Research Article

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

by  Swati R. Mahendrakar, B. M. Patil
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Issue 4
Published: Aug 2017
Authors: Swati R. Mahendrakar, B. M. Patil
10.5120/ijca2017915130
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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 },
                        year    = { 2017 },
                        volume  = { 172 },
                        number  = { 4 },
                        pages   = { 32-39 },
                        doi     = { 10.5120/ijca2017915130 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2017
                        %A Swati R. Mahendrakar
                        %A B. M. Patil
                        %T PRISM: Fine-Grained Phase and Resource Information-aware Scheduler for Map-Reduce%T 
                        %J International Journal of Computer Applications
                        %V 172
                        %N 4
                        %P 32-39
                        %R 10.5120/ijca2017915130
                        %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.

References
  • Hadoop MapReduce distribution [Online]. Available: http://hadoop.apache.org, 2015.
  • Hadoop Capacity Scheduler [Online]. Available: http://hadoop.apache.org/docs/stable/capacity_scheduler html/, 2015.
  • Hadoop Fair Scheduler [Online]. Available: http://hadoop.apache.org/docs/r0.20.2/fair_scheduler.html, 2015.
  • Hadoop Distributed File System [Online]. Available: hadoop.apache.org/docs/hdfs/current/, 2015.
  • GridMix benchmark for Hadoop clusters [Online]. Available:http://hadoop.apache.org/docs/mapreduce/curt/gridmix.html, 2015.
  • PUMA benchmarks [Online]. Available: http://web.ics.purdue.edu/fahmad/benchmarks/datasets.htm, 2015.
  • The Next Generation of Apache Hadoop MapReduce [Online].Available:http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html, 2015.
  • T. Condie, N. Conway, P. Alvaro, J. Hellerstein, K. Elmeleegy, and R. Sears, “MapReduce online,” in Proc. USENIX Symp. Netw. Syst. Des. Implementation, 2010, p. 21.
  • J. Dean and S. Ghemawat, “Mapreduce: Simplified data processing on large clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, 2008.
  • A. Ghodsi, M. Zaharia, B. Hindman, A. Konwinski, S. Shenker, and I. Stoica, “Dominant resource fairness: Fair allocation of multiple resource types,” in Proc. USENIX Symp. Netw. Syst. Des. Implementation, 2011, pp. 323–336.
  • H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. Cetin, and S. Babu, ”Starfish: A self-tuning system for big data analytics,” in Proc. Conf. Innovative Data Syst. Res., 2011, pp. 261–272.
  • M. Isard, V. Prabhakaran, J. Currey, U. Wieder, and K. Talwar, “Quincy: Fair scheduling for distributed computing clusters,” in Proc. ACMSIGOPS Symp. Oper. Syst. Principles, 2009, pp. 261–276.
  • C. Joe-Wong, S. Sen, T. Lan, and M. Chiang. “Multi-resource allocation: Flexible tradeoffs in a unifying framework,” in Proc. IEEE Int. Conf. Comput. Commun., 2012, pp. 1206–1214.
  • J. Polo, C. Castillo, D. Carrera, Y. Becerra, I. Whalley, M. Steinder, J. Torres, and E. Ayguad_e, “Resource-aware adaptive scheduling for MapReduce clusters,” in Proc. ACM/IFIP/USENIX Int. Conf. Middleware, 2011, pp. 187–207.
  • Verma, L. Cherkasova, and R. Campbell, “Resource provisioning framework for MapReduce jobs with performance goals,” in Proc. ACM/IFIP/USENIX Int. Conf. Middleware, 2011, pp. 165–186.
  • Qi Zhang, Student Member, IEEE, Mohamed Faten Zhani, Member, IEEE, Yuke Yang, Raouf Boutaba, Fellow, IEEE, and Bernard Wong, “PRISM: Fine-Grained Resource-Aware Scheduling for Map-Reduce,” in ieee transactions on cloud computing, vol. 3, no. 2, april/june 2015.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Map Reduce scheduling resource allocation.

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