A Large Neighborhood Search approach for the Machine Reassignment Problem in data centers

Souza, Filipe; Grimes, Diarmuid; O'Sullivan, Barry

A Large Neighborhood Search approach for the Machine Reassignment Problem in data centers

dc.contributor.author	Souza, Filipe
dc.contributor.author	Grimes, Diarmuid
dc.contributor.author	O'Sullivan, Barry
dc.contributor.editor	Longo, L.
dc.contributor.editor	O'Reilly, R.
dc.contributor.funder	Science Foundation Ireland	en
dc.contributor.funder	European Regional Development Fund	en
dc.date.accessioned	2023-01-11T11:45:34Z
dc.date.available	2023-01-11T11:45:34Z
dc.date.issued	2022-12-08
dc.date.updated	2023-01-11T11:38:52Z
dc.description.abstract	One of the main challenges in data centre operations involves optimally reassigning running processes to servers in a dynamic setting such that operational performance is improved. In 2012, Google proposed the Machine Reassignment Problem in collaboration with the ROADEF/Euro challenge. A number of complex instances were generated for evaluating the submissions. This work focuses on new approaches to solve this problem. In particular, we propose a Large Neighbourhood Search approach with a novel, domain-specific heuristic for neighborhood selection. This heuris tic uses the unbalanced resource usage on the machines to select the most promising processes in each iteration. Furthermore, we compare two search strategies to optimise the sub-problems. The first one is based on the concept of Limited Discrepancy Search, albeit tailored to large scale problems; and the second approach involves the standard combination of constraint programming with random restart strategies. An empirical evaluation on the widely studied instances from ROADEF 2012 demonstrates the effectiveness of our approach against the state-of the-art, with new upper bounds found for three instances.	en
dc.description.sponsorship	Science Foundation Ireland (Supported by SFI Centre for Research Training in Artificial Intelligence under Grant No. 18/CRT/6223 and SFI under Grant No. 12/RC/2289-P2, co-funded under the European Regional Development Fund)	en
dc.description.status	Peer reviewed	en
dc.description.version	Accepted Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Souza, F., Grimes, D. and O’Sullivan, B. (2023) ‘A large neighborhood search approach for the data centre machine reassignment problem’, AICS2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science. Cham: Springer Nature Switzerland, pp. 397–408. https://doi.org/10.1007/978-3-031-26438-2_3	en
dc.identifier.doi	10.1007/978-3-031-26438-2_31
dc.identifier.endpage	408	en
dc.identifier.isbn	978-3-031-26438-2
dc.identifier.isbn	978-3-031-26437-5
dc.identifier.startpage	397	en
dc.identifier.uri	https://hdl.handle.net/10468/14038
dc.language.iso	en	en
dc.publisher	Springer Cham	en
dc.relation.project	info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/	en
dc.rights	© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made	en
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en
dc.subject	LNS	en
dc.subject	Neighbourhood selection	en
dc.subject	Machine reassignment problem	en
dc.subject	Limited discrepancy search	en
dc.title	A Large Neighborhood Search approach for the Machine Reassignment Problem in data centers	en
dc.type	Conference item	en