Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79632
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTang, Shanjiangen
dc.contributor.authorLee, Bu-Sungen
dc.contributor.authorHe, Bingshengen
dc.contributor.authorLiu, Haikunen
dc.date.accessioned2014-08-22T08:14:43Zen
dc.date.accessioned2019-12-06T13:29:45Z-
dc.date.available2014-08-22T08:14:43Zen
dc.date.available2019-12-06T13:29:45Z-
dc.date.copyright2014en
dc.date.issued2014en
dc.identifier.citationTang, 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.en
dc.identifier.urihttps://hdl.handle.net/10356/79632-
dc.description.abstractFair 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.en
dc.language.isoenen
dc.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].en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleLong-term resource fairness : towards economic fairness on pay-as-you-use computing systemsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceThe 28th ACM international conference on Supercomputingen
dc.identifier.doi10.1145/2597652.2597672en
dc.description.versionAccepted versionen
dc.identifier.rims179537en
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:SCSE Conference Papers

SCOPUSTM   
Citations 20

26
Updated on Jul 19, 2024

Web of ScienceTM
Citations 20

16
Updated on Oct 25, 2023

Page view(s) 10

913
Updated on Jul 23, 2024

Download(s) 5

513
Updated on Jul 23, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.