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The enterprise aims to upgrade the working of applications in the unify environment by balancing the memory pages of virtual machines. The MEB system is inconsequential and can be perfectly integrated into user space without interfacing with virtual machine monitor operation. A Global scheduling algorithm based on the dynamic criterion to determine the optimum allocation memory. They virtualization technique enables multiple virtual machines (VMs) to be placed on the same physical hosts and supports the live migration of virtual machine between physical hosts based on the performance requirements. When virtual machine does not use all the provided resources, they can be logically resized and consolidated to the minimum number of physical hosts. While idle nodes can be switched to sleep or hibernate mode to eliminate the idle energy consumption and thus reducing the total energy consumption (TEC) in cloud data centers. Cloud can achieve the same level of computing power a supercomputer does but at a much-reduced cost. Cloud is like a virtual supercomputer. The efficient task scheduling considers the completion time of tasks in a cloud environment. ASJS is mainly intended to decrease job’s completion time. This project works on ASJS algorithm. This algorithm mainly concentrates on allocating suitable VM with the suitable task. The computing power of each resource is defined as the product CPU speed and available CPU percentage and the transmission power of each cluster is defined as the average bandwidth between different clusters. ASJS status of each resource in the cloud as parameters to initialize the cluster score of each bundle.
Keywords:
virtual machine, EASJS, ASJS, Cluster, Resource
Cite Article:
"Enhanced Automatic Control Virtual Machine for Cloud Using Real Time Task Oriented Algorithm ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 6, page no.47 - 52, June-2018, Available :http://www.ijrti.org/papers/IJRTI1806008.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator