• [ ] 7.2 Create service mesh for microservices communication
  • Implement Istio or similar service mesh
  • Create service discovery and load balancing
  • Implement circuit breaker patterns and retry logic
  • Write distributed tracing and monitoring
  • Requirements: 7.6, 9.5, 9.7

Here's a professionally structured and clearly formatted Markdown document that consolidates both inputs into a comprehensive, readable, and production-grade technical report for Service Mesh Implementation (Task: Service Mesh).

🌐 Service Mesh Implementation – Comprehensive File Mapping & Verification

A production-ready service mesh architecture implemented using Istio, tailored for the semiconductor AI/ML ecosystem. This document provides a complete mapping of requirements to implementation files, detailed feature breakdowns, and verification results.

✅ All requirements fully implemented with enterprise-grade configurations, automation, security, and observability.

✅ Final Verification Summary

✅ Overall Status: COMPLETE & VERIFIED

1. ✅ Implement Istio or Similar Service Mesh

📁 Core Implementation Files

🔧 Key Features Implemented

• High Availability (HA) Control Plane
  • Pilot, Citadel, Galley: 2–5 replicas
  • HPA and PodDisruptionBudget (PDB) for resilience
• Ingress & Egress Gateways
  • LoadBalancer-type external access
  • TLS termination and routing
• Sidecar Injection
  • Automatic via labeled namespaces
  • Resource limits and security context
• Telemetry v2
  • Enabled with Prometheus integration
  • OpenTelemetry support
• Automation & Safety
  • deploy-service-mesh.sh: One-click install with validation
  • cleanup-service-mesh.sh: Full removal with CRD options

2. ✅ Create Service Discovery and Load Balancing

📁 Service Discovery Implementation

🛠 Programmatic Management

🔧 Key Features Implemented

🌐 Service Discovery

• Kubernetes-native DNS: service.namespace.svc.cluster.local
• Automatic Registration: Via Istio sidecar injection
• Subsets: Version-based routing (e.g., v1, v2)

⚖️ Load Balancing Algorithms

🔄 Connection Pooling

• TCP: 30–100 max connections
• HTTP: 16–64 pending requests
• Per Connection: 2–10 max requests (prevents exhaustion)

3. ✅ Implement Circuit Breaker Patterns & Retry Logic

📁 Configuration Files

All DestinationRule and VirtualService configurations in:

• wafer-vision-mesh.yaml
• llm-serving-mesh.yaml
• ai-ml-services-mesh.yaml
• all-services-mesh.yaml

🛠 Programmatic Control

🔧 Key Features Implemented

🚧 Circuit Breaker (outlierDetection)

outlierDetection:
  consecutiveGatewayErrors: 3-5    # Eject after 3–5 errors
  interval: 30s                    # Check every 30 seconds
  baseEjectionTime: 30-60s         # Minimum ejection duration
  maxEjectionPercent: 50           # No more than 50% of instances
  minHealthPercent: 50             # Keep at least 50% healthy

🔁 Retry Logic (retries)

retries:
  attempts: 1-3                    # Service-specific
  perTryTimeout: 10s-150s          # LLMs get longer timeouts
  retryOn: "5xx,reset,connect-failure,refused-stream"

🎯 Service-Specific Examples

💡 Additional Resilience

• Connection Pool Limits: Prevent resource exhaustion
• Dynamic Policy Updates: Via service_mesh_manager.py
• Health Monitoring: Real-time status and ejection tracking

4. ✅ Write Distributed Tracing & Monitoring

📁 Telemetry & Monitoring Files

🔧 Key Features Implemented

🌟 Distributed Tracing (Jaeger + OpenTelemetry)

• 100% Trace Sampling for debugging
• Custom Semiconductor Tags:

  {
    "wafer_batch_id": "WAF-2024-0876",
    "process_step": "Etch-Step-3",
    "equipment_id": "ETCH-04",
    "request_id": "req-9a8b7c6d",
    "user_id": "eng-1024"
  }

• Jaeger Production Setup:
  • Collector: Auto-scales (1–5 replicas)
  • Query: HA (2 replicas)
  • Agent: DaemonSet on all nodes
  • Backend: Elasticsearch for storage

📊 Metrics Collection (Prometheus)

• Prometheus Annotations on all services:

  annotations:
    prometheus.io/scrape: "true"
    prometheus.io/port: "8000"
    prometheus.io/path: "/metrics"

• ServiceMonitor for Prometheus Operator
• Custom Metrics:
  • Wafer processing rate
  • Equipment utilization
  • Model inference latency

📈 Grafana Dashboards

• Service Mesh Overview:
  • Request rate, success rate, P95/P99 latency
  • Service topology map
  • Error heatmaps
• Semiconductor-Specific Views:
  • Batch processing pipeline
  • Equipment health correlation
  • Step-wise yield impact

🔔 Alert Rules (PrometheusRule)

🧪 Service Mesh Manager Monitoring

• get_service_mesh_status() method:
  • Control plane health
  • Data plane proxy status
  • Traffic distribution
  • Version drift detection

📚 Documentation & Support

📘 Documentation Includes:

• Step-by-step deployment instructions
• Debug commands (istioctl proxy-status, proxy-config)
• Performance tuning for high-throughput services
• mTLS and JWT security configuration
• Canary deployment and A/B testing examples

🚀 Advanced Features Implemented (Beyond Requirements)

🔗 Key Relationships & Dependencies

🏁 Conclusion

The Service Mesh Implementation is now fully complete, verified, and production-ready.

