Offloading strategy for dependency-aware tasks in MEC system based on deep Q-network

Rui Yuan; Wei Jiang; Jing Hu; Tiecheng Song

doi:10.1117/12.2685767

15 August 2023 Offloading strategy for dependency-aware tasks in MEC system based on deep Q-network

Rui Yuan, Wei Jiang, Jing Hu, Tiecheng Song

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127192H (2023) https://doi.org/10.1117/12.2685767
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

Task offloading strategy is one of the promising methods in mobile edge computing (MEC) to reschedule computing resources. Scheduling for computationally intensive tasks was mainly focused on and the dependency restriction between tasks was generally ignored. In this paper, an End-to-Edge collaborative resource allocation model is established. Firstly, in order to improve the unrealistic assumption raised in previous studies that tasks can be offloaded to MEC servers with arbitrary proportion and order, we establish the directed acyclic graphs to describe the dependency relationship within tasks. And then, system time cost including execution time, transmission delay and waiting latency are considered, and the task offloading problem is transformed into an optimization model to minimize time consumption. To manage the non-convex problem, an offloading strategy under dependency constraints based on deep Q-network (DQN) is proposed. Simulations prove that the proposed algorithm can obtain smaller time cost by comparing with other baseline algorithms.

Citation Download Citation

Rui Yuan, Wei Jiang, Jing Hu, and Tiecheng Song "Offloading strategy for dependency-aware tasks in MEC system based on deep Q-network", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127192H (15 August 2023); https://doi.org/10.1117/12.2685767

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