Wireless edge device intelligent task offloading in mobile edge computing using hyper-heuristics

EURASIP Journal on Advances in Signal Processing

Research

B. Vijayaram¹  V. Vasudevan¹ 

[1] Kalasalingam Academy of Research and Education, Kalasalingam University, Krishnankoil, Srivilliputhur, Tamilnadu, India;

关键词: Mobile edge computing;    Hyper-heuristics;    Meta-heuristics;    Task offloading;    Optimization;   

DOI  :  10.1186/s13634-022-00965-1

 received in 2022-08-12, accepted in 2022-12-16,  发布年份 2022

来源: Springer

【 摘 要 】

To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms.

【 授权许可】

CC BY   
© The Author(s) 2022

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