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Reliable MapReduce computing on opportunistic resources

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Abstract

MapReduce offers an ease-of-use programming paradigm for processing large data sets, making it an attractive model for opportunistic compute resources. However, unlike dedicated resources, where MapReduce has mostly been deployed, opportunistic resources have significantly higher rates of node volatility. As a consequence, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate on such volatile resources.

In this paper, we propose MOON, short for MapReduce On Opportunistic eNvironments, which is designed to offer reliable MapReduce service for opportunistic computing. MOON adopts a hybrid resource architecture by supplementing opportunistic compute resources with a small set of dedicated resources, and it extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms to take advantage of the hybrid resource architecture. Our results on an emulated opportunistic computing system running atop a 60-node cluster demonstrate that MOON can deliver significant performance improvements to Hadoop on volatile compute resources and even finish jobs that are not able to complete in Hadoop.

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References

  1. Hadoop. http://hadoop.apache.org/core/

  2. Spot Instances on Amazon EC2. http://aws.amazon.com/ec2/spot-instances/

  3. Adya, A., Bolosky, W., Castro, M., Chaiken, R., Cermak, G., Douceur, J., Howell, J., Lorch, J., Theimer, M., Wattenhofer, R.: FARSITE: federated, available, and reliable storage for an incompletely trusted environment. In: Proceedings of the 5th Symposium on Operating Systems Design and Implementation (2002)

    Google Scholar 

  4. Anderson, D.: Boinc: a system for public-resource computing and storage. In: IEEE/ACM International Workshop on Grid Computing (2004)

    Google Scholar 

  5. Apple Inc. Xgrid. http://www.apple.com/server/macosx/technology/xgrid.html

  6. Averitt, S., Bugaev, M., Peeler, A., Shaffer, H., Sills, E., Stein, S., Thompson, J., Vouk, M.: Virtual computing laboratory (VCL). In: International of the International Conference on Virtual Computing Initiative (2007)

    Google Scholar 

  7. Chen, S., Schlosser, S.: Map-reduce meets wider varieties of applications meets wider varieties of applications. Technical report IRP-TR-08-05, Intel research (2008)

  8. Chien, A., Calder, B., Elbert, S., Bhatia, K.: Entropia: Architecture and performance of an enterprise desktop grid system. J. Parallel Distrib. Comput. 63, 597–610 (2003)

    Article  Google Scholar 

  9. Chun, B.-G., Dabek, F., Haeberlen, A., Sit, E., Weatherspoon, H., Kaashoek, M.F., Kubiatowicz, J., Morris, R.: Efficient replica maintenance for distributed storage systems. In: NSDI’06: Proceedings of the 3rd conference on Networked Systems Design & Implementation, Berkeley, CA, USA, pp. 4–4. USENIX Association, Berkeley (2006)

    Google Scholar 

  10. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  11. Fedak, G., He, H., Cappello, F.: Bitdew: a programmable environment for large-scale data management and distribution. In: SC ’08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, Piscataway, NJ, USA, pp. 1–12. IEEE Press, New York (2008)

    Google Scholar 

  12. Gharaibeh, A., Ripeanu, M.: Exploring data reliability tradeoffs in replicated storage systems. In: HPDC ’09: Proceedings of the 18th ACM international symposium on High performance distributed computing, New York, NY, USA, pp. 217–226. ACM, New York (2009)

    Chapter  Google Scholar 

  13. Ghemawat, S., Gobioff, H., Leung, S.: The Google file system. In: Proceedings of the 19th Symposium on Operating Systems Principles (2003)

    Google Scholar 

  14. Grant, M., Sehrish, S., Bent, J., Wang, J.: Introducing map-reduce to high end computing. In: 3rd Petascale Data Storage Workshop, Nov (2008)

    Google Scholar 

  15. GridGain Systems, LLC. Gridgain. http://www.gridgain.com/

  16. Gupta, A., Lin, B., Dinda, P.A.: Measuring and understanding user comfort with resource borrowing. In: HPDC ’04: Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing, Washington, DC, USA, pp. 214–224. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  17. Haeberlen, A., Mislove, A., Druschel, P.: Glacier: Highly durable, decentralized storage despite massive correlated failures. In: Proceedings of the 2nd Symposium on Networked Systems Design and Implementation (NSDI’05), May (2005)

    Google Scholar 

  18. Ko, S., Hoque, I., Cho, B., Gupta, I.: On availability of intermediate data in cloud computations. In: 12th Workshop on Hot Topics in Operating Systems (HotOS XII) (2009)

    Google Scholar 

  19. Kondo, D., Taufe, M., Brooks, C., Casanova, H., Chien, A.: Characterizing and evaluating desktop grids: an empirical study. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium (2004)

    Google Scholar 

  20. Matsunaga, A., Tsugawa, M., Fortes, J.: Cloudblast: combining mapreduce and virtualization on distributed resources for bioinformatics. In: Microsoft eScience Workshop (2008)

    Google Scholar 

  21. Strickland, J., Freeh, V., Ma, X., Vazhkudai, S.: Governor: Autonomic throttling for aggressive idle resource scavenging. In: Proceedings of the 2nd IEEE International Conference on Autonomic Computing (2005)

    Google Scholar 

  22. Sun Microsystems. Compute server. https://computeserver.dev.java.net/

  23. Thain, D., Tannenbaum, T., Livny, M.: Distributed computing in practice: the condor experience. In: Concurrency and Computation: Practice and Experience (2004)

    Google Scholar 

  24. Vazhkudai, S., Ma, X., Freeh, V., Strickland, J., Tammineedi, N., Scott, S.: Freeloader: scavenging desktop storage resources for bulk, transient data. In: Proceedings of Supercomputing (2005)

    Google Scholar 

  25. Zaharia, M., Konwinski, A., Joseph, A., Katz, R., Stoica, I.: Improving mapreduce performance in heterogeneous environments. In: OSDI (2008)

    Google Scholar 

  26. Zhong, M., Shen, K., Seiferas, J.: Replication degree customization for high availability. SIGOPS Oper. Syst. Rev. 42(4), 55–68 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, USA

    Heshan Lin & Wu-chun Feng

  2. Oak Ridge National Laboratory, North Carolina State University, Raleigh, USA

    Xiaosong Ma

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  1. Heshan Lin
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  2. Xiaosong Ma
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  3. Wu-chun Feng
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Correspondence to Heshan Lin.

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Lin, H., Ma, X. & Feng, Wc. Reliable MapReduce computing on opportunistic resources. Cluster Comput 15, 145–161 (2012). https://doi.org/10.1007/s10586-011-0158-7

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  • DOI: https://doi.org/10.1007/s10586-011-0158-7

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