International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 1 January-February 2024 Submit your research before last 3 days of February to publish your research paper in the issue of January-February.

An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

Author(s) Anil Paila
Country India
Abstract Regarding the increasing importance of cloud computing in modern IT architecture, it is crucial to create highly efficient resource allocation algorithms. This work conducts an empirical investigation into the utilisation of several reinforcement learning methods for optimising resource allocation in cloud computing environments. Our objective is to assess the efficiency of RL algorithms in a dynamic environment with fluctuating workloads, focusing on resource utilisation, cost effectiveness, and optimality. This research examines the effects of altering cloud settings in order to integrate theoretical reinforcement learning concepts into a practical resource management system.
In the literature review, we consider the traditional resource allocation techniques and their inability to accommodate the changing demand. We additionally analyse the available research employing machine learning approaches, paying special attention to RL in cloud computing resource distribution. The methodology describes the research design, detailing the employed RL algorithms Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO). We explain the data collection procedure that involves different workloads and also situations to mimic real environments.
Performance of each RL algorithm is presented in the experimental results based on the resource utilization, cost efficiency and also system responsiveness. Q-learning, DQN and PPO are being tested which provides a better understanding of their pros and cons. Discussion that follows the Interprets results of these findings bringing to light many challenges along the way as well as possible directions for future inquiry. Therefore, this research fills in the evolving landscape of cloud computing by demonstrating RL algorithms’ adaptability and effectiveness regarding resource allocation challenges under the dynamic environments.
Keywords Reinforcement Learning, RL Algorithm, Q-Learning, DQN, PPO
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-07
Cite This An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing - Anil Paila - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12845
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.12845
Short DOI https://doi.org/gtg6rd

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