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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Swati R. Mahendrakar, B. M. Patil

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

	author = {Swati R. Mahendrakar and B. M. Patil},
	title = {PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {172},
	number = {4},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {32-39},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2017915130},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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Map Reduce, scheduling, resource allocation.