A Hybrid Many-Objective Optimization Algorithm for Task Offloading and Resource Allocation in Multi-Server Mobile Edge Computing Networks

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. Alow-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.

Automated summary

Mobile edge computing is important for improving the quality of service for user task computing, but existing solutions often overlook significant factors. In this study, a trusted many-objective model is built to consider various factors, and an optimization algorithm is designed to address the model. The results show that the proposed algorithm outperforms other advanced algorithms.