International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 118

Survey Paper | Computer Science & Engineering | India | Volume 4 Issue 3, March 2015


Survey on Schedulers Optimization to Handle Multiple Jobs in Hadoop Cluster

Shivaraj B. G. | Nagaraj Naik [3]


Abstract: An apache Hadoop project is a good platform that supports cost-effective such as commodity hardware implementation, with scalable infrastructure called Hadoop Distributed File System HDFS with parallel processing mechanism called MapReduce. Hadoop is well known for Big Data analytics requires more resources for collecting, storing, processing petabytes of large data requires one meaningful resource management. Hadoop handles jobs in batch modes, allocating resources, scheduling to these modes is an important issue in terms of Network Bandwidth, CPU time and Memory. Resources handled by MapReduce schedulers assigning resources in the form of MapReduceTasks. The MapReduceTasks are carefully handled by MapReduce schedulers by setting some benchmarks for individual namely MapCombineReducer and different Block Sizes, which ensures schedulers are optimized to achieve maximum efficiency in storage capacity, time and cost for handling multiple batch jobs in multiple cluster while guaranteeing the effectiveness of individual scheduler in terms of job execution.


Keywords: Big Data, Hadoop, HDFS, MapReduce, Schedulers optimization


Edition: Volume 4 Issue 3, March 2015,


Pages: 1179 - 1184

Survey on Schedulers Optimization to Handle Multiple Jobs in Hadoop Cluster


How to Cite this Article?

Shivaraj B. G., Nagaraj Naik, "Survey on Schedulers Optimization to Handle Multiple Jobs in Hadoop Cluster", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=SUB152238, Volume 4 Issue 3, March 2015, 1179 - 1184, #ijsrnet

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