What Multi-Layer Correlation Is About
Managing multiple applications across clusters is challenging and error-prone when done manually; sizing at only one layer (app or node) wastes money or causes slowdowns. This approach forecasts the primary workload and its sub-workload demands, then pre-allocates GPU/CPU, memory, and network resources across layers—from application to cluster—so that capacity is in place before demand shifts. By modeling cross-layer relationships, it aligns on-prem and cloud capacity with near-term demand, cutting hidden operating costs and reducing risk.
How Multi-Layer Correlation Works
Observe
Collect time-stamped workload for the primary app and resource usage (GPU/CPU, memory, network) for the app and each sub-app across nodes and clusters.
Forecast
Map the workload forecasts to resource demand with ML models trained on historical patterns, accounting for recency, seasonality, and trends to improve accuracy.
Analyze
Identify each workload and its resource usage, then quantify its correlation with the primary workloads for a holistic view of demand and resource consumption.
Allocate
Prioritize distributing sufficient resources to mission-critical workloads, then pre-allocate capacity across app, node, and cluster layers for Just-in-Time Fitted allocation.
Update
Refresh the forecasting models and workload-to-resource mappings with the latest evaluation results; reweight resource importance and recalibrate per node/cluster.
Figure: Multi-layer correlation applied to GPU and IT/Cloud resource management