Exploratory Learning of Resource Management in Private Cloud Environment
Vipul Chudasama1, Madhuri Bhavsar2
1Vipul Chudasama, Department of Computer Science and Engineering, Nirma University, Ahmedabad, India.
2Dr. Madhuri Bhavsar, Department of Computer Science and Engineering, Nirma University, Ahmedabad, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 27 August 2019. | Manuscript published on 30 September 2019. | PP: 8011-8014 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6415098319/2019©BEIESP | DOI: 10.35940/ijrte.C6415.098319
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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: Paper: Scientific and Web applications are major sources of Internet traffic that requires resources such as Memory ,CPU and Network are on demand. Cloud computing and virtualization are the boons for such resource demand applications from various users. Service models of cloud computing provide a platform for many applications to use resources as pay per use model. In Cloud, Auto-scaling with manage Service Level Agreement (SLA) of resources is one of the main challenges to meet the current demand for resources. To maintain the performance of the cloud, which provision resources based on a heuristic for workload prediction is prime importance. In this paper, we address auto-scaling as a problem to forecast near-future demand of resource using a KNN machine learning methods suggest the optimized model for the dynamic variation of CPU utilization.
Keywords: Virtualization, Service Level Agreement, Resource Provisioning, Resource Prediction.
Scope of the Article: QOS And Resource Management