Resource Allotment Utilizing Multi-Armed Bandit Fostered Reinforcement Learning in Mobile Edge Computing Ecosystems

Priyabrata Nayak, Dipti Dash, Sapthak Mohajon Turjya, Anjan Bandyopadhyay

2024

Abstract

Mobile Edge Computing (MEC) leverages the nearness of computational elements to end-users in wireless networks, pledging low latency and elevated throughput for emerging mobile utilities. Nevertheless, efficient resource allotment remains a notable challenge in MEC ecosystems due to the varying and heterogeneous character of mobile networks. Classic static resource allotment strategies often fail to adjust to varying network conditions, showing suboptimal allotments. In this paper, we propose a novel strategy for resource allotment in MEC ecosystems utilizing a Multi-Armed Bandit (MAB) based Reinforcement Learning (RL) approach. By viewing the resource allotment problem as an MAB problem, our strategy enables the dynamic assignment of resources founded on real-time feedback, thereby enhancing resource usage and user satisfaction. We offer a comprehensive evaluation of our technique through simulations in diverse MEC scenarios, which includes a comprehensive comparison with the round robin task scheduling algorithm to represent the efficacy of our proposed methodology, exhibiting its efficacy in acclimating to changing network conditions and surpassing traditional static allocation procedures. Thereby, our results showcase the prospect of MAB-based RL strategies in improving resource administration in MEC ecosystems, curving the path for better adaptive and productive mobile edge computing applications.

Paper Citation

in Harvard Style

Nayak P., Dash D., Turjya S. and Bandyopadhyay A. (2024). Resource Allotment Utilizing Multi-Armed Bandit Fostered Reinforcement Learning in Mobile Edge Computing Ecosystems. In Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com; ISBN 978-989-758-739-9, SciTePress, pages 106-114. DOI: 10.5220/0013335900004646

in Bibtex Style

@conference{ic3com24,
author={Priyabrata Nayak and Dipti Dash and Sapthak Mohajon Turjya and Anjan Bandyopadhyay},
title={Resource Allotment Utilizing Multi-Armed Bandit Fostered Reinforcement Learning in Mobile Edge Computing Ecosystems},
booktitle={Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com},
year={2024},
pages={106-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013335900004646},
isbn={978-989-758-739-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com
TI - Resource Allotment Utilizing Multi-Armed Bandit Fostered Reinforcement Learning in Mobile Edge Computing Ecosystems
SN - 978-989-758-739-9
AU - Nayak P.
AU - Dash D.
AU - Turjya S.
AU - Bandyopadhyay A.
PY - 2024
SP - 106
EP - 114
DO - 10.5220/0013335900004646
PB - SciTePress