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2011 | 21 | 2 | 275-284
Tytuł artykułu

Performance evaluation of MapReduce using full virtualisation on a departmental cloud

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work analyses the performance of Hadoop, an implementation of the MapReduce programming model for distributed parallel computing, executing on a virtualisation environment comprised of 1 + 16 nodes running the VMWare workstation software. A set of experiments using the standard Hadoop benchmarks has been designed in order to determine whether or not significant reductions in the execution time of computations are experienced when using Hadoop on this virtualisation platform on a departmental cloud. Our findings indicate that a significant decrease in computing times is observed under these conditions. They also highlight how overheads and virtualisation in a distributed environment hinder the possibility of achieving the maximum (peak) performance.
Rocznik
Tom
21
Numer
2
Strony
275-284
Opis fizyczny
Daty
wydano
2011
otrzymano
2010-06-28
poprawiono
2010-11-16
poprawiono
2011-01-02
Twórcy
  • School of Computing, Robert Gordon University, St Andrew Street, Aberdeen AB25 1HG, UK
  • IDEAS Research Institute, Robert Gordon University, St Andrew Street, Aberdeen AB25 1HG, UK
  • School of Computing, Robert Gordon University, St Andrew Street, Aberdeen AB25 1HG, UK
Bibliografia
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  • Danelutto, M. (2004). Adaptive task farm implementation strategies, 12th Euromicro Workshop on Parallel, Distributed and Network-Based Processing, PDP 2004, IEEE, La Coruña, pp. 416-423.
  • Dean, J. and Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters, Proceedings of the 6th conference on Symposium on Operating Systems Design & Implementation OSDI'04, Vol. 6, USENIX, San Francisco, CA, pp. 137-150.
  • Dean, J. and Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters, Communications of the ACM 51(1): 107-113.
  • González-Vélez, H. (2006). Self-adaptive skeletal task farm for computational grids, Parallel Computing 32(7-8): 479-490.
  • González-Vélez, H. and Cole, M. (2010a). Adaptive statistical scheduling of divisible workloads in heterogeneous systems, Journal of Scheduling 13(4): 427-441.
  • González-Vélez, H. and Cole, M. (2010b). Adaptive structured parallelism for distributed heterogeneous architectures: A methodological approach with pipelines and farms, Concurrency and Computation: Practice and Experience 22(15): 2073-2094.
  • González-Vélez, H. and Leyton, M. (2010). A survey of algorithmic skeleton frameworks: High-level structured parallel programming enablers, Software: Practice and Experience 40(12): 1135-1160.
  • Ibrahim, S., Jin, H., Lu, L., Qi, L., Wu, S. and Shi, X. (2009). Evaluating MapReduce on virtual machines: The Hadoop case, in M. Jaatun, G. Zhao, and C. Rong (Eds.) CloudCom 2009, Lecture Notes in Computer Science, Vol. 5931, Springer-Verlag, Berlin/Heidelberg, pp. 519-528.
  • Kontagora, M. and González-Vélez, H. (2010). Benchmarking a MapReduce environment on a full virtualisation platform, in L. Barolli, F. Xhafa, S. Vitabile and H.-H. Hsu (Eds.), CISIS 2010, The Fourth International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, Poland, 15-18 February 2010, IEEE Computer Society, Washington, DC, pp. 433-438.
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  • Nagarajan, A.B., Mueller, F., Engelmann, C. and Scott, S.L. (2007). Proactive fault tolerance for HPC with Xen virtualization, in B. J. Smith (Ed.), Proceedings of the 21th Annual International Conference on Supercomputing, ICS 2007, Seattle, Washington, USA, June 17-21, 2007, ACM, New York, NY, pp. 23-32.
  • Nokia Research Center (2009). Disco, Manual version 0.2.3, Nokia Research Center, discoproject.org.
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  • Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G. and Kozyrakis, C. (2007). Evaluating MapReduce for multi-core and multiprocessor systems, 13th International Conference on High-Performance Computer Architecture (HPCA-13 2007), Phoenix, AZ, USA, pp. 13-24.
  • Robertazzi, T.G. (2003). Ten reasons to use divisible load theory, Computer 36(5): 63-68.
  • Sandholm, T. and Lai, K. (2009). MapReduce optimization using regulated dynamic prioritization, in J.R. Douceur, A.G. Greenberg, T. Bonald, J. Nieh (Eds.), Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/Performance 2009, Seattle, WA, USA, June 15-19, 2009, ACM, New York, NY, pp. 299-310.
  • The Apache Software Foundation (2008). Hadoop MapReduce tutorial, Manual version 0.15, Hadoop Project, hadoop.apache.org.
  • VMware (2007). Understanding full virtualization, paravirtualization, and hardware assist, White Paper Revision: 20070911, VMware, Inc., Palo Alto, CA.
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  • Youseff, L., Wolski, R., Gorda, B. and Krintz, C. (2006). Paravirtualization for HPC systems, in G. Min, B. Di Martino, L.T. Yang, M. Guo and Gudula Rünger (Eds.), Frontiers of High Performance Computing and Networking - ISPA 2006 International Workshops, Sorrento, Italy, December 4-7, 2006, Lecture Notes in Computer Science, Vol. 4331, Springer-Verlag, Berlin/Heidelberg, pp. 474-486.
  • Zaharia, M., Konwinski, A., Joseph, A., Katz, R. and Stoica, I. (2008). Improving MapReduce performance in heterogeneous environments, in R. Draves and R. van Renesse (Eds.), 8th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2008, December 8-10, 2008, San Diego, California, USA, USENIX Association, Berkeley, CA.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv21i2p275bwm
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