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Management of Optimal Resource Allocation in the Cloud

by Manoj Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 25
Year of Publication: 2023
Authors: Manoj Kumar
10.5120/ijca2023923006

Manoj Kumar . Management of Optimal Resource Allocation in the Cloud. International Journal of Computer Applications. 185, 25 ( Jul 2023), 20-24. DOI=10.5120/ijca2023923006

@article{ 10.5120/ijca2023923006,
author = { Manoj Kumar },
title = { Management of Optimal Resource Allocation in the Cloud },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 25 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number25/32848-2023923006/ },
doi = { 10.5120/ijca2023923006 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:02.900802+05:30
%A Manoj Kumar
%T Management of Optimal Resource Allocation in the Cloud
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 25
%P 20-24
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The management of resource allocation in the cloud is a critical issue that has received significant attention in recent years due to the increasing demand for cloud-based services. The efficient allocation of resources is crucial to meet the requirements of different applications and to optimize the utilization of available resources. This research paper explores the concept of optimal management of resource allocation in the cloud. The paper analyzes different approaches to resource allocation and discusses the advantages and limitations of each approach. The research also examines various factors that affect resource allocation in the cloud, including workload, resource availability, and resource utilization. The paper proposes a novel approach to resource allocation that is based on machine learning algorithms. The approach uses historical data to predict resource utilization and allocate resources accordingly. The research also investigates the impact of different factors on the performance of the proposed approach and compares it with other existing approaches. The findings of this research paper provide insights into the optimal management of resource allocation in the cloud. The proposed approach is shown to be effective in improving resource utilization and meeting the requirements of different applications. The research also highlights the importance of considering different factors that affect resource allocation in the cloud to achieve optimal performance.

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Index Terms

Computer Science
Information Sciences

Keywords

Resource allocation Cloud software SRGM

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