A Resource Efficient Expectation Maximization Clustering Approach for Cloud. (7th June 2017)

• Record Type: Journal Article
• Authors: Chaurasia, Nisha; Tapaswi, Shashikala; Dhar, Joydip
• Is Part Of: Computer journal. Volume 61:Number 1(2018)
• Page Start: 95
• Page End: 104
• Publication Date: 2017-06-07
• Subjects: cloud computing -- virtual machine -- VM migration -- expectation maximization -- server consolidation; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxx043
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Effective server consolidation in cloud has significantly become one of the major challenges. The determination of over-utilized and under-utilized servers and migration needs to work cordially with energy conservation and optimal resource usage. Clustering of servers makes easy retrieval of servers for best possible allocation of tasks. The paper focuses on the clustering of servers in effective manner using Expectation Maximization (EM) concept. It presents an algorithm using EM as a phase for the server consolidation in cloud. Employing EM for clustering makes more uniform clustering of servers leading to improved allocation of resource requests. The results of the proposed scheme have been compared with existing K-means, Fuzzy C-Means and Spectral clustering. The proposed EM algorithm performance is better in terms of handling of probabilistic constraints, guaranteeing convergence with well-separated components.