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

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.

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"A Virtual Machine Scheduling Strategy based on Workload Prediction and Simulated Annealing Algorithm in Cloud Computing", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 6, page no.b750-b758, June-2023, Available :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: 000121122

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

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

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