Improving resource elasticity in cloud computing thanks to model-free control

TL;DR

This paper introduces a model-free control approach for dynamic resource management in cloud computing, demonstrating superior performance over traditional auto-scaling methods during workload fluctuations, validated through AWS experiments.

Contribution

It applies model-free control to cloud resource elasticity, offering a simple, effective alternative to existing auto-scaling algorithms with improved adaptability.

Findings

Outperforms commercial auto-scaling algorithms during workload spikes

Easily implementable approach validated on AWS

Provides better resource adaptation in dynamic cloud environments

Abstract

In cloud computing management, the dynamic adaptation of computing resource allocations under time-varying workload is an active domain of investigation. Several control strategies were already proposed. Here the model-free control setting and the corresponding "intelligent" controllers, which are most successful in many concrete engineering situations, are employed for the "horizontal elasticity." When compared to the commercial "Auto-Scaling" algorithms, our easily implementable approach, behaves better even with sharp workload fluctuations. This is confirmed by experiments on Amazon Web Services (AWS).