A hybrid many-objective optimization algorithm for task offloading and resource allocation in multi-server mobile edge computing networks

Document Type

Journal Article

Publication Title

IEEE Transactions on Services Computing

Volume

16

Issue

5

First Page

3101

Last Page

3114

Publisher

IEEE

School

School of Engineering

RAS ID

62437

Funders

National Key Research and Development Program of China / NSF / US Department of Transportation, Toyota and Amazon

Comments

Zhang, J., Gong, B., Waqas, M., Tu, S., & Han, Z. (2023). A hybrid many-objective optimization algorithm for task offloading and resource allocation in multi-server mobile edge computing networks. IEEE Transactions on Services Computing, 16(5), 3101-3114. https://doi.org/10.1109/TSC.2023.3268990

Abstract

Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. A low-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

DOI

10.1109/TSC.2023.3268990

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