Investigations on optimizing performance of the distributed computing in heterogeneous environment using machine learning technique for large scale data set

Abstract

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment.