Double deep Q-network-based task offloading strategy in mobile edge computing

Mingchu Li; Zhi Yang

doi:10.1117/12.3034986

7 August 2024 Double deep Q-network-based task offloading strategy in mobile edge computing

Mingchu Li, Zhi Yang

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 1322418 (2024) https://doi.org/10.1117/12.3034986
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

With the popularization of smartphones, mobile applications and mobile Internet, mobile devices have an increasing demand for real-time and low latency. However, MDs constrained in their computational power and resources cannot entirely dependent on cloud computing for their processing needs. In order to reduce network latency and improve user experience, Mobile Edge Computing has emerged, and the research on computation offloading lays the foundation for the realization of MEC. In this work, in scenarios involving multi-users and multi-edge servers, we adopt the double deep Qnetwork strategy to address the issue of task offloading. Our primary objective is to reduce the total system latency while considering device mobility, task urgency, and the heterogeneous tasks. We extend the DDQN algorithm by adding a prioritized experience reaply mechanism. Experimental results indicate that the improved DDQN method enhances the convergence speed and effectively reduces the task latency relative to other baseline algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingchu Li and Zhi Yang "Double deep Q-network-based task offloading strategy in mobile edge computing", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 1322418 (7 August 2024); https://doi.org/10.1117/12.3034986

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available