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

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 ti...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2022-12, Vol.2022 (1), p.1-23, Article 126
Main Authors: Vijayaram, B., Vasudevan, V.
Format: Article
Language:English
Subjects:
Algorithms
Engineering
Hyper-heuristics
Meta-heuristics
Mobile edge computing
Optimization
Pareto efficiency
Quantum Information Technology
Signal,Image and Speech Processing
Spintronics
Task offloading
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Summary: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.
ISSN:1687-6180
1687-6180
DOI:10.1186/s13634-022-00965-1