Bigdata and HPC Convergence with Locality Based Cuckoo Search Method

Bigdata analytics with High Performance Computing has attained focus of various researchers due to the services that has been provided to the cloud users with user satisfaction. Understanding the evolution of big data systems and HPC systems helps to define the key differences, the goals behind them, and their architectures. There are four broad application classes that driving the requirements of data analytics tools and frameworks. They are the data pipelines, large-scale machine learning including deep learning applications streaming applications, and graph applications. Historically, HPC systems have given less focus to data management and more focus to designing highperformance algorithms. Big data systems have done an excellent job in data management, data queries, and streaming applications. In this Research optimal scheduling of group of tasks would be done by using Locality Aware Scheduling based on Cuckoo Search Algorithm (LS-CSA) and the performance of Bigdata systems can immensely benefit from HPC. This method would schedule the similar tasks that shares the same data in the virtual machine where its corresponding data resides. The overall evaluation of the research work is done in the Cloudsim environment which is implemented and evaluated in terms of various performance metrics. The proposed research method provides optimal results than the existing research methods