Improvised Schema for Data Analysis in Big Data Cloud

Abstract

The big data analysis using cloud computing is an ideal combination. The big data cloud greatly improves the performance of different applications and more helpful in problem-solving and decision making. The advancement of big data and cloud computing is attractive for data analysis. In big data for data processing and analysis, resource scheduling algorithms play a vital role. To meet the industry standards in data analysis, optimal usage of resources is very essential in the big data cloud. Moreover, the big data cloud has many challenges in optimal resource utilization. Many researchers have introduced a different kind of resource scheduling algorithms. But no algorithm can guarantee the performance and doesn't meet the user expectations. In existing system, there is no specification for monitor of VM status and does not aware the requirement of tasks. In this paper, we proposed improvised schema for better allocation and utilization of resources and improves the performance of data analysis in big data cloud. The proposed improvised schema considers different aspects such as analyzing tasks, scheduling and VM management. The proposed improvised schema improves the performance in terms of computation cost, execution time and optimal resource utilization. The proposed improvised schema achieved better results when compares with previous resource scheduling algorithms