HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue...

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Veröffentlicht in:	World wide web (Bussum) Jg. 25; H. 5; S. 2265 - 2295
Hauptverfasser:	Kang, Yan, Yang, Xuekun, Pu, Bin, Wang, Xiaokang, Wang, Haining, Xu, Yulong, Wang, Puming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2022 Springer Nature B.V
Schlagworte:	Algorithms Cloud computing Computer Science Data centers Database Management Edge computing Entrapment Hybrid systems Information Systems Applications (incl.Internet) Mobile and IoT Applications Multiple objective analysis Operating Systems Optimization Optimization algorithms Performance enhancement Special Issue on Resource Management at the Edge for Future Web Multi-objective optimization Scheduling Edge cloud computing system Whale optimization algorithm Task selection strategy
ISSN:	1386-145X, 1573-1413
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Abstract	Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue to increase, the problem of how to optimize task selection based on execution time with limited computing resources becomes a pressing one. To this end, a hybrid whale optimization algorithm (HWOA) is proposed for multi-objective edge computing task selection. In addition to the execution time of the task, economic profits are also considered to optimize task selection. Specifically, a fuzzy function is designed to address the uncertainty of task’s economic profits and execution time. Five interactive constraints among tasks are presented and formulated to improve the performance of task selection. Furthermore, some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment. Finally, an extensive experimental assessment of synthetic datasets is implemented to evaluate the multi-objective optimization performance. Compared with the traditional WOA, the diversity metric (Δ-spread), the hypervolume (HV) and other evaluation metrics are significantly improved. The experiment results also indicate the proposed approach achieves remarkable performance compared with other competitive methods.
AbstractList	Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue to increase, the problem of how to optimize task selection based on execution time with limited computing resources becomes a pressing one. To this end, a hybrid whale optimization algorithm (HWOA) is proposed for multi-objective edge computing task selection. In addition to the execution time of the task, economic profits are also considered to optimize task selection. Specifically, a fuzzy function is designed to address the uncertainty of task’s economic profits and execution time. Five interactive constraints among tasks are presented and formulated to improve the performance of task selection. Furthermore, some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment. Finally, an extensive experimental assessment of synthetic datasets is implemented to evaluate the multi-objective optimization performance. Compared with the traditional WOA, the diversity metric (Δ-spread), the hypervolume (HV) and other evaluation metrics are significantly improved. The experiment results also indicate the proposed approach achieves remarkable performance compared with other competitive methods.
Author	Wang, Xiaokang Wang, Puming Yang, Xuekun Kang, Yan Xu, Yulong Pu, Bin Wang, Haining
Author_xml	– sequence: 1 givenname: Yan surname: Kang fullname: Kang, Yan organization: Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University – sequence: 2 givenname: Xuekun surname: Yang fullname: Yang, Xuekun organization: Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University – sequence: 3 givenname: Bin surname: Pu fullname: Pu, Bin organization: College of Computer Science and Electronic Engineering, Hunan University – sequence: 4 givenname: Xiaokang surname: Wang fullname: Wang, Xiaokang email: xkwang@hainanu.edu.cn organization: School of Computer Science and Technology, Hainan University – sequence: 5 givenname: Haining surname: Wang fullname: Wang, Haining organization: Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University – sequence: 6 givenname: Yulong surname: Xu fullname: Xu, Yulong organization: Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University – sequence: 7 givenname: Puming surname: Wang fullname: Wang, Puming organization: Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University
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SubjectTerms	Algorithms Cloud computing Computer Science Data centers Database Management Edge computing Entrapment Hybrid systems Information Systems Applications (incl.Internet) Mobile and IoT Applications Multiple objective analysis Operating Systems Optimization Optimization algorithms Performance enhancement Special Issue on Resource Management at the Edge for Future Web
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Title	HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system
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