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Authors: Somayeh Abdi ; Mohammad Ashjaei and Saad Mubeen

Affiliation: Department of Networked and Embedded Systems, Mälardalen University, Västerås, Sweden

Keyword(s): Task Offloading, Edge-Cloud Computing Continuum, Reinforcement Learning, q-Learning Algorithm.

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.

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Paper citation in several formats:
Abdi, S.; Ashjaei, M. and Mubeen, S. (2024). Task Offloading in Edge-Cloud Computing Using a Q-Learning Algorithm. In Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-701-6; ISSN 2184-5042, SciTePress, pages 159-166. DOI: 10.5220/0012590800003711

@conference{closer24,
author={Somayeh Abdi. and Mohammad Ashjaei. and Saad Mubeen.},
title={Task Offloading in Edge-Cloud Computing Using a Q-Learning Algorithm},
booktitle={Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER},
year={2024},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012590800003711},
isbn={978-989-758-701-6},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Cloud Computing and Services Science - CLOSER
TI - Task Offloading in Edge-Cloud Computing Using a Q-Learning Algorithm
SN - 978-989-758-701-6
IS - 2184-5042
AU - Abdi, S.
AU - Ashjaei, M.
AU - Mubeen, S.
PY - 2024
SP - 159
EP - 166
DO - 10.5220/0012590800003711
PB - SciTePress

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