PEEL: A Framework for Benchmarking Distributed Systems and Algorithms

  • Christoph BodenEmail author
  • Alexander Alexandrov
  • Andreas Kunft
  • Tilmann Rabl
  • Volker Markl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10661)


During the last decade, a multitude of novel systems for scalable and distributed data processing has been proposed in both academia and industry. While there are published results of experimental evaluations for nearly all systems, it remains a challenge to objectively compare different system’s performance. It is thus imperative to enable and establish benchmarks for these systems. However, even if workloads and data sets or data generators are fixed, orchestrating and executing benchmarks can be a major obstacle. Worse, many systems come with hardware-dependent parameters that have to be tuned and spawn a diverse set of configuration files. This impedes portability and reproducibility of benchmarks. To address these problems and to foster reproducible and portable experiments and benchmarks of distributed data processing systems, we present PEEL, a framework to define, execute, analyze, and share experiments. PEEL enables the transparent specification of benchmarking workloads and system configuration parameters. It orchestrates the systems involved and automatically runs and collects all associated logs of experiments. PEEL currently supports Apache HDFS, Hadoop, Flink, and Spark and can easily be extended to include further systems.


Distributed Data Processing System Hadoop System Under Test (SUT) Feature Hashing Bean Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been supported through grants by the German Science Foundation (DFG) as FOR1306 Stratosphere, the German Ministry for Education and Research as Berlin Big Data Center BBDC (funding mark 01IS14013A) and by Oracle Labs.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.-C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V., Naumann, F., Peters, M., Rheinländer, A., Sax, M.J., Schelter, S., Höger, M., Tzoumas, K., Warneke, D.: The stratosphere platform for big data analytics. VLDB J. 23(6), 939–964 (2014)CrossRefGoogle Scholar
  6. 6.
    Alexandrov, A., Kunft, A., Katsifodimos, A., Schüler, F., Thamsen, L., Kao, O., Herb, T., Markl, V.: Implicit parallelism through deep language embedding. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, 31 May–4 June, 2015, pp. 47–61 (2015)Google Scholar
  7. 7.
    Boden, C., Spina, A., Rabl, T., Markl, V.: Benchmarking data flow systems for scalable machine learning. In: Proceedings of the 4th Algorithms and Systems on MapReduce and Beyond, BeyondMR 2017, pp. 5:1–5:10. ACM, New York (2017)Google Scholar
  8. 8.
    Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., Tzoumas, K.: Apache Flink™: stream and batch processing in a single engine. IEEE Data Eng. Bull. 38(4), 28–38 (2015)Google Scholar
  9. 9.
    Chu, C.-T., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. In: Proceedings of the 19th International Conference on Neural Information Processing Systems, NIPS 2006, pp. 281–288. MIT Press, Cambridge (2006)Google Scholar
  10. 10.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: OSDI, pp. 137–150 (2004)Google Scholar
  11. 11.
    Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716–727 (2012)CrossRefGoogle Scholar
  12. 12.
    Low, Y., Gonzalez, J.E., Kyrola, A., Bickson, D., Guestrin, C.E., Hellerstein, J.: Graphlab: a new framework for parallel machine learning. arXiv preprint arXiv:1408.2041 (2014)
  13. 13.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, pp. 135–146. ACM, New York (2010)Google Scholar
  14. 14.
    McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., Chikkerur, S., Liu, D., Wattenberg, M., Hrafnkelsson, A.M., Boulos, T., Kubica, J.: Ad click prediction: a view from the trenches. In: KDD 2013. ACM (2013)Google Scholar
  15. 15.
    Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: WWW 2007. ACM (2007)Google Scholar
  16. 16.
    Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Christoph Boden
    • 1
    • 2
    Email author
  • Alexander Alexandrov
    • 1
  • Andreas Kunft
    • 1
  • Tilmann Rabl
    • 1
    • 2
  • Volker Markl
    • 1
    • 2
  1. 1.Technische Universität BerlinBerlinGermany
  2. 2.DFKISaarbrückenGermany

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