A Virtual Machine Scheduling Strategy based on Workload Prediction and Simulated Annealing Algorithm in Cloud Computing

Registration ID: IJNRD_199070

Published ID: IJNRD2306185

DOI: Click Here to Get

TUYISHIMIRE EVANGELINE , Wang Hanwu

Cloud Computing, Live Migration; Workload Prediction; SA Algorithm

Cloud computing is a system that helps customers manage IT infrastructure. However, the energy consumption of cloud data centers remains a problem. There are many ways to tackle this problem. Virtualization technology provides an effective solution for the efficient use of data center resources. It allows cloud service providers to run multiple virtual machines on a single server at the same time. Online migration technology can realize the dynamic integration of virtual machines to a small number of servers that satisfy resource requests. The deployment of virtual machines based on virtualization technology has become a research focus in the world. Currently, the initial deployment of virtual machines is mainly based on the degree of performance matching. We consider two types of virtualization and issues of both aspect. For one, the lack of consideration of the type of virtual machine load makes it impossible to efficiently use server resources and also leads to resource competition. On the other hand, the second and current dynamic deployment of virtual machines does not take into account the changing trend of server load, and cannot meet the dynamically changing cloud computing environment. For the above two issues, the specific work of this paper is as follows: For the initial deployment of virtual machines, a virtual machine allocation method based on load type awareness is proposed. This method aims at energy consumption optimization and load balancing. At the same time, it considers four types of resources: CPU, disk, network bandwidth, and memory requirements. Minimizes the deployment of virtual machines that consume the same type of resources to the same server. Experimental results show that the proposed algorithm effectively reduces energy consumption. (2) For the dynamic deployment of virtual machines, an efficient scheduling method for virtual machines based on load forecasting is proposed. First use the time series model ARMA to predict the change of server load in advance, and determine the migration timing of the virtual machine through the delay mechanism to avoid frequent migration of the virtual machine. Second, use a simulated annealing algorithm to find a suitable destination server for the virtual machine to be placed. Experimental results show that the proposed algorithm can sharply reduce the number of virtual machine migrations and significantly reduce energy consumption.

Published Paper   E-Certificate

TUYISHIMIRE EVANGELINE & Wang Hanwu (June-2023). A Virtual Machine Scheduling Strategy based on Workload Prediction and Simulated Annealing Algorithm in Cloud Computing. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), b750-b758. https://ijnrd.org/papers/IJNRD2306185.pdf

Volume 8 Issue 6, June-2023

Pages : b750-b758

Paper Reg. ID: IJNRD_199070

Published Paper Id: IJNRD2306185

Downloads: 000121978

Research Area: Computer Science & Technology 

Country: Changsha, Hunan, China

Published Paper PDF: https://ijnrd.org/papers/IJNRD2306185.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306185

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details