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 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

AI for Cloud Cost Management: Predictive and Prescriptive Analytics

Author(s) Sambhav Patil, Yuvaraj Madheswaran
Country India
Abstract With reference to the previous information and literature review, the purpose of this research paper is to evaluate the impact of multithreading concerning computer resource utilization and performance by undertaking and comparing various crucial multithreading techniques, including work stealing, fork join, and the use of the thread pool control. In an experimental context, benchmark applications that can be characteristic of several domains were evaluated in a controlled multicore computing platform. Time, CPU occupation, memory usage and throughputs were determined systematically and compared according to the various workloads. The results highlight the superiority of the work-steing algorithm in keeping the execution time and CPU usage low compared to the rest as a sign of its efficiency in managing work loads. Further, the memory usage and the throughput statistics showed the degree of inefficiency of each algorithm and performance penalty. Interviews with developers elaborated on more practical issues of multithreading as the generally rigid nature of the problems suggested that optimal solutions called for adaptive measures in practice. Based on the results achieved in this research, developers and researchers can increase their knowledge about available multithreading algorithms and make suggestions to choose and apply them with high efficiency and low resource consumption.
Keywords multithreading, resource utilization, performance metrics, algorithms, efficiency
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-05
Cite This AI for Cloud Cost Management: Predictive and Prescriptive Analytics - Sambhav Patil, Yuvaraj Madheswaran - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28221
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.28221
Short DOI https://doi.org/g688sp
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com