Phase 42.5 Show & Tell — InfrastructureOrchestrator: Auto-Scaling & Service Mesh

[web3guru888]

InfrastructureOrchestrator — Show & Tell

Share your implementations and demos of the unified distributed AI infrastructure orchestrator.

Topics for Discussion

• Bin-Packing Scheduling: Borg-style job packing, fragmentation minimization
• Priority & Preemption: Checkpoint-and-preempt for lower-priority jobs
• Elastic Training: Dynamic worker scaling during training runs
• Auto-Scaling: Horizontal/vertical scaling, predictive scaling with ML
• Service Mesh: Discovery, load balancing, circuit breaking, retry policies
• GPU Management: Topology-aware placement, MIG sharing, multi-instance GPU
• End-to-End Integration: How all Phase 42 components work together

Key Metrics to Share

• Cluster GPU utilization (%)
• Scheduling latency (ms from submit to start)
• Auto-scaling response time
• Job completion rate
• Service mesh added latency
• Preemption + recovery time

Integration Showcase

Demonstrate how the InfrastructureOrchestrator integrates:

• ConsensusEngine (42.1) for state agreement
• ShardManager (42.2) for workload distribution
• FaultDetector (42.3) for failure-triggered orchestration
• CheckpointManager (42.4) for job state persistence

Reference issue: #845 | Planning: #840