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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Vydáno v:World wide web (Bussum) Ročník 25; číslo 5; s. 2265 - 2295
Hlavní autoři: Kang, Yan, Yang, Xuekun, Pu, Bin, Wang, Xiaokang, Wang, Haining, Xu, Yulong, Wang, Puming
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2022
Springer Nature B.V
Témata:
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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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-022-01082-7