System Architecture
A distributed, event-driven architecture designed for planet-scale telemetry ingestion, real-time stream processing, and sub-second AI inference at the network edge.
Distributed Telemetry Mesh
Lightweight eBPF-based agents deploy at L2–L7 across physical, virtual, and containerized infrastructure. Each agent runs a local inference model for edge anomaly pre-classification, reducing backbone telemetry volume by 94% while preserving full fidelity for flagged events.
→ eBPF kernel probes · XDP fast-path · DPDK integration
→ gRPC streaming backhaul · Protocol Buffers v3
→ Agent footprint: <12MB RAM · <0.3% CPU overhead
→ Supports: Linux 5.4+, FreeBSD 13+, Windows Server 2022+
Neural Mesh Coordinator
Centralized orchestration layer manages agent lifecycle, model distribution, and policy enforcement. Built on a Raft-consensus control plane with automatic leader election and partition tolerance. Coordinates real-time topology graph updates across all regions.
→ Raft consensus · <50ms leader failover
→ Topology graph: directed acyclic multi-graph (DAMG)
→ Policy engine: OPA-based with custom network DSL
→ API: gRPC + REST · OpenTelemetry-native export
Real-Time Event Pipeline
Multi-stage stream processing engine handles 48M+ events/second per node. Windowed aggregation, sessionization, and complex event processing (CEP) run in parallel across a horizontally scalable cluster. Exactly-once semantics with checkpointed state recovery.
→ Apache Kafka backbone · Flink-based CEP engine
→ Windowed aggregation: tumbling, sliding, session
→ Backpressure-aware ingestion · adaptive batching
→ P99 processing latency: <18ms end-to-end
Time-Series & Graph Store
Dual-engine storage combining a custom columnar time-series database optimized for network telemetry with a distributed property graph for topology and dependency modeling. Tiered retention with automatic compaction and downsampling at configurable intervals.
→ Columnar TSDB: 40:1 compression ratio
→ Graph engine: labeled property graph w/ Cypher queries
→ Hot tier: NVMe SSD · Warm: object storage · Cold: archive
→ Query engine: vectorized execution · SIMD-accelerated
Multi-Model Serving Architecture
Heterogeneous model serving infrastructure runs 847+ specialized models simultaneously. Transformer-based anomaly detectors, GNN-based topology analyzers, and reinforcement learning remediation agents share a unified inference bus with dynamic batching and model hot-swapping.
→ NVIDIA Triton Inference Server · TensorRT optimization
→ Dynamic batching · model versioning · A/B serving
→ Edge inference: ONNX Runtime · quantized INT8 models
→ GPU cluster: H100 SXM5 · NVLink 4.0 interconnect
Autonomous Action Framework
Closed-loop remediation powered by a constrained reinforcement learning agent. Actions are bounded by operator-defined safety policies, tested in a network digital twin, and executed via programmatic interfaces to routers, load balancers, DNS, and CDN control planes.
→ RL agent: PPO with safety constraints (CPO variant)
→ Digital twin: full topology simulation pre-execution
→ Rollback: automatic state checkpoint + undo
→ Integrations: BGP/NETCONF/RESTCONF/OpenConfig/gNMI