Energy efficient workflow job scheduling for green cloud

Fei Cao, Michelle Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

13 Citations (Scopus)

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

Fingerprint

Job Scheduling
Energy Efficient
Work Flow
Energy utilization
Scheduling
Energy Consumption
Resources
Data Center
Cloud computing
Scheduling algorithms
Minimise
Scientific Workflow
Globe
Performance Bounds
Dynamic Algorithms
Resource Sharing
Directed Acyclic Graph
Profitability
Energy conservation
Quality of service

Keywords

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

Cite this

Cao, F., & Zhu, 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
Cao, Fei ; Zhu, Michelle. / Energy efficient workflow job scheduling for green cloud. Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. IEEE Computer Society, 2013. pp. 2218-2221 (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013).
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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.",
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Cao, F & Zhu, 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., 6651134, Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013, IEEE Computer Society, pp. 2218-2221, 2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013, Boston, MA, Japan, 22/07/13. https://doi.org/10.1109/IPDPSW.2013.19

Energy efficient workflow job scheduling for green cloud. / Cao, Fei; Zhu, Michelle.

Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. IEEE Computer Society, 2013. p. 2218-2221 6651134 (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AB - 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.

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