Energy efficient workflow job scheduling for green cloud

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

14 Scopus citations

Abstract

The elastic resource provision, no interfering resource sharing and flexible customized configuration provided by the Cloud infrastructure has shed light on efficient execution of many scientific applications modeled as Directed Acyclic Graph (DAG) structured workflows. However, the energy cost on running the increasingly deployed Cloud data centers around the globe together with the amount of CO2 emissions have skyrocketed. In order to maintain sustainable Cloud computing facing with ever-increasing problem complexity and big data size, we propose an energy-efficient scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission while satisfying certain Quality of Service (QoS). Our multiple-step resource provision and allocation algorithm applies Dynamic Voltage and Frequency Scaling (DVFS) technology to reduce energy consumption within acceptable performance bounds, and minimize the Virtual Machine (VM) overhead for further reduced energy consumption and higher resource utilization rate. The candidacy of multiple data centers from the energy and performance efficiency perspectives is also evaluated. The simulation results show that our algorithm is able to achieve an average up to 30% of energy savings and increase the resource utilization rate for about 25% leading to higher profit and less CO2 emissions.

Original languageEnglish
Title of host publicationProceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013
PublisherIEEE Computer Society
Pages2218-2221
Number of pages4
ISBN (Print)9780769549798
DOIs
StatePublished - 1 Jan 2013
Event2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013 - Boston, MA, Japan
Duration: 22 Jul 201326 Jul 2013

Publication series

NameProceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013

Conference

Conference2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013
CountryJapan
CityBoston, MA
Period22/07/1326/07/13

Keywords

  • Energy-efficient
  • Green Cloud Computing
  • VM Allocation
  • Workflow Scheduling

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  • Cite this

    Cao, F., & Zhu, M. M. (2013). Energy efficient workflow job scheduling for green cloud. In Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013 (pp. 2218-2221). [6651134] (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013). IEEE Computer Society. https://doi.org/10.1109/IPDPSW.2013.19