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ML for Cloud Efficiency Optimization

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Problem

Scenario

You are working on a platform that runs large fleets of virtual machines and containers in the cloud. Capacity is often overprovisioned to avoid incidents, but that drives up cost and lowers hardware utilization. You want to use machine learning to predict demand and recommend actions such as right-sizing, autoscaling, or workload placement.

Question

How would you optimize cloud infrastructure efficiency using machine learning?

Representative Dataset

Size·14 months, 62K Azure VMs and AKS nodes, 410M 5-minute intervalsTarget·Next 60-minute CPU and memory demand, plus saturation-event riskFeatures·Telemetry, lags, rolling stats, SKU, region, deployment and autoscale metadataChallenge·Strong temporal dependence, skewed demand, rare saturation events