Dldss-196

The experimental evaluation validates that the system delivers for high‑volume streams while maintaining high throughput and resilience.

# 1. Build images docker build -t registry.example.com/dlss-scheduler:196 -f Scheduler.Dockerfile . docker build -t registry.example.com/dlss-runtime:196 -f Runtime.Dockerfile . dldss-196

# 2. Push docker push registry.example.com/dlss-scheduler:196 docker push registry.example.com/dlss-runtime:196 docker build -t registry

# 3. Apply manifests kubectl apply -f k8s/namespace.yaml kubectl apply -f k8s/configmap.yaml kubectl apply -f k8s/scheduler-deployment.yaml kubectl apply -f k8s/runtime-statefulset.yaml kubectl apply -f k8s/monitoring.yaml Apply manifests kubectl apply -f k8s/namespace

| Risk | Impact | Mitigation | |------|--------|------------| | (large RocksDB snapshots) | May cause temporary latency spikes. | Enable incremental state streaming (only WAL entries) and compress snapshots with LZ4. | | Scheduler Single Point of Failure | Scheduler crash stalls rebalancing. | Deploy scheduler in active‑passive HA mode using etcd for leader election. | | Metric Staleness (Δt too large) | Delayed reaction to spikes. | Adaptive Δt: shrink to 200 ms when queue depth > 80 % of capacity. | | Operator Compatibility (non‑idempotent code) | Duplicate processing during failover. | Enforce exactly‑once contract via the built‑in idempotent commit protocol; provide a linting tool for user code. |