✅ Enterprise-grade resilience with circuit breakers and retries

🌐 Seamless service discovery and intelligent load balancing

🔍 Full observability with semiconductor-specific tracing and metrics

🔐 Robust security via mTLS, JWT, and policy enforcement

🤖 Automated lifecycle management via scripts and Python SDK

This service mesh provides a unified, secure, and observable foundation for the entire semiconductor AI ecosystem — enabling scalable, reliable, and maintainable microservices.

✅ Status: All Requirements Implemented & Verified

📁 Fully documented, tested, and CI/CD compatible

Absolutely! Below is a detailed explanation of each component and data flow in the Service Mesh Architecture Diagram for your semiconductor AI ecosystem. This breakdown is designed to help engineers, architects, and stakeholders understand what each element does and how data flows across the system.

🌐 Service Mesh Architecture – Detailed Component & Flow Explanation

This architecture implements Istio as a service mesh to provide secure, observable, and resilient communication between microservices in a semiconductor manufacturing AI/ML environment.

🔷 Overall Architecture Overview

The system is divided into 7 logical layers:

1. External Access
2. Istio Control Plane
3. Data Plane (Service Mesh)
4. Observability Stack
5. Security
6. Automation & Management
7. Custom Semiconductor Context

All communication flows are intercepted by Istio sidecar proxies, enabling policy enforcement, telemetry, and resilience without changing application code.

1. 🌍 External Access

These components manage inbound and outbound traffic to/from the cluster.

🔁 Flow Explanation

• Client → Ingress Gateway: All incoming API calls (e.g., from MES) enter via the Ingress Gateway.
• Ingress → Internal Services: After validation, traffic is routed to the appropriate service.
• Internal → Egress Gateway → External: Outbound calls (e.g., alerting, data export) go through the Egress Gateway for audit and control.

✅ Ensures secure perimeter control with zero direct access to backend services.

2. ⚙️ Istio Control Plane

The brain of the service mesh. It configures and manages all data plane proxies.

🔁 Flow Explanation

• Pilot → Sidecar Proxies: Pushes updated routing rules when new versions are deployed.
• Citadel → Sidecars: Issues mTLS certificates so services can authenticate each other.
• Galley → Pilot: Validates configuration before it’s applied.
• Telemetry → Prometheus/Jaeger: Automatically collects metrics and traces from all service interactions.

✅ Provides zero-trust security, dynamic configuration, and real-time observability.

3. 🧱 Data Plane (Service Mesh)

Where your actual AI/ML services run, each with an Istio sidecar proxy injected.

🔁 Flow Explanation

• Every request between services goes through the sidecar proxy.
• Example: Anomaly Detection → Predictive Maintenance:
  1. Outbound request intercepted by sidecar O
  2. mTLS established with sidecar Q
  3. Request forwarded securely
  4. Telemetry collected (latency, success/failure)
  5. If failure occurs, retry logic or circuit breaker activates

✅ Enables transparent resilience, secure communication, and uniform observability across services.

4. 📊 Observability Stack

Provides full visibility into system health, performance, and behavior.

🔁 Flow Explanation

• Sidecars → Prometheus: Metrics pushed via telemetry v2.
• Sidecars → Jaeger: Traces sampled and sent with custom tags.
• Prometheus → Grafana: Real-time dashboards updated every 15s.
• Jaeger → Trace Dashboard: Engineers can debug slow requests.
• Access Logs → Grafana: Correlated with metrics for deep analysis.

✅ Enables rapid troubleshooting, SLA monitoring, and performance optimization.

5. 🔐 Security

Zero-trust security model enforced by Istio.

🔁 Flow Explanation

• Before any two services communicate:
  1. Their sidecars exchange mTLS certificates.
  2. Identity is verified (e.g., serviceAccount: wafer-vision).
  3. Authorization policy checked.
  4. Connection established only if all checks pass.

✅ Prevents lateral movement, data sniffing, and unauthorized access.

6. 🤖 Automation & Management

Programmatic control and lifecycle management.

• Service registration
• Traffic policy updates
• Health checks
• Circuit breaker monitoring | Z. deploy-service-mesh.sh | Shell script that:
• Installs Istio
• Labels namespaces for auto-injection
• Validates installation
• Shows dashboard access | AA. cleanup-service-mesh.sh | Safely removes Istio with options to delete CRDs (important in shared clusters). |

🔁 Flow Explanation

• DevOps runs deploy-service-mesh.sh → Istio installed
• CI/CD pipeline uses service_mesh_manager.py → Updates traffic rules for canary releases
• On decommission: cleanup-service-mesh.sh → Full removal

✅ Enables GitOps workflows, automated rollouts, and safe upgrades.

7. 🏷️ Custom Semiconductor Context

Tailored telemetry to support semiconductor-specific analysis.

• wafer_batch_id: WAF-2024-0876
• process_step: Etch-Step-3
• equipment_id: ETCH-04 | AD. Custom Metrics | Exposes domain-specific KPIs:
• Equipment utilization %
• Wafer throughput per hour
• Defect density per batch

🔁 Flow Explanation

• When a request starts (e.g., defect classification):
  1. Application adds wafer_batch_id to context
  2. Sidecar injects it into trace headers
  3. Jaeger and Prometheus store it
  4. Grafana dashboards filter by batch or equipment

✅ Enables root cause analysis by batch, equipment health correlation, and process step optimization.

🔗 Key Data Flows Summary

🎯 Business & Engineering Value

✅ Conclusion

This service mesh architecture transforms a collection of AI/ML microservices into a unified, secure, and intelligent platform tailored for semiconductor manufacturing.

It goes beyond basic connectivity to deliver:

• 🔐 Zero-trust security
• 📈 Full observability with domain context
• ⚙️ Resilience via circuit breakers and retries
• 🤖 Automation for DevOps efficiency

All without requiring changes to individual service code — thanks to sidecar-based architecture.