Providing fairer resource allocation for multi-tenant cloud-based systems

Ru, Jia, Grundy, John, Yang, Yun, Keung, Jacky and Hao, Li 2015, Providing fairer resource allocation for multi-tenant cloud-based systems, in CloudCom 2015: Proceedings of the IEEE Cloud Computing Technology and Science 2015 International Conference, IEEE, Piscataway, N.J., pp. 306-313, doi: 10.1109/CloudCom.2015.30.

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Title Providing fairer resource allocation for multi-tenant cloud-based systems
Author(s) Ru, Jia
Grundy, JohnORCID iD for Grundy, John
Yang, Yun
Keung, Jacky
Hao, Li
Conference name IEEE Cloud Computing Technology and Science. International Conference (7th: 2015: Vancouver, B.C.)
Conference location Vancouver, B.C.
Conference dates 30 Nov. - 3 Dec. 2015
Title of proceedings CloudCom 2015: Proceedings of the IEEE Cloud Computing Technology and Science 2015 International Conference
Editor(s) [Unknown]
Publication date 2015
Conference series IEEE Cloud Computing Technology and Science International Conference
Start page 306
End page 313
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Cloud-computing
Summary A fundamental premise in cloud computing is trying to provide a more sophisticated computing resource sharing capability. In order to provide better allocation, the Dominant Resource Fairness (DRF) approach has been developed to address the "fair resource allocation problem" at the application layer for multi-tenant cloud applications. Nevertheless conventional DRF only considers the interplay of CPU and memory, which may result in over allocation of resources to one tenant's application to the detriment of others. In this paper, we propose an improved DRF algorithm with 3-dimensional demand vector to support disk resources as the third dominant shared resource, enhancing fairer resource sharing. Our technique is integrated with LINUX 'group' controls resource utilisation and realises data isolation to avoid undesirable interactions between co-located tasks. Our method ensures all tenants receive system resources fairly, which improves overall utilisation and throughput as well as reducing traffic in an over-crowded system. We evaluate the performance of different types of workload using different algorithms and compare ours to the default algorithm. Results show an increase of 15% resource utilisation and a reduction of 59% completion time on average, indicating that our DRF algorithm provides a better, smoother, fairer high-performance resource allocation scheme for both continuous workloads and batch jobs.
ISBN 9781467395601
Language eng
DOI 10.1109/CloudCom.2015.30
Field of Research 080309 Software Engineering
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
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