Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287503 
Year of Publication: 
2021
Citation: 
[Journal:] TOP [ISSN:] 1863-8279 [Volume:] 30 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 296-331
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Cutting and packing
Server consolidation
Bin packing problem
Normal distribution
JEL: 
C10
C90
B36
M07
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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