Performance analysis of graph based iterative algorithms on MapReduce framework

In the recent few years, there has been an enormous growth in the amount of digital data that is being produced. Numerous attempts are being made to process this large amount of data in a fast and effective manner. Hadoop MapReduce is one such software framework that has gained popularity in the last few years for distributed computation of Big Data. It provides a scalable, economical and easier way to process massive amounts of data in-parallel on large computing cluster preserving the properties of fault tolerance in a transparent manner. However, Hadoop always stores intermediate results to the local disk for running iterative jobs. As a result, Hadoop usually suffers from long execution runtimes for iterative jobs as it typically pays a high I/O cost, wasting CPU cycles and network bandwidth. This paper analyses the problems of existing Hadoop and compare its performance against iMapReduce and HaLoop for graph based iterative algorithms. HaLoop offers better performance as it stores intermediate results in cache and reuses those data on the next successive iteration. For using cache invariant data (inter-iteration locality) it schedules the tasks onto the same node that might occur in different iterations.