EMBER — Ephemeral Model-Based Burst Execution Runtime

🔥 EMBER

Ephemeral Model-Based Burst Execution Runtime

Predictable compute for an energy-constrained world.

#distributed-systems #inference #python-3.14 #grpc #fault-tolerance

EMBER is a hierarchical control plane that governs high-variance Python inference tasks across a distributed regional fabric. It replaces ad-hoc offloading with deterministic routing — balancing local thermal budgets against regional energy costs so the system behaves predictably even when demand doesn't.

Performance is a side effect. Predictability is the goal.

What it enforces

Latency stays bounded — local p50 ≤ 80ms, p99 ≤ 500ms
Resources don't spiral — 43 fixed workers, 22-slot bounded queue
Memory is shared, not copied — 400MB model weights via mmap, verified at 2MB system delta
Failures are expected — 20ms abort-early, circuit breaker trips at 3 failures
Energy is a first-class citizen — per-job Joule targets, CPU frequency scales with remaining budget

Phase 3 — Steel Thread

Baseline validation proving each architectural pillar under test.

1.96ms

gRPC p99 RTT (20ms budget)

0.09s

43-worker boot time

2MB

System delta (400MB shared)

0.00ms

RTT in OPEN state (zero overhead)

Ticket	Status
Skeleton gRPC & Baseline Network Latency 1,000-ping test, p99 = 1.96ms within 20ms budget	PASS
Admission Controller & Hard Concurrency Limit Concurrency wall, 100ms dead letter TTL, 90%/75% hysteresis	PASS · 7/7
Circuit Breaker & Failure Injection Abort-early at 22ms, 3-failure trip, half-open recovery	PASS · 8/8
Local Execution Substrate 43-worker boot, 400MB shared memory, 450ms task leasing	PASS · 6/6

Architecture

Edge + Regional topology. Chicago is an optimization layer, not a dependency.

Client
  │
  ▼
Edge Controller (Columbus)
  │
  ├── Admission Gate         90%/75% hysteresis, 22-slot queue
  │
  ├── Routing Client ─────── RouteRequest ──→ Chicago Regional
  │                           20ms timeout / circuit breaker
  │
  ├── Bounded Queue          Dead letter TTL: 100ms
  │
  ├── Worker Scheduler       Task leasing: 450ms reclaim
  │
  └── Worker Pool (43)       Shared model weights via mmap

What's next

The steel thread is complete. Every architectural pillar is validated. Upcoming work focuses on the pieces the design report flagged as still missing.

Queue scheduling policy — FIFO vs priority, cancellation, aging strategy
Memory tier implementation — hot/warm/cold allocation, eviction, migration
Routing model — variance modeling, real network telemetry, adaptive thresholds
Observability stack — latency histograms, energy dashboards, distributed tracing
Security — authentication, encryption, execution sandboxing