Cluster Based Resource Allocation Using K-Medoid Clustering Algorithm
|International Journal of Computer Science and Engineering|
|© 2016 by SSRG - IJCSE Journal|
|Volume 3 Issue 5|
|Year of Publication : 2016|
|Authors : D. Arul Selve, K. Kavitha|
How to Cite?
D. Arul Selve, K. Kavitha, "Cluster Based Resource Allocation Using K-Medoid Clustering Algorithm," SSRG International Journal of Computer Science and Engineering , vol. 3, no. 5, pp. 10-13, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I5P102
Map Reduce was proposed to make simpler the parallel meting out using a distributed computing platform that offers only two interfaces. Map Reduce has come out as a significant model for processing data in huge information centers. Map Reduce is a three phase algorithm consisting of Map, Shuffle and Reduce phases. Due to its extensive deployment, there have been numerous recent papers exactness practical schemes to get better the performance of Map Reduce systems. All these hard work focuses on one of the three phases to obtain performance enhancement. To reduce network traffic within a Map Reduce employment, we deem to aggregate data to send them to distant reduce tasks with same keys. In existing system, a decomposition-based distributed algorithm and online algorithm is used to deal with the large-scale optimization problem and aggregation of data in a dynamic manner respectively. This paper mainly focus in detail on the system process of implementing Partitioning cluster based resource allocation using K- medoid clustering algorithm in decomposition based distribution algorithm. The common realization of k-medoid clustering is the Partitioning Around Medoids (PAM).
Map Reduce, Big Data, Data partition, Aggregation, K-Medoid Clustering
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