Improved Fair Scheduling Algorithm for Hadoop Clustering SNEHA and SHONEY SEbASTIAN

Traditional way of storing such a huge amount of data is not convenient because processing those data in the later stages is very tedious job. So nowadays, Hadoop is used to store and process large amount of data. When we look at the statistics of data generated in the recent years it is very high in the last 2 years. Hadoop is a good framework to store and process data efficiently. It works like parallel processing and there is no failure or data loss as such due to fault tolerance. Job scheduling is an important process in Hadoop Map Reduce. Hadoop comes with three types of schedulers namely FIFO (First in first out), Fair and Capacity Scheduler. The schedulers are now a pluggable component in the Hadoop Map Reduce framework. This paper talks about the native job scheduling algorithms in Hadoop. Fair scheduling algorithm is analysed with its algorithm considering its response time, throughput and performance. Advantages and drawbacks of fair scheduling algorithm is discussed. Improvised fair scheduling algorithm is proposed with new strategy. Analysis is made with respect to response time, throughput and performance is calculated in naive fair scheduling and improvised fair scheduling. Improvised fair Scheduling algorithms is used in the cases where there is jobs with high and less processing time.