Infrastructure · Software component
Edge Update Orchestrator
Software componentInfrastructureInfrastructurearc:EdgeUpdateOrchestrator
A fleet-management component that distributes model and configuration updates to edge devices in waves using differential, resumable transfers and device-capability targeting, rolling back failed updates.
Responsibility. Rolls out model updates safely across large, intermittently connected device fleets.
Also known as: Edge fleet manager, OTA model updater, Over-the-air update orchestrator, OTA update orchestrator, Staged rollout
Relationships
is configured by structural
invokes dependency
is invoked by dependency
reads dependency
writes dependency
escalates to dynamic
is triggered by dynamic
receives data from dynamic
sends data to dynamic
orchestrates control
overrides control
requires approval from control
monitors assurance
Design guidance
- SHOULD roll out updates in waves (e.g., 1%, 10%, 50%, 100%) to detect issues before fleet-wide deployment.
- SHOULD send binary diffs rather than full models.
- MUST roll back automatically when updates raise error rates, power consumption or crashes.
- SHOULD stage updates to a small fraction of sites (e.g., 5%) and validate stability before broad rollout.
- MUST automatically halt and roll back when the health-check failure rate exceeds a threshold.
Quantitative guidance
As stated by the sources; verify before use.
- Delta update of ~5 MB vs 50 MB full model when ~10% of weights change (10x bandwidth reduction) (Ch4.3).
- Weekly updates for 500 stores: ~10 hours automated at 50 locations/hour vs 1,000 hours/week manually (Ch4.6).
- Rollback triggered if >5% of locations fail health checks (Ch4.6).
Classification
- Patterns
- Tiered wave rolloutDifferential (delta) updatesResumable downloadsCapability-targeted deploymentAutomated rollbackStaged (canary) rolloutRolling updateAutomatic rollback
- Technologies
- AWS GreengrassAzure IoT EdgeEdge ImpulseWallarooNVIDIA Fleet Command
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Performance efficiency (ISO/IEC 25010)Maintainability (ISO/IEC 25010)
- Risks mitigated
- Partial fleet deploymentsBandwidth exhaustionFleet-wide faulty updateA single bad update disrupting all locations simultaneouslyMulti-day outages from manual edge updates
Sources
- Ch4.3: T. Nguyen, "Container Orchestration and Edge Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.3. ISBN: 9798244538229.
- Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.