Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/comjnl/bxx043 | COMPUTER JOURNAL |
Keywords | Field | DocType |
cloud computing,virtual machine,VM migration,expectation maximization,server consolidation | Computer science,Expectation–maximization algorithm,Artificial intelligence,Cluster analysis,Machine learning,Cloud computing | Journal |
Volume | Issue | ISSN |
61 | 1 | 0010-4620 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nisha Chaurasia | 1 | 0 | 0.34 |
| Shashikala Tapaswi | 2 | 187 | 30.57 |
| Joydip Dhar | 3 | 37 | 12.11 |