Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79632
Title: Long-term resource fairness : towards economic fairness on pay-as-you-use computing systems
Authors: Tang, Shanjiang
Lee, Bu-Sung
He, Bingsheng
Liu, Haikun
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2014
Source: Tang, S., Lee, B.- S., He, B. & Liu, H. (2014). Long-Term Resource Fairness: Towards Economic Fairness on Pay-as-you-use Computing Systems. Proceedings of the 28th ACM international conference on Supercomputing, 251-260.
Conference: The 28th ACM international conference on Supercomputing
Abstract: Fair resource allocation is a key building block of any shared computing system. However, MemoryLess Resource Fairness (MLRF), widely used in many existing frameworks such as YARN, Mesos and Dryad, is not suitable for pay-as-you-use computing. To address this problem, this paper proposes Long-Term Resource Fairness (LTRF), a novel fair resource allocation mechanism. We show that LTRF satisfies several highly desirable properties. First, LTRF incentivizes clients to share resources via group-buying by ensuring that no client is better off in a computing system that she buys and uses individually. Second, LTRF incentivizes clients to submit non-trivial workloads and be willing to yield unneeded resources to others. Third, LTRF has a resource-as-you-pay fairness property, which ensures the amount of resources that each client should get according to her monetary cost, despite that her resource demand varies over time. Finally, LTRF is strategy-proof, since it can make sure that a client cannot get more resources by lying about her demand. We have implemented LTRF in YARN by developing LTYARN, a long-term YARN fair scheduler, and shown that it leads to a better resource fairness than other state-of-the-art fair schedulers.
URI: https://hdl.handle.net/10356/79632
http://hdl.handle.net/10220/20381
DOI: 10.1145/2597652.2597672
Schools: School of Computer Engineering 
Rights: © 2014 Association for Computing Machinery. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the 28th ACM international conference on Supercomputing, Association for Computing Machinery. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2597652.2597672].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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