The Challenge of Cloud Resource Optimization

Cloud resource allocation often suffers from a trade-off between over-provisioning (which wastes budget) and under-provisioning (which risks application performance). Traditional predictive models often output point estimates that lack uncertainty quantification, making it difficult for operators to trust automated scaling decisions. The Right-sizing Recommendations (RSR) framework addresses this by applying conformal prediction to cloud workload forecasting, providing a mathematically grounded way to quantify uncertainty in resource demand.

Conformal Prediction for Reliable Scaling

Unlike standard regression models that provide a single predicted value for CPU or memory usage, the RSR framework generates prediction intervals. By leveraging conformal prediction, the system guarantees that the true resource demand will fall within the recommended range with a user-defined confidence level (e.g., 95%). This approach allows data center operators to:

• Quantify Risk: Explicitly define the acceptable probability of resource exhaustion.
• Adapt to Volatility: Automatically widen or narrow the recommended resource bounds based on the historical variance and unpredictability of specific virtual machine workloads.
• Improve Efficiency: Reduce the 'safety buffer' typically added by human operators, as the model provides a statistically valid bound rather than a heuristic guess.

Practical Implementation in Data Centers

The RSR framework is designed for integration into existing data center management pipelines. By treating resource right-sizing as a set-valued prediction problem, it ensures that recommendations remain valid even under non-stationary workload patterns—a common issue in cloud environments where application behavior shifts over time. The framework provides a robust mechanism for automated decision-making, moving away from static thresholds toward dynamic, uncertainty-aware scaling that aligns with actual operational requirements.