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
N1 - Publisher Copyright:
© 2021 IEEE.
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 -
