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

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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.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61762092); the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province (Grant No. 2020SE303); the Major Science and Technology Project of Precious Metal Materials Genome Engineering in Yunnan Province (Grant No. 2019ZE001-1 and 202002AB080001-6); Yunnan provincial major science and technology: Research and Application of key Technologies for Resource Sharing and Collaboration Toward Smart Tourism (Grant No. 202002AD080047).

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  1. Yan Kang and Bin Pu equal contributed to this work.

Authors and Affiliations

  1. Key Laboratory in Software Engineering of Yunnan Province, National Pilot School of Software, Yunnan University, Kunming, 650500, China

    Yan Kang, Xuekun Yang, Haining Wang, Yulong Xu & Puming Wang

  2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410006, China

    Bin Pu

  3. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Xiaokang Wang

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  1. Yan Kang
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This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

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Appendices

Appendix: A

Details of data ECTD1 and ECTD2 are shown in Tables 7 and 8.

Table 7 The details of ECTD1
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Table 8 The details of ECTD2
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Kang, Y., Yang, X., Pu, B. et al. HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system. World Wide Web 25, 2265–2295 (2022). https://doi.org/10.1007/s11280-022-01082-7

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