Energy-aware job management approaches for workflow in cloud

Mustafa Khaleel, Michelle M. Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The energy consumption of cloud servers has dramatically increased. In order to meet the growing demands of users and reduce the skyrocketing cost of electricity, it is critical to have performance guaranteed and cost-effective job schedulers for clouds. In recent years, there has been a growing body of research which focus on improving resource utilization to improve energy efficiency, system throughput and at the same time meet the Quality of Service (QoS) requirements specified in the Service Level Agreements (SLA). This paper propose a multiple procedure scheduling algorithm which aims to maximize the resource utilization for cloud resources for reduced energy consumption as well as guarantee the execution deadline for cloud jobs modeled as scientific workflows. Our simulation results demonstrate better performance compared with other similar algorithms.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages506-507
Number of pages2
ISBN (Electronic)9781467365987
DOIs
StatePublished - 26 Oct 2015
EventIEEE International Conference on Cluster Computing, CLUSTER 2015 - Chicago, United States
Duration: 8 Sep 201511 Sep 2015

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2015-October
ISSN (Print)1552-5244

Other

OtherIEEE International Conference on Cluster Computing, CLUSTER 2015
CountryUnited States
CityChicago
Period8/09/1511/09/15

Keywords

  • Energy-aware
  • Scientific workflow scheduling
  • Task consolidation

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    Khaleel, M., & Zhu, M. M. (2015). Energy-aware job management approaches for workflow in cloud. In Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015 (pp. 506-507). [7307632] (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2015-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2015.85