Analyzing Cost Parameters Affecting Map Reduce Application Performance
Narinder Kaur Seera, Sunil Taruna · International Journal of Information Technology and Computer Science · 2016
Recently, big data analysis has become an imperative task for many big companies.Map-Reduce, an emerging distributed computing paradig m, is known as a promising architecture for big data analytics on commodity hardware.Map-Reduce, and its open source implementation Hadoop, have been extensively accepted by several co mpanies due to their salient features such as scalability, elasticity, fault-tolerance and flexibility to handle big data.Ho wever, these benefits entail a considerable performance sacrifice.The performance of a Map-Reduce application depends on various factors including the size of the input data set, cluster resource settings etc.A clear understanding of the factors that affect Map-Reduce application performance and the cost associated with those factors is required.In this paper, we study different performance parameters and an existing Cost Optimizer that computes the cost of Map-Reduce job execution.The cost based optimizer also considers various configuration parameters available in Hadoop that affect performance of these programs.This paper is an attempt to analyze the Map-Reduce application performance and identifying the key factors affecting the cost and performance of executing Map-Reduce applications.