Implementation of Algorithms for Right-Sizing Data Centers

TL;DR

This paper explores dynamic algorithms for reducing data center energy use by adjusting server capacity in real-time, demonstrating near-optimal performance and cost savings through empirical evaluation on real-world traces.

Contribution

It implements and evaluates online convex optimization algorithms for data center right-sizing, highlighting their practical effectiveness and potential for further cost reductions with predictive features.

Findings

Algorithms perform close to offline optimal in practice

Significant cost reductions over static provisioning

Performance influenced by data center features and trace characteristics

Abstract

The energy consumption of data centers assumes a significant fraction of the world's overall energy consumption. Most data centers are statically provisioned, leading to a very low average utilization of servers. In this work, we survey uni-dimensional and high-dimensional approaches for dynamically powering up and powering down servers to reduce the energy footprint of data centers while ensuring that incoming jobs are processed in time. We implement algorithms for smoothed online convex optimization and variations thereof where, in each round, the agent receives a convex cost function. The agent seeks to balance minimizing this cost and a movement cost associated with changing decisions in-between rounds. We implement the algorithms in their most general form, inviting future research on their performance in other application areas. We evaluate the algorithms for the application of right-sizing data centers using traces from Facebook, Microsoft, Alibaba, and Los Alamos National Lab. Our experiments show that the online algorithms perform close to the dynamic offline optimum in practice and promise a significant cost reduction compared to a static provisioning of servers. We discuss how features of the data center model and trace impact the performance. Finally, we investigate the practical use of predictions to achieve further cost reductions.