Algorithms for Right-sizing Heterogeneous Data Centers

Susanne Albers, Jens Quedenfeld

Research output: Contribution to journalArticlepeer-review


Power consumption is a dominant and still growing cost factor in data centers. In time periods with low load, the energy consumption can be reduced by powering down unused servers. We resort to a model introduced by Lin, Wierman, Andrew, and Thereska [23, 24] that considers data centers with identical machines and generalize it to heterogeneous data centers with d different server types. The operating cost of a server depends on its load and is modeled by an increasing, convex function for each server type. In contrast to earlier work, we consider the discrete setting, where the number of active servers must be integral. Thereby, we seek truly feasible solutions. For homogeneous data centers (d = 1), both the offline and the online problem were solved optimally in References [3, 4]. In this article, we study heterogeneous data centers with general time-dependent operating cost functions. We develop an online algorithm based on a work function approach that achieves a competitive ratio of 2d + 1 + ϵ for any ϵ > 0. For time-independent operating cost functions, the competitive ratio can be reduced to 2d + 1. There is a lower bound of 2d shown in Reference [5], so our algorithm is nearly optimal. For the offline version, we give a graph-based (1 + ϵ)-approximation algorithm. Additionally, our offline algorithm is able to handle time-variable data-center sizes.

Original languageEnglish
Article number20
JournalACM Transactions on Parallel Computing
Issue number4
StatePublished - 14 Dec 2023


  • Heterogeneous machines
  • approximation algorithm
  • competitive analysis
  • discrete setting
  • energy conservation
  • online algorithm


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