A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers

Shikui Zhang; Ce Chi; Kaixuan Ji; Zhiyong Liu; Fa Zhang; Penglei Song; Huimei Yuan; Dehui Qiu; Xiaohua Wan

doi:10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers

Shikui Zhang, Ce Chi, Kaixuan Ji, Zhiyong Liu, Fa Zhang, Penglei Song, Huimei Yuan, Dehui Qiu^*, Xiaohua Wan^*

^*此作品的通讯作者

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

Data center, as an important infrastructure of cloud computing, is experiencing rapid growth in both quantity and scale, which causes the high energy consumption and severe environmental problems restricting the development of data centers. Task scheduling can significantly improve the energy efficiency in cloud computing and alleviate the constrain of the high stress on environment. But efficient task scheduling in heterogeneous cloud environment is rather challenging because of the dynamic and complicated environment of data centers. In this paper, we propose a new meta-heuristic task scheduling algorithm called WACOA combining the whale optimization algorithm with the ant colony algorithm, which uses pheromones to collect part excellent solutions from historical information to schedule tasks. Experiments show that WACOA is superior to the whale optimization algorithm and ant colony algorithm. WACOA can reduce energy consumption and improve the performance on task scheduling.

源语言	英语
主期刊名	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
出版商	Institute of Electrical and Electronics Engineers Inc.
页	947-954
页数	8
ISBN（电子版）	9781665435741
DOI	https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133
出版状态	已出版 - 2021
已对外发布	是
活动	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, 美国期限: 30 9月 2021 → 3 10月 2021

出版系列

姓名	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

会议

会议	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
国家/地区	美国
市	New York
时期	30/09/21 → 3/10/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, S., Chi, C., Ji, K., Liu, Z., Zhang, F., Song, P., Yuan, H., Qiu, D., & Wan, X. (2021). A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers. 在 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 (页码 947-954). (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

Zhang, Shikui ; Chi, Ce ; Ji, Kaixuan 等. / A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 947-954 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

@inproceedings{14ec82a802a440cdaa11695cf39b9ac3,

title = "A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers",

abstract = "Data center, as an important infrastructure of cloud computing, is experiencing rapid growth in both quantity and scale, which causes the high energy consumption and severe environmental problems restricting the development of data centers. Task scheduling can significantly improve the energy efficiency in cloud computing and alleviate the constrain of the high stress on environment. But efficient task scheduling in heterogeneous cloud environment is rather challenging because of the dynamic and complicated environment of data centers. In this paper, we propose a new meta-heuristic task scheduling algorithm called WACOA combining the whale optimization algorithm with the ant colony algorithm, which uses pheromones to collect part excellent solutions from historical information to schedule tasks. Experiments show that WACOA is superior to the whale optimization algorithm and ant colony algorithm. WACOA can reduce energy consumption and improve the performance on task scheduling.",

keywords = "Ant colony algorithm, Data center energy consumption, Independent task scheduling, Multi-objective optimization, Whale optimization algorithm",

author = "Shikui Zhang and Ce Chi and Kaixuan Ji and Zhiyong Liu and Fa Zhang and Penglei Song and Huimei Yuan and Dehui Qiu and Xiaohua Wan",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 ; Conference date: 30-09-2021 Through 03-10-2021",

year = "2021",

doi = "10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133",

language = "English",

series = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

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

pages = "947--954",

booktitle = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

address = "United States",

}

Zhang, S, Chi, C, Ji, K, Liu, Z, Zhang, F, Song, P, Yuan, H, Qiu, D & Wan, X 2021, A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers. 在 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, Institute of Electrical and Electronics Engineers Inc., 页码 947-954, 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, New York, 美国, 30/09/21. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers. / Zhang, Shikui; Chi, Ce; Ji, Kaixuan 等.
19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 947-954 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers

AU - Zhang, Shikui

AU - Chi, Ce

AU - Ji, Kaixuan

AU - Liu, Zhiyong

AU - Zhang, Fa

AU - Song, Penglei

AU - Yuan, Huimei

AU - Qiu, Dehui

AU - Wan, Xiaohua

PY - 2021

Y1 - 2021

N2 - Data center, as an important infrastructure of cloud computing, is experiencing rapid growth in both quantity and scale, which causes the high energy consumption and severe environmental problems restricting the development of data centers. Task scheduling can significantly improve the energy efficiency in cloud computing and alleviate the constrain of the high stress on environment. But efficient task scheduling in heterogeneous cloud environment is rather challenging because of the dynamic and complicated environment of data centers. In this paper, we propose a new meta-heuristic task scheduling algorithm called WACOA combining the whale optimization algorithm with the ant colony algorithm, which uses pheromones to collect part excellent solutions from historical information to schedule tasks. Experiments show that WACOA is superior to the whale optimization algorithm and ant colony algorithm. WACOA can reduce energy consumption and improve the performance on task scheduling.

AB - Data center, as an important infrastructure of cloud computing, is experiencing rapid growth in both quantity and scale, which causes the high energy consumption and severe environmental problems restricting the development of data centers. Task scheduling can significantly improve the energy efficiency in cloud computing and alleviate the constrain of the high stress on environment. But efficient task scheduling in heterogeneous cloud environment is rather challenging because of the dynamic and complicated environment of data centers. In this paper, we propose a new meta-heuristic task scheduling algorithm called WACOA combining the whale optimization algorithm with the ant colony algorithm, which uses pheromones to collect part excellent solutions from historical information to schedule tasks. Experiments show that WACOA is superior to the whale optimization algorithm and ant colony algorithm. WACOA can reduce energy consumption and improve the performance on task scheduling.

KW - Ant colony algorithm

KW - Data center energy consumption

KW - Independent task scheduling

KW - Multi-objective optimization

KW - Whale optimization algorithm

UR - http://www.scopus.com/inward/record.url?scp=85124145598&partnerID=8YFLogxK

U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

M3 - Conference contribution

AN - SCOPUS:85124145598

T3 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

SP - 947

EP - 954

BT - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Y2 - 30 September 2021 through 3 October 2021

ER -

Zhang S, Chi C, Ji K, Liu Z, Zhang F, Song P 等. A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers. 在 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 页码 947-954. (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00133

A new meta-heuristic task scheduling algorithm for optimizing energy efficiency in data centers

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此