A Proactive Approach for Resource Provisioning in Cloud Computing
Ankita Jain1, Arun Kumar Yadav2, Brijesh Kumar Chaurasia3

1Ankita Jain, M.Tech Research Scholor, ITM University, Gwalior (Madhya Pradesh), India.
2Arun Kumar Yadav, Department of CSE, ITM University, Gwalior (Madhya Pradesh), India.
3Brijesh Kumar Chaurasia, Department of CSE, ITM University, Gwalior (Madhya Pradesh), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 435-444 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11780275S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cloud computing has become a preferred service supplier for information technology, on the users demands resources can be provided there over internet. On the internet workload are changing frequently but continuously changing pattern is still there. Currently for automatic scaling of resource management, low cost and improving resource utility in the cloud, workload prediction scheme has become a very bright packer. Recently, there are various approaches available for workload prediction which is based on the single model prediction approach. However because of the internet providing a large scale heterogeneity data over the cloud, it is very difficult to find out a satisfactory result by mean of a traditional model. We have proposed a proactive approach for resource allocation by analyzing large scale heterogeneity data in cloud. Our implementation shows a better result of resource prediction accuracy with low cost and less time consuming than previous approaches.
Keywords: Master Server, Slave Server, Workload, Resource Utilization, Virtual Machine.
Scope of the Article: Cloud Computing