Task reverse offloading with deep reinforcement learning in multi-access edge computing

Saeed, Mamoon M. and Saeed, Rashid A. and Mokhtar, Rania A. and Khalifa, Othman Omran and Ahmed, Zeinab E. and Barakat, Mohammed and Elnaim, Areeg Ali (2023) Task reverse offloading with deep reinforcement learning in multi-access edge computing. In: 2023 9th International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia.

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Abstract

The Multi-access Edge Computing (MEC) technology’s quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques.

Item Type:	Proceeding Paper (Keynote)
Uncontrolled Keywords:	Multi-access Edge Computing, Mobile Edge, Reverse Offloading, Deep Reinforcement Learning
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	04 Oct 2023 09:20
Last Modified:	02 Feb 2024 16:43
URI:	http://irep.iium.edu.my/id/eprint/107253

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