Energy-aware job management approaches for workflow in cloud

Mustafa Khaleel; Michelle M. Zhu

doi:10.1109/CLUSTER.2015.85

Energy-aware job management approaches for workflow in cloud

Mustafa Khaleel
, Michelle M. Zhu

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	506-507
Number of pages	2
ISBN (Electronic)	9781467365987
DOIs	https://doi.org/10.1109/CLUSTER.2015.85
State	Published - 26 Oct 2015
Event	IEEE International Conference on Cluster Computing, CLUSTER 2015 - Chicago, United States Duration: 8 Sep 2015 → 11 Sep 2015

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2015-October
ISSN (Print)	1552-5244

Other

Other	IEEE International Conference on Cluster Computing, CLUSTER 2015
Country/Territory	United States
City	Chicago
Period	8/09/15 → 11/09/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Energy-aware
Scientific workflow scheduling
Task consolidation

Access to Document

10.1109/CLUSTER.2015.85

Cite this

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). Article 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

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title = "Energy-aware job management approaches for workflow in cloud",

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.",

keywords = "Energy-aware, Scientific workflow scheduling, Task consolidation",

author = "Mustafa Khaleel and Zhu, \{Michelle M.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Cluster Computing, CLUSTER 2015 ; Conference date: 08-09-2015 Through 11-09-2015",

year = "2015",

month = oct,

day = "26",

doi = "10.1109/CLUSTER.2015.85",

language = "English",

series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Khaleel, M & Zhu, MM 2015, Energy-aware job management approaches for workflow in cloud. in Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015., 7307632, Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2015-October, Institute of Electrical and Electronics Engineers Inc., pp. 506-507, IEEE International Conference on Cluster Computing, CLUSTER 2015, Chicago, United States, 8/09/15. https://doi.org/10.1109/CLUSTER.2015.85

Energy-aware job management approaches for workflow in cloud. / Khaleel, Mustafa; Zhu, Michelle M.
Proceedings - 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 506-507 7307632 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2015-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Scientific workflow scheduling

KW - Task consolidation

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ER -

Energy-aware job management approaches for workflow in cloud

Abstract

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UN SDGs

Keywords

Access to Document

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