Infrastructure Telemetry

Real-time telemetry and hardware pipeline monitoring for AWS & ESP32 edge infrastructure.

Edge Standby • Cloud Core Operational
AWS EC2 c7g.large (Arm Graviton3 — 32.196.120.236)
Vision Engine & Agronomist LLM
  • Status:KleidiCV NEON Active
  • Latency:9.8ms
  • Model:Llama 3.1 8B Q4_K_M
AWS EC2 t2.small (Backend Engine — 3.80.177.174)
Digital Twin & REST / WS Backend Engine
  • Status:DynamoDB Connected
  • Port:8080
  • Engine Mode:Dual-Mode Engine
Amazon DynamoDB (us-east-1)
Spatial Persistence & Log Warehouse
  • Tables:4 Active Tables
  • Status:Nominal Status
  • Buffer:Zero-Loss Write Buffer
AWS IoT Core (aiwa631kpod3d-ats.iot.us-east-1.amazonaws.com)
Secure TLS MQTT Broker (Port 8883)
  • Security:X.509 Mutual Auth
  • Delivery:QoS 1 Delivery
  • Architecture:Zero-Trust Mesh
[STANDBY / AWAITING HEARTBEAT]
ESP32 Edge Hardware Nodes (192.168.1.52)
Physical Actuator & Stepper Gantry Controller
  • Status:[STANDBY / AWAITING HEARTBEAT]
  • Heartbeat:30s Hardware Watchdog
  • Edge Gateway:Middleware Not Connected
Component Health — Last 24h
24 Hourly Segments
AWS EC2 c7g.large (Arm Graviton3 — 32.196.120.236)99.98%
AWS EC2 t2.small (Backend Engine — 3.80.177.174)99.99%
Amazon DynamoDB (us-east-1)100.00%
AWS IoT Core (aiwa631kpod3d-ats.iot.us-east-1.amazonaws.com)100.00%
ESP32 Edge Hardware Nodes (192.168.1.52)Standby
AWS Graviton COOL Benchmark

COOL on AWS Graviton executed the vision pipeline 2.4x faster & 38% more cost-effectively than the x86 baseline.

COOL on AWS Graviton
2.4x
-38% cost per inference vs x86
x86 Baseline
1.0x
Baseline performance