Load Balancing for Effective Resource Provisioning in a Heterogeneous Cluster using Machine Learning
Vijayasherly Velayutham1, Srimathi Chandrasekaran2

1Ms. Vijayasherly Velayutham, School of Computer Science and Engineering, VIT University, Vellore, (Tamil Nadu) India.
2Dr. Srimathi Chandrasekaran, School of Computer Science and Engineering, VIT University, Vellore, (Tamil Nadu) India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 505-508 | Volume-8 Issue-6, August 2019. | Retrieval Number: E7920068519/2019©BEIESP | DOI: 10.35940/ijeat.E7920.088619
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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: Compute Clusters are typically installed to increase performance and/or accessibility. Appropriate Resource Provisioning is a key feature in clustered computing environments to avoid provisioning resources lower than the actual requirement and provisioning of resources in excess. In this paper, a load balancing scheme leading to effective provisioning of resources have been proposed. Job History of compute-intensive jobs have been collected by conducting experiments to observe basic parameters of a job in a heterogeneous computing cluster environment. A Machine Learning model using Multi-Layer Perceptron and Support Vector Machine for provisioning of resources has been presented. The prediction model uses the job history collected from the cluster environment to predict the resource that would be appropriate for provisioning in future. The accuracy of the model is computed and the results of experiments show that Multi-Layer Perceptron presents a better performance than Support Vector Machine.
Keywords: Cluster Computing, Machine Learning, Multilayer Perceptron, Resource Provisioning, Support Vector Machine.