Abstract
With cloud-based data management gaining more ground by day, the problem of estimating the progress of MapReduce queries in the cloud is of paramount importance. This problem is challenging to solve for two reasons: i) cloud is typically a large-scale heterogeneous environment, which requires progress estimation to tailor to non-uniform hardware characteristics, and ii) cloud is often built with cheap and commodity hardware that is prone to fail, so our estimation should be able to dynamically adjust. These two challenges were largely unaddressed in previous work. In this paper, we propose PEQC, a Progress Estimator of Queries composed of MapReduce jobs in the Cloud. Our work is able to apply to a heterogeneous setting and provides a dynamically update mechanism to repair the network when failure occurs. We experimentally validate our techniques on a heterogeneous cluster and results show that PEQC outperforms the state of the art.
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References
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A., Rasin, A.: HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads. In: 35th ACM Conference of Very Large Databases, pp. 922–933. ACM Press, New York (2009)
Chaudhuri, S., Kaushik, R., Ramamurthy, R.: When can we trust progress estimators for SQL queries. In: 25th ACM International Conference on Management of Data, pp. 575–586. ACM Press, New York (2005)
Chaudhuri, S., Narassaya, V., Ramamurthy, R.: Estimating progress of execution for SQL queries. In: 24th ACM International Conference on Management of Data, pp. 803–814. ACM Press, New York (2004)
Dean, J.: Experiences with mapreduce, an abstraction for large-scale computation. In: PACT, p. 1. IEEE Press, Washington (2006)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI, pp. 137–150. ACM Press, New York (2004)
Malcolm, D.G., Roseboom, J.H., Clark, C.E., Fazar, W.: Application of a technique for research and development program evaluation. Operations Research 7(5), 646–669 (1959)
Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online Aggregation. In: 17th ACM International Conference on Management of Data, pp. 171–182. ACM Press, New York (1997)
Dean, J.: Designs, lessons and advice from building large distributed systems. In: Keynote from LADIS 2009 (2009)
Luo, G., Naughton, J.F., Ellmann, C.J., Watzke, M.: Toward a progress indicator for database queries. In: 24th ACM International Conference on Management of Data, pp. 791–802. ACM Press, New York (2004)
Luo, G., Naughton, J.F., Ellmann, C.J., Watzke, M.: Increasing the accuracy and coverage of SQL progress indicators. In: 21st IEEE International Conference on Data Engineering, pp. 853–864. IEEE Press, Washington (2005)
Morton, K., Balazinska, M., Grossman, D.: ParaTimer: A progress indicator for mapreduce DAGs. In: 30th ACM International Conference on Management of Data, pp. 507–518. ACM Press, New York (2010)
Morton, K., Friesen, A., Balazinska, M., Grossman, D.: Estimating the progress of MapReduce pipelines. In: 26th IEEE International Conference on Data Engineering, pp. 681–684. IEEE Press, Washington (2010)
Pavlo, A., Rasin, A., Madden, S., Stonebraker, M., DeWitt, D., Paulson, E., Shrinivas, L., Abadi, D.J.: A comparison of approaches to large-scale data analysis. In: 29th ACM International Conference on Management of Data, pp. 165–178. ACM Press, New York (2009)
Schad, J., Dittrich, J., Quian-Ruiz, J.: Runtime measurements in the cloud: observing, analyzing, and reducing variance. J. Proc. of VLDB Endowment 3(1), 460–471 (2010)
Schatz, M.C.: CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)
Shogan, A.W.: Bounding distributions for a stochastic pert network. Networks 7(4), 259–381 (1977)
Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving MapReduce performance in heterogeneous environments. In: OSDI. ACM Press, New York (2008)
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Shi, Y., Meng, X., Liu, B. (2012). Halt or Continue: Estimating Progress of Queries in the Cloud. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_12
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DOI: https://doi.org/10.1007/978-3-642-29035-0_12
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