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Title: Capturing Node Resource Status and Classifying Workload for Map Reduce Resource Aware Scheduler
Authors: Mude, R.G.
Betta, A.
Debbarma, A.
Issue Date: 2015
Citation: Advances in Intelligent Systems and Computing, 2015, Vol.309 AISC, VOLUME 2, pp.247-257
Abstract: There has been an enormous growth in the amount of digital data, and numerous software frameworks have been made to process the same. Hadoop MapReduce is one such popular software framework which processes large data on commodity hardware. Job scheduler is a key component of Hadoop for assigning tasks to node. Existing MapReduce scheduler assigns tasks to node without considering node heterogeneity, workload type, and the amount of available resources. This leads to overburdening of node by one type of job and reduces the overall throughput. In this paper, we propose a new scheduler which capture the node resource status after every heartbeat, classifies jobs into two types, CPU bound and IO bound, and assigns task to the node which is having less CPU/IO utilization. The experimental result shows an improvement of 15-20 % on heterogeneous and around 10 % of homogeneous cluster with respect to Hadoop native scheduler. � Springer India 2015.
Appears in Collections:2. Conference Papers

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