An introduction to stochastic bin packing-based server consolidation with conflicts

John Martinovic (), Markus Hähnel (), Guntram Scheithauer () and Waltenegus Dargie ()
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John Martinovic: Technische Universität Dresden
Markus Hähnel: Technische Universität Dresden
Guntram Scheithauer: Technische Universität Dresden
Waltenegus Dargie: Technische Universität Dresden

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2022, vol. 30, issue 2, No 4, 296-331

Abstract: Abstract The energy consumption of large-scale data centers or server clusters is expected to grow significantly in the next couple of years contributing to up to 13% of the worldwide energy demand in 2030. As the involved processing units require a disproportional amount of energy when they are idle, underutilized, or overloaded, balancing the supply of and the demand for computing resources is a key issue to obtain energy-efficient server consolidations. Whereas traditional concepts mostly consider deterministic predictions of the future workloads or only aim at finding approximate solutions, in this article, we propose an exact approach to tackle the problem of assigning jobs with (not necessarily independent) stochastic characteristics to a minimal amount of servers subject to further practically relevant constraints. As a main contribution, the problem under consideration is reformulated as a stochastic bin packing problem with conflicts and modeled by an integer linear program. Finally, this new approach is tested on real-world instances obtained from a Google data center.

Keywords: Cutting and packing; Server consolidation; Bin packing problem; Normal distribution; 90C10; 90C90; 90B36; 68M07 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11750-021-00613-1

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