Thermal-aware Workload Distribution for Data Centers with Demand Variations

TL;DR

This paper presents a thermal-aware workload distribution method for data centers that considers operational costs, server reliability, and demand variations, using a nonlinear optimization approach combined with heuristics and Model Predictive Control.

Contribution

It introduces a novel nonlinear optimization model that incorporates workload transition costs and proposes heuristics and MPC for practical workload reconfiguration.

Findings

Proposed schemes effectively reduce operational costs.

Heuristics provide good approximations to the nonlinear problem.

MPC enables adaptive workload management under demand uncertainty.

Abstract

Thermal-aware workload distribution is a common approach in the literature for power consumption optimization in data centers. However, data centers also have other operational costs such as the cost of equipment maintenance and replacement. It has been shown that server reliability depends on frequency of their temperature variations, arising from workload transitions due to dynamic demands. In this work, we formulate a nonlinear optimization problem that considers the cost of workload transitions in addition to IT and cooling power consumption. To approximate the solution, we first linearize the problem; the result is a mixed integer programming problem. A modified heuristic is then proposed to approximate the solution of the linear problem. Finally, a Model Predictive Control (MPC) approach is integrated with the proposed heuristics for automatic workload reconfiguration when future demand is not known exactly, but predictions are available. Numerical results show that the proposed schemes are attractive in different settings.