Semi-online task assignment policies for workload consolidation in cloud computing systems
| dc.contributor.author | Armant, Vincent | |
| dc.contributor.author | De Cauwer, Milan | |
| dc.contributor.author | Brown, Kenneth N. | |
| dc.contributor.author | O'Sullivan, Barry | |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.date.accessioned | 2018-01-11T11:22:42Z | |
| dc.date.available | 2018-01-11T11:22:42Z | |
| dc.date.issued | 2018-01-03 | |
| dc.date.updated | 2018-01-11T10:18:34Z | |
| dc.description.abstract | Satisfying on-demand access to cloud computing infrastructures under quality-of-service constraints while minimising the wastage of resources is an important challenge in data centre resource management. In this paper we tackle this challenge in a semi-online workload management system allocating tasks with uncertain duration to physical servers. Our semi-online framework, based on a bin packing approach, allows us to gather information on incoming tasks during a short time window before deciding on their assignments. Our contributions are as follows: (i) we propose a formal framework capturing the semi-online consolidation problem; (ii) we propose a new dynamic and real-time allocation algorithm based on the incremental merging of bins; and (iii) an adaptation of standard bin packing heuristics with a local search algorithm for the semi-online context considered here. We provide a systematic study of the impact of varying time-period size and varying the degrees of uncertainty on the duration of incoming tasks. The policies are compared in terms of solution quality and solving time on a data-set extracted from a real-world cluster trace. Our results show that, around periods of high demand, our best policy saves up to 40% of the resources compared to the other polices, and is robust to uncertainty in the task durations. Finally, we show that small increases in the allowable time window allows a significant improvement, but that larger time windows do not necessarily improve resource usage for real world data sets. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Armant, V., De Cauwer, M. Brown, K. N. and O'Sullivan, B. (2018) 'Semi-online task assignment policies for workload consolidation in cloud computing systems', Future Generation Computer Systems, 82, pp. 89-103. doi:10.1016/j.future.2017.12.035 | en |
| dc.identifier.doi | 10.1016/j.future.2017.12.035 | |
| dc.identifier.endpage | 103 | |
| dc.identifier.issn | 0167-739X | |
| dc.identifier.journaltitle | Future Generation Computer Systems | en |
| dc.identifier.startpage | 89 | |
| dc.identifier.uri | https://hdl.handle.net/10468/5268 | |
| dc.identifier.volume | 82 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier B.V. | 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 | © 2018, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | Cloud computing | en |
| dc.subject | Workload consolidation | en |
| dc.subject | Semi-online policies | en |
| dc.subject | Stochastic task duration | en |
| dc.title | Semi-online task assignment policies for workload consolidation in cloud computing systems | en |
| dc.type | Article (peer-reviewed) | en |
