectl port-forward`             | Constantly during dev    | Expose services via Ingress controller / NodePort.               | Interruptions eliminated |
+| Staring at terminal during canary rollouts         | Every deploy (Lab 7)     | Argo Rollouts + Prometheus AnalysisRuns to auto-promote/abort.   | ~5 mins per deploy       |
+
+## 6. Monitoring Gaps
+- **Missing Dependency Latency Alerts:** During Lab 8 chaos experiments, if the database became slow (but didn't throw 5xx), our error-rate SLO wouldn't catch it until the gateway completely timed out. We needed latency-specific alerts for downstream services (events/payments).
+- **Missing Redis Capacity Alerts:** If Redis runs out of memory or connections during a ticket spike, we currently don't have an alert for it until users start seeing 500s from the gateway.
+
+## 7. Capacity Plan
+
+**Per-pod metrics at breaking point (100 users / ~58 RPS, measured during 3-min sustained load):**
+- **Gateway (5 replicas):** ~100m CPU, 42Mi Mem (per pod)
+- **Events (1 replica):** ~413m CPU, 51Mi Mem
+- **Payments (1 replica):** ~23m CPU, 36Mi Mem
+
+**Analysis of Bottleneck:**
+Sustained load testing revealed that the single `events` pod is the main bottleneck, spiking to over 400m CPU. The system failure at 100 users (502 Bad Gateway` and `Connection refused`) `is caused by the events service struggling to process the concurrency queue. It either hits CPU throttling limits or exhausts its internal application thread/connection pool, causing upstream requests from the gateway to time out.` 
+
+**Scaling Plan for 2x Traffic (~80-100 RPS target):**
+- **Events Service:** Scale from 1 to 3 replicas to distribute the processing load. Set explicit resources requests/limits (e.g., Request: 200m, Limit: 500m).
+- **Payments Service:** Scale from 1 to 2 replicas (primarily for High Availability, as CPU usage is very low).
+- **Gateway Service:** Keep at 5 replicas (100m CPU per pod is perfectly healthy and well-distributed).
+- **Database:** Introduce **PgBouncer** before scaling `events` to 3 replicas, to prevent the newly scaled pods from overwhelming the Postgres `max_connections` limit.
+- **Cache:** Move Redis to a Sentinel/Cluster setup to remove the single point of failure.
+
+**Rough Cost Estimate:**
+Assuming small cloud containers (e.g., ~$5/pod per month):
+- 5 Gateway + 3 Events + 2 Payments + 1 Postgres + 3 Redis = 14 total pods.
+- **Estimated Total Cost:** ~$70 / month for the compute footprint.
+
+