Research Article | Open Access | Download PDF
Volume 7 | Issue 4 | Year 2017 | Article Id. IJCOT-V45P305 | DOI : https://doi.org/10.14445/22492593/IJCOT-V45P305
Clustering Categorical-Time Evolving Data from K-Means to Rough Set Theory Using Map-Reduce Technique
S.Sridevi, Dr. Jeevaa Katiravan
Citation :
S.Sridevi, Dr. Jeevaa Katiravan, "Clustering Categorical-Time Evolving Data from K-Means to Rough Set Theory Using Map-Reduce Technique," International Journal of Computer & Organization Trends (IJCOT), vol. 7, no. 4, pp. 37-45, 2017. Crossref, https://doi.org/10.14445/22492593/IJCOT-V45P305
Abstract
In the cloud environment, utilization of resources should be scaled-up and scaled-down according to the customer needs. Managing the scalability in the cloud is a critical issue. Scalability can be accomplished by dynamic resource allocation. This dynamic resource allocation based on demand is efficient only on the knowledge of load prediction. Improving the accuracy of load prediction is essential to achieve optimal job scheduling and load balancing for cloud computing. When the load prediction and server reliability is carried out simultaneously, an optimal resource allocation is possible. Various load prediction methods are discussed in this paper.
Keywords
load prediction, prediction accuracy, dynamic resource allocation.
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