A deadline scheduler for jobs in distributed systems

Quentin Perret, Gabriel Charlemagne, Stelios Sotiriadis, Nik Bessis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

16 Citations (Scopus)

Abstract

This study presents a soft deadline scheduler for distributed systems that aims of exploring data locality management. In Hadoop, neither the Fair Scheduler nor the Capacity Scheduler takes care about deadlines defined by the user for a job. Our algorithm, named as Cloud Least Laxity First (CLLF), minimizes the extra-cost implied from tasks that are executed over a cloud setting by ordering each of which using its laxity and locality. By using our deadline scheduling algorithm, we demonstrate prosperous performance, as the number of available nodes needed in a cluster in order to meet all the deadlines is minimized while the total execution time of the job remains in acceptable levels. To achieve this, we compare the ability of our algorithm to meet deadlines with the Time Shared and the Space Shared scheduling algorithms. At last we implement our solution in the CloudSim simulation framework for producing the experimental analysis.

Original languageEnglish
Title of host publicationProceedings - 27th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2013
Pages757-764
Number of pages8
DOIs
Publication statusPublished - 1 Jul 2013
Event27th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2013 - Barcelona, Spain
Duration: 25 Mar 201328 Mar 2013

Publication series

NameProceedings - 27th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2013

Conference

Conference27th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2013
Country/TerritorySpain
CityBarcelona
Period25/03/1328/03/13

Keywords

  • Cloud computing
  • Cluster computing
  • Hadoop
  • Soft deadline scheduling

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