Survey Of Distributed Task Offloading Optimization Based on Heuristic Algorithms in Edge Computing

Yuanrun Zheng

doi:10.54097/y1vp2j42

Authors

Yuanrun Zheng

DOI:

https://doi.org/10.54097/y1vp2j42

Keywords:

Edge computing, Task offloading, Heuristic algorithms, Distributed based stations, Optimization strategies.

Abstract

The explosive proliferation of terminal devices has generated massive volumes of data to be processed, exposing the limitations of traditional cloud computing in handling such ever-increasing data loads. The emergence of edge computing enables data processing at edge nodes, significantly improving the efficiency of data transmission and computation. Distributed task offloading based on heuristic algorithms is an important branch in edge computing. This survey provides a systematic review of distributed task offloading based on heuristic algorithms, comprehensively reviewing research on offloading problems empowered by ant colony optimization algorithms, particle swarm optimization algorithms, simulated annealing algorithms, and hybrid heuristic algorithms. The paper examines the effectiveness of task offloading under various algorithmic approaches, while investigating both the advantages of these algorithms and the challenges they face. This paper analyzes the inherent limitations of existing algorithms and identifies three core challenges confronting distributed task offloading empowered by heuristic algorithms. And proposes viable forward-looking optimization solutions addressing current research gaps, with these solutions focusing on interdisciplinary integration and multi-domain collaboration.

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