Open Access

ARTICLE

Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

Dayong Wang^1,*, Kamalrulnizam Bin Abu Bakar¹, Babangida Isyaku²

1 Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia
2 Department of Computer Science, Faculty of Information Communication Technology, Sule Lamido University, Jigawa, 741103, Nigeria

* Corresponding Author: Dayong Wang. Email:

(This article belongs to the Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)

Computers, Materials & Continua 2024, 80(2), 2065-2080. https://doi.org/10.32604/cmc.2024.051944

Received 19 March 2024; Accepted 20 June 2024; Issue published 15 August 2024

Abstract

The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot, but lack support for multiple retry in subsequent time slots. It is resulting in TD missing potential offloading opportunities in the future. To fill this gap, we propose a Two-Stage Offloading Decision-making Framework (TSODF) with request holding and dynamic eviction. Long Short-Term Memory (LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot. The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology. Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

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Keywords

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