Enhancing Cluster Computing Performance through Big Data Techniques: A Survey on Scalability and Optimization

Cluster Computing Systems (CCS) is a type of technology that not only causes computing power improvement but also utilizes energy to a lesser degree by taking advantage of parallel programming while processing and reading massive amounts of data. We can have multiple Central Processing Units (CPUs) and storage devices (disks) where the massive size of data can be processed. However, Cluster Computing System also comes with its own set of challenges such as if for a reason the node stops operating, nodes stops communicating with each other and the data transfer doesn’t happen due to poor network which can lead to bottleneck while processing massive amounts of data. To overcome these issues, a well reputed tech giant known as Google, came up with a solution known as MapReduce. MapReduce is a framework designed for Big Data which takes care of processing large amounts of data over various servers. In this paper, we outline how CCS works and the challenges it faces today in the age of massive data. The introduction to some well received measures of Big Data are presented by us in this paper. These solutions show us the way we can address the issues we face in CSS. The primary goal of this writing is to look into the issues that we might face and the most efficient ways to resolve it in CSS.