Queuing Theory in Cloud Computing: Analyzing M/M/1 and M/M/c/N Models with AWS

Clicks: 1
ID: 310651
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #339 of 409 articles by views in International Journal of Science and Social Science Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 409 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
This paper explores the application of queuing theory in cloud computing, emphasizing its potential to optimize service delivery, resource utilization, and cost efficiency. Various queuing models, such as M/M/1, and  M/M/c/N, are analyzed in the context of cloud services to address dynamic workloads and user demands. Implementation steps and code examples for each model are provided, highlighting key metrics like queue length, waiting time, server utilization, and blocking probability. A comparative analysis of these models illustrates their suitability for different scenarios, from single-server setups to complex systems with variable service times. The findings underline the importance of selecting appropriate queuing models to meet system-specific requirements and propose future enhancements to tackle challenges like impatient user behavior and resource constraints.
Reference Key
imported_1768940259_696fe2e3444b8 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pratima Singh
Journal International Journal of Science and Social Science Research
Year 2025
DOI
10.5281/zenodo.14942226
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.