Online Energy Budgeting for Virtualized Data Centers

Abstract

Increasingly serious concerns about the IT carbon footprints have been pushing data center operators to cap their (brown) consumption. Naturally, achieving capping involves deciding the usage over a long timescale (without foreseeing the far future) and hence, we call this process energy budgeting. The specific goal of this paper is to study budgeting for virtualized data centers from an algorithmic perspective: we develop a provably-efficient online algorithm, called eBud (energy Budgeting), which determines server CPU speed and resource allocation to virtual machines for minimizing the data center operational cost while satisfying the long-term capping constraint in an online fashion. We rigorously prove that eBud achieves a close-to-minimum cost compared to the optimal offline algorithm with future information, while bounding the potential violation of budget constraint, in an almost arbitrarily random environment. We also perform a trace-based simulation study to complement the analysis. The simulation results are consistent with our theoretical analysis and show that eBud reduces the cost by more than 60% (compared to state-of-the-art prediction-based algorithm) while resulting in a zero budget deficit.