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Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

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Abstract

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

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Acknowledgements

In our experiments, we used the NSGA-II [42], NSGA-III [43], and MOPSO [44] algorithms. Dr. Mostapha Kalami Heris is acknowledged for delivering the implementations.

Funding

This work has been partially supported by the National Key R &D Program of China under Grant No. 2018YFB1800805, the National Natural Science Foundation of China under Grant No. 61772345, 61902257, 61972261, the Pearl River Young Scholars funding of Shenzhen University, and the Shenzhen Science and Technology Program under Grant No. RCYX20200-714114645048, No. JCYJ20190808142207420, and No. GJHZ2-0190822095416463.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518061, Guangdong, China

    Kuanishbay Sadatdiynov, Laizhong Cui & Joshua Zhexue Huang

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  1. Kuanishbay Sadatdiynov
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  2. Laizhong Cui
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Contributions

Kuanishbay Sadatdiynov: Conceptualization, Investigation, Software, Methodology, Writing - original draft. Laizhong Cui: Conceptualization, Project administration, Resources, Validation. Joshua Zhexue Huang: Supervision, Funding acquisition, Methodology, Writing - review & editing.

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Correspondence to Laizhong Cui.

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Sadatdiynov, K., Cui, L. & Huang, J. Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method. Peer-to-Peer Netw. Appl. 16, 657–674 (2023). https://doi.org/10.1007/s12083-022-01435-z

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