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JAIT 2022 Vol.13(2): 139-146
doi: 10.12720/jait.13.2.139-146

Reinforcement Learning Based Offloading Framework for Computation Service in the Edge Cloud and Core Cloud

Nasif Muslim 1, Salekul Islam 1, and Jean-Charles Grégoire 2
1. United International University Dhaka, Bangladesh
2. INRS-EMT, Montréal, Canada

Abstract—Smartphones have increasingly become indispensable tools of our everyday lives, with extensive applications beyond communications, from utilitarian to entertaining. As such, demands on the technology remain stringent, leading to a limitation in performance and battery lifetime, requiring approaches such as computation offloading to improve the user experience for computation-intensive tasks such as gaming. This work presents the use of Reinforcement Learning in an offloading framework that provides smartphones with the ability to decide whether to perform computations on the smartphone or on the remote Cloud (Edge and Core) to minimize process. Several scenarios have been used to produce simulations that demonstrate that the proposed algorithm can operate efficiently in a dynamic Cloud computing and networking environment.
Index Terms—reinforcement learning, cloud computing, computation offloading

Cite: Nasif Muslim, Salekul Islam, and Jean-Charles Grégoire, "Reinforcement Learning Based Offloading Framework for Computation Service in the Edge Cloud and Core Cloud," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 139-146, April 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.