Abstract
Big data systems address the challenges of capturing, storing, managing, analyzing, and visualizing big data. Within this context, developing benchmarks to evaluate and compare big data systems has become an active topic for both research and industry communities. To date, most of the state-of-the-art big data benchmarks are designed for specific types of systems. Based on our experience, however, we argue that considering the complexity, diversity, and rapid evolution of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads. Given this motivation, in this paper, we first propose the key requirements and challenges in developing big data benchmarks from the perspectives of generating data with 4 V properties (i.e. volume, velocity, variety and veracity) of big data, as well as generating tests with comprehensive workloads for big data systems. We then present the methodology on big data benchmarking designed to address these challenges. Next, the state-of-the-art are summarized and compared, following by our vision for future research directions.
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Big data benchmark by amplab of uc berkeley (2013). https://amplab.cs.berkeley.edu/benchmark/
Gridmix (2013). https://hadoop.apache.org/docs/r1.2.1/gridmix.html
Ibm big data platform (2013). http://www-01.ibm.com/software/data/bigdata/
Pigmix (2013). https://cwiki.apache.org/confluence/display/PIG/PigMix
Sort benchmark (2013). http://sortbenchmark.org/
Standard performance evaluation corporation (spec) (2013). http://www.spec.org/gwpg/wpc.static/wpcv1info.html
Tpc transaction processing performance council (2013). http://www.tpc.org/
Armstrong, T.G., Ponnekanti, V., Borthakur, D., Callaghan, M.: Linkbench: a database benchmark based on the facebook social graph. In: Proceedings of the 2013 International Conference on Management of Data, pp. 1185–1196. ACM (2013)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 143–154. ACM (2010)
Ferdman, M., Adileh, A., Kocberber, O., Volos, S., Alisafaee, M., Jevdjic, D., Kaynak, C., Popescu, A.D., Ailamaki, A., Falsafi, B.: Clearing the clouds: A study of emerging workloads on modern hardware. Technical report (2011)
Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A., Jacobsen, H.A.: Bigbench: towards an industry standard benchmark for big data analytics. In: Proceedings of the 2013 International Conference on Management of Data, pp. 1197–1208. ACM (2013)
Huang, S., Huang, J., Dai, J., Xie, T., Huang, B.: The hibench benchmark suite: Characterization of the mapreduce-based data analysis. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), pp. 41–51. IEEE (2010)
Jia, Z., Wang, L., Zhan, J., Zhang, L., Luo, C.: Characterizing data analysis workloads in data centers. In: 2013 IEEE International Symposium on Workload Characterization (IISWC), pp 66–76. IEEE (2013)
Ming, Z., Luo, C., Gao, W., Han, R., Yang, Q., Wang, L., Zhan, J.: Bdgs: A scalable big data generator suite in big data benchmarking. In: Rabl, T., et al. (eds.) Advancing Big Data Benchmarks. LNCS, pp. 138–154. Springer, Heidelberg (2014)
Pavlo, A., Paulson, E., Rasin, A., Abadi, D.J., DeWitt, D.J., Madden, S., Stonebraker, M.: A comparison of approaches to large-scale data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 165–178. ACM (2009)
Rabl, T., Frank, M., Sergieh, H.M., Kosch, H.: A data generator for cloud-scale benchmarking. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 41–56. Springer, Heidelberg (2011)
Tay, Y.: Data generation for application-specific benchmarking. VLDB, Challenges and Visions (2011)
Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S., et al.: Bigdatabench: A big data benchmark suite from internet services. In: Proceedings of the 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), IEEE (2014)
Zhu, Y., Zhan, J., Weng, C., Nambiar, R., Zhang, J., Chen, X., Wang, L.: BigOP: generating comprehensive big data workloads as a benchmarking framework. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014, Part II. LNCS, vol. 8422, pp. 483–492. Springer, Heidelberg (2014)
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Han, R., Lu, X., Xu, J. (2014). On Big Data Benchmarking. In: Zhan, J., Han, R., Weng, C. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2014. Lecture Notes in Computer Science(), vol 8807. Springer, Cham. https://doi.org/10.1007/978-3-319-13021-7_1
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DOI: https://doi.org/10.1007/978-3-319-13021-7_1
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