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Project artifact

Low-latency edge processing for connected vehicles

A telecom provider needed sub-10ms processing for vehicle-to-infrastructure communication. Cloud round-trips exceeded latency budgets. We designed an edge compute layer that kept critical decisions local.

LatencyCostReliabilitySecurity
Edge Computing5GReal-Time Systems
Industry
Telecommunications
Timeline
6 months
Executive skim
Three measured signals
Jump to outcomes
Critical-path latency
<8ms P99
Held within budget for safety-critical paths
Saturation behaviour
Controlled degradation
Graceful handling at 150% designed load
Data loss under load
Zero
No events dropped during stress testing
System sketch

Context

Connected vehicle infrastructure required real-time event processing where delays could impact safety-critical decisions.

Constraint

Critical paths could not exceed 8ms latency, and the system had to degrade gracefully under saturation rather than cascading into back-pressure.

Intervention

Implemented an edge compute layer for time-critical decisions, with cloud sync for analytics. Event streaming decoupled ingestion from processing. Added explicit degradation modes and redundancy on critical paths.

Key decisions

  • Edge-first processing for tight latency budgets
  • Event streaming for ingestion/processing decoupling
  • Low-latency state access with Redis
  • Redundancy on critical paths
  • Graceful degradation under overload
  • Real-time latency monitoring

Outcomes

Latency-sensitive paths consistently held under 8ms. System degraded predictably under 150% load without data loss.

Why it matters

Predictable latency enables hard real-time guarantees—essential for safety-critical vehicle communication.

Implementation

Practical technology choices that matched the constraints.

KafkaRedisC++Python5G NRDockerKubernetesPrometheus

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