Survey of Hardware Platforms Available for Big Data Analytics Using K-means Clustering Algorithm

Dr. M. Manimekalai, S. Regha

Abstract: The Purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper review the different hardware platforms available for big data analytics and assesses the advantages and Negative aspects of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, Concurrent processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and Weakness. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs. Using a star ratings table, a rigorous qualitative comparison between different platforms is also discussed for each of the six characteristics that are critical for the algorithms of big data analytics.

Keywords: Big data, MapReduce, graphics processing units, scalability, big data analytics, big data Platform, k-means clustering