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Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm

Fulltext:


Authors:

Somayeh Abdi, Mohammad Ashjaei, Saad Mubeen

Research group:

Heterogeneous systems - hardware software co-design

Publication Type:

Conference/Workshop Paper

Venue:

The International Conference on Cloud Computing and Services Science

Abstract

Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacity of fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines. This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximize the number of independent IoT tasks that meet their deadlines and to minimize the deadline violation time of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve the formulated problem. The performance of the proposed algorithms is experimentally evaluated with respect to several algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform well in meeting task deadlines and reducing the total deadline violation time.

Bibtex

@inproceedings{Abdi6911,
author = {Somayeh Abdi and Mohammad Ashjaei and Saad Mubeen},
title = {Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm},
month = {June},
year = {2024},
booktitle = {The International Conference on Cloud Computing and Services Science},
url = {http://www.es.mdu.se/publications/6911-}
}