Delay Optimization in Mobile Edge Computing Federation using Task Offloading and Resource Allocation

In recent years, computation offloading has become an effective solution for limited resource issues. This article investigates decision offloading and computing resource allocation. We propose a task offloading framework to minimize the total delay considering the resource condition in the MEC fede...

Full description

Saved in:
Bibliographic Details
Published in2022 13th International Conference on Information and Communication Technology Convergence (ICTC) pp. 767 - 770
Main Authors Do, Huong Mai, Yoo, Myungsik
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.10.2022
Subjects
deep reinforcement learning (DRL)
Delays
Information and communication technology
Markov decision process
Markov processes
MEC federation
Multi-access edge computing
Reinforcement learning
resource allocation
Resource management
Simulation
task offloading
Online AccessGet full text

Cover

More Information
Summary:In recent years, computation offloading has become an effective solution for limited resource issues. This article investigates decision offloading and computing resource allocation. We propose a task offloading framework to minimize the total delay considering the resource condition in the MEC federation system. Then, we also solve this problem by transferring to Markov Decision Process (MDP) and using the Deep Deterministic Policy Gradient (DDPG) framework. Simulation results show that the DDPG can successfully lower the delay of the tasks.
ISSN:2162-1241
DOI:10.1109/ICTC55196.2022.9952817