Computational Offloading Strategy Based on Multi-objective Optimization in D2D Network

doi:10.3969/j.issn.1006-2475.2024.01.004

Abstract

Abstract: Abstract： Focused on the high latency and energy consumption for computational offload in mobile edge computing scenarios with device-to-device （D2D） communication technology， a computational offloading strategy based on multi-objective optimization is proposed. The strategy is based on a computing offloading model with multi-objective optimization of delay and energy consumption， introduces the analysis of excessive offloading problem， improves the NSGA-II algorithm， including genetic encoding strategy， crossover and variation methods applicable to computing offloading， and minimizes task execution time and energy consumption by solving the Pareto optimum. In addition， a data routing algorithm is proposed， which balances the transmission energy consumption of routing devices and optimizes the routing paths. Through simulation experiments， the average boosting efficiency of the algorithm is up to 41.7% and the task retransmission rate is reduced to 7.8%. The experiment results show that the proposed algorithm can significantly reduce the execution delay， energy consumption， task retransmission rate and improve the task offload success rate.

Key words: Key words： device-to-device （D2D）, mobile edge computing, multi-objective optimization, Pareto optimization

CLC Number:

TP389. 1

CHEN Qi, LI Jing-jing. Computational Offloading Strategy Based on Multi-objective Optimization in D2D Network[J]. Computer and Modernization, 2024, 0(01): 21-28.

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