Home
Author Guide
Editor Guide
Reviewer Guide
Published Issues
Special Issue
Introduction
Special Issues List
Sections and Topics
Sections
Topics
journal menu
Aims and Scope
Editorial Board
Indexing Service
Article Processing Charge
Open Access
Copyright and Licensing
Preservation and Repository Policy
Publication Ethics
Editorial Process
Contact Us
General Information
ISSN:
1798-2340 (Online)
Frequency:
Bimonthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
,
etc
.
Acceptance Rate:
19%
APC:
450 USD
Average Days to Accept:
112 days
Journal Metrics:
Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
Powered by
Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of the
JAIT
Editorial Board.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2023-09-22
All papers published in JAIT Vol. 14, No. 3&4 have been indexed by Scopus.
2023-08-28
Vol. 14, No. 4 has been published online!
2023-08-02
JAIT Vol. 14, No. 2 has been indexed by Scopus.
Home
>
Published Issues
>
2022
>
Volume 13, No. 2, April 2022
>
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.
5-JAIT-3241-Final-Bangladesh&Canada
PREVIOUS PAPER
Multimodal Wearable Sensing for Sport-Related Activity Recognition Using Deep Learning Networks
NEXT PAPER
Step-by-Step Acquisition of Cooperative Behavior in Soccer Task