Various Multicore Processors with Splitting and Federation of Jobs In Map Reduction

Authors(3) :-K. Darshan, Prof. S. Ramakrishna, A. Mallikarjuna

To increase the performance of the appliance we elect the digital computer supported its quicker execution and power hungry, power economical options of the cores. Here we tend to area unit selecting a replacement hadoop hardware that is capable of process Heterogeneous cores at intervals one Multi core processor for achieving the great performance. this sort of Multi core processors area unit ready to produce virtual resource pools supported the priority planning like “slow” and “fast” based mostly on the multi category priority schedules. In some cases same knowledge are often accessed with the opposite resources bestowed within the Resource pool with either “slow” or “fast” slots. Heterogeneous Multi core processors improve the capability of the Processors so turnout values are often accrued.

Authors and Affiliations

K. Darshan
Student, Department of Computer Science , S.V.University, Tirupati, Andhra Pradesh, India
Prof. S. Ramakrishna
Professor, Department of Computer Science , S.V.University, Tirupati, Andhra Pradesh, India
A. Mallikarjuna
Professor, Department of Computer Science , S.V.University, Tirupati, Andhra Pradesh, India

Multicore Processor, Heterogeneous Cores, Resource Pool, Priority Scheduling

  1. T. White, Hadoop: The Definitive Guide. Yahoo Press.
  2. F. Ahmad et al., Tarazu: Optimizing Map Reduce on Heterogeneous Clusters, in Proceedings of ASPLOS, 2012.
  3. J. Dean and S. Ghemawat, Map Reduce: Simplified data processing on large clusters, Communications of the ACM, vol. 51, no. 1, 2008.
  4. M. Zaharia et al., Delay scheduling: A simple technique for Achieving locality and fairness in cluster scheduling, in Proceedings of EuroSys, 2010.
  5. Apache, Capacity Scheduler Guide, 2010. Online]. Available: capacity scheduler.html
  6. Z. Zhang, L. Cherkasova, and B. T. Loo, Benchmarking approach for designing a map reduce performance model, in ICPE, 2013, pp. 253–258.
  7. S. Rao et al., Sailfish: A Framework For Large Scale Data Processing, in Proceedings of SOCC, 2012.
  8. A. Gates, O. Natkovich, S. Chopra, P. Kamath, S. Narayanam, C. Olston, B. Reed, S. Srinivasan, and U. Srivastava, Building a high-level dataflow system on top of map reduce: The pig experience, PVLDB, vol. 2, no. 2, pp. 1414–1425, 2009.
  9. A. Verma, L. Cherkasova, and R. H. Campbell, ARIA: Automatic Resource Inference and Allocation for MapReduce Environments, in Proc. of ICAC, 2011.
  10. Play It Again, SimMR! in Proceedings of Intl. IEEE Cluster’ 2011.
  11. S. Ren, Y. He, S. Elnikety, and S. McKinley, Exploiting Processor Heterogeneity in Interactive Services, in Proceedings of ICAC, 2013.
  12. H. Esmaeilzadeh, T. Cao, X. Yang, S. M. Blackburn, and K. S. McKinley, Looking back and looking forward: power, performance, and upheaval, Commun. ACM, vol. 55, no. 7, 2012.
  13. C. Bienia, S. Kumar, J. Singh, and K. Li, The PARSEC benchmark suite: Characterization and architectural implications. in Technical Report TR-811-08, Princeton University, 2008.
  14. Pass Mark Software. CPU Benchmarks, 2013. Online]. Available: Xeon+E3-1240+%40+3.30GHz
  15. F. Yan, L. Cherkasova, Z. Zhang, and E. Smirni, Optimizing power and performance trade-offs of map reduce job processing with heterogeneous multi-core processors, in Proc. of the IEEE 7th International Conference on Cloud Computing (Cloud’2014), June, 2014.
  16. A. Verma et al., Deadline-based workload management for map reduce environments: Pieces of the performance puzzle, in Proc. of IEEE/IFIP NOMS, 2012.
  17. R. Kumar, D. M. Tullsen, P. Ranganathan, N. P. Jouppi, and K. I. Farkas, Single-is a heterogeneous multi-core architectures for multithreaded workload performance, in ACM SIGARCH Computer Architecture News, vol. 32, no. 2, 2004.
  18. K. Van Craeynest, A. Jaleel, L. Eeckhout, P. Narvaez, and J. Emer, Scheduling heterogeneous multi-cores through performance impact estimation (pie), in Proceedings of the 39th International Symposium on Computer Architecture, 2012.
  19. M. Becchi and P. Crowley, Dynamic thread assignment on heterogeneous multiprocessor architectures, in Proceedings of the 3rd conference on Computing frontiers, 2006.
  20. D. Shelepov and A. Fedorova, Scheduling on heterogeneous multi core processors using architectural signatures, in Proceedings of the Workshop on the Interaction between Operating Systems and Computer Architecture, 2008.
  21. K. Van Craeynest and L. Eeckhout, Understanding fundamental design choices in single-is a heterogeneous multicore architectures, ACM Transactions on Architecture and Code Optimization (TACO), vol. 9, no. 4, p. 32, 2013.
  22. M. Zaharia et al., Improving map reduce performance in heterogeneous environments, in Proceedings of OSDI, 2008.
  23. Q. Chen, D. Zhang, M. Guo, Q. Deng, and S. Guo, Samr: A self-adaptive map reduce scheduling algorithm in heterogeneous environment, in IEEE 10th International Conference on Computer and Information Technology (CIT), 2010.
  24. R. Gandhi, D. Xie, and Y. C. Hu, Pikachu: How to rebalance load in optimizing map reduce on heterogeneous clusters, in Proceedings of 2013 USENIX Annual Technical Conference. USENIX Association, 2013.
  25. J. Xie et al., Improving map reduce performance through data placement in heterogeneous hadoop clusters, in Proceedings of the IPDPS Workshops: Heterogeneity in Computing, 2010.
  26. G. Gupta, C. Fritz, B. Price, R. Hoover, J. DeKleer, and C. Witteveen, Throughput Scheduler: Learning to Schedule on Heterogeneous Hadoop Clusters, in Proc. of ICAC, 2013.
  27. G. Lee, B.-G. Chun, and R. H. Katz, Heterogeneity-aware resource allocation and scheduling in the cloud, in Proceedings of the 3rd USENIX Workshop on Hot Topics in Cloud Computing, Hot Cloud, 2011.
  28. J. Polo et al., Performance management of accelerated map reduce workloads in heterogeneous clusters, in Proceedings of the 41st Intl. Conf. on Parallel Processing, 2010.
  29. W. Jiang and G. Agrawal, Mate-cg: A map reduce-like framework for accelerating data-intensive computations on heterogeneous clusters, in Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, May 2012, pp. 644–655.
  30. Apache, Apache Hadoop Yarn, 2013. Online]. Available: hadoop-yarn-site/YARN.html
  31. A. Verma, L. Cherkasova, and R. H. Campbell, Resource Provisioning Framework for Map Reduce Jobs with Performance Go als, Proc. of the 12th ACM/IFIP/USENIX Middleware Conference, 2011.

Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1247-1252
Manuscript Number : CSEIT1833593
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Darshan, Prof. S. Ramakrishna, A. Mallikarjuna, "Various Multicore Processors with Splitting and Federation of Jobs In Map Reduction ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.1247-1252, March-April-2018.
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