Analyzing Job Aware Scheduling Algorithm in Hadoop for Heterogeneous Cluster

• Main Authors: Mayuri A Mehta, Supriya Pati
• Format: Article
• Language: English
• Published: SAINTGITS College of Engineering 2015-12-01
• Series: International Journal of Research and Innovations in Science and Technology
• Subjects: Scheduling algorithm; heterogeneous cluster; Hadoop; MapReduce
• Online Access: http://journals.saintgits.org/paper-submission/uploads/article/V2%20I2%2008.pdf

A scheduling algorithm is required to efficiently manage cluster resources in a Hadoop cluster, thereby to increase resource utilization and to reduce response time. The job aware scheduling algorithm schedules non-local map tasks of jobs based on job execution time, earliest deadline first or workload of the job. In this paper, we present the performance evaluation of the job aware scheduling algorithm using MapReduce WordCount benchmark. The experimental results are compared with matchmaking scheduling algorithm. The results show that the job aware scheduling algorithm reduces average waiting time and memory wastage considerably as compared to matchmaking algorithm.