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Reseach Article

PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce

by Swati R. Mahendrakar, B. M. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 4
Year of Publication: 2017
Authors: Swati R. Mahendrakar, B. M. Patil
10.5120/ijca2017915130

Swati R. Mahendrakar, B. M. Patil . PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce. International Journal of Computer Applications. 172, 4 ( Aug 2017), 32-39. DOI=10.5120/ijca2017915130

@article{ 10.5120/ijca2017915130,
author = { Swati R. Mahendrakar, B. M. Patil },
title = { PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 4 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number4/28241-2017915130/ },
doi = { 10.5120/ijca2017915130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:28.055114+05:30
%A Swati R. Mahendrakar
%A B. M. Patil
%T PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 4
%P 32-39
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, Map Reduce has become a popular model with regard to data-intensive computation. Map Reduce can significantly reduce the execution time of data-intensive jobs. In order to achieve this objective, Map Reduce breaks down each job into small map and reduce tasks and executes them in parallel across a large number of machines. However, existing solutions mainly focus on scheduling at the task-level, which offer sub-optimal job performance, because tasks may have resource requirements which may vary during their lifetime. This makes it difficult for existing system’s task-level schedulers to effectively utilize available resources in order to reduce job execution time. To avoid this limitation, PRISM is introduced. PRISM stands for Phase and Resource Information-aware Scheduler for Map-Reduce. PRISM consists of various clusters that perform resource-aware scheduling at the level of phases. PRISM can be defined as a fine-grained resource-aware Map Reduce scheduler that divides tasks into phases. Here, each phase has a constant resource usage profile, so that not a single phase suffers from starvation. PRISM also offers high resource utilization and provides 1:3x improvements in job running time as compared to the current Hadoop schedulers.

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Index Terms

Computer Science
Information Sciences

Keywords

Map Reduce scheduling resource allocation.