Mobile Edge Computing Task Placement Strategy Based on NSGA-II

FANG-QIN XU, ZHI-MIN ZHU

Abstract


With the proliferation of smart terminal devices, the traditional centralized service model has been unable to quickly handle the explosive growth of business data. The maturity of 5G networks has led to the accelerated development of edge computing technology and the emergence of mobile edge computing technology, which can effectively solve the problems of network delay and load balancing in edge computing. This paper first proposes the use of genetic algorithm to solve the task unloading problem in multi-service nodes and mobile terminal tasks in large-scale heterogeneous MECs. Then, combined with the actual use environment of MEC, the algorithm is improved by NSGA-II, and finally through edge calculation simulation. The platform iFogSim and the dataset Google Cluster Trace experiment with task mode issues. The simulation results show that the task unloading strategy based on NSGA-II algorithm has better effect on load balancing and service delay than the original genetic algorithm, and has strong practical application value.

Keywords


Mobile Edge Calculation, Task offload strategy, NSGA-II.Text


DOI
10.12783/dtcse/cmso2019/33605

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