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Towards Benchmarking IaaS and PaaS Clouds for Graph Analytics

  • Alexandru Iosup
  • Mihai Capotă
  • Tim Hegeman
  • Yong Guo
  • Wing Lung Ngai
  • Ana Lucia Varbanescu
  • Merijn Verstraaten
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8991)

Abstract

Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.

Keywords

Cloud Service Graph Analytic Generic Architecture Input Dataset System Under Test 
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.

Notes

Acknowledgments

This work is supported by the Dutch STW/NOW Veni personal grants @large (#11881) and Graphitti (#12480), by the EU FP7 project PEDCA, by the Dutch national program COMMIT and its funded project COMMissioner, and by the Dutch KIEM project KIESA. The authors wish to thank Hassan Chafi and the Oracle Research Labs, Peter Boncz and the LDBC project, and Josep Larriba-Pey and Arnau Prat Perez, whose support has made the Graphalytics benchmark possible; and to Tilmann Rabl, for facilitating this material.

References

  1. 1.
    Lumsdaine, B.H.A., Gregor, D., Berry, J.W.: Challenges in parallel graph processing. Parallel Process. Lett. 17, 5–20 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Agarwal, V., Petrini, F., Pasetto, D., Bader, D.A.: Scalable graph exploration on multicore processors. In: SC, pp. 1–11 (2010)Google Scholar
  3. 3.
    Albayraktaroglu, K., Jaleel, A., Wu, X., Franklin, M., Jacob, B., Tseng, C.-W., Yeung, D.: Biobench: a benchmark suite of bioinformatics applications. In: ISPASS, pp. 2–9. IEEE Computer Society (2005)Google Scholar
  4. 4.
    Amaral, J.N.: How did this get published? pitfalls in experimental evaluation of computing systems. LTES talk (2012). http://webdocs.cs.ualberta.ca/amaral/Amaral-LCTES2012.pptx. Accessed October 2012
  5. 5.
    Amazon Web Services. Case studies. Amazon web site, October 2012. http://aws.amazon.com/solutions/case-studies/. Accessed October 2012
  6. 6.
    Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–12 (1999)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Brebner, P., Cecchet, E., Marguerite, J., Tuma, P., Ciuhandu, O., Dufour, B., Eeckhout, L., Frénot, S., Krishna, A.S., Murphy, J., Verbrugge, C.: Middleware benchmarking: approaches, results, experiences. Concurrency Comput. Pract. Experience 17(15), 1799–1805 (2005)CrossRefGoogle Scholar
  8. 8.
    Buble, A., Bulej, L., Tuma, P.: Corba benchmarking: a course with hidden obstacles. In: IPDPS, p. 279 (2003)Google Scholar
  9. 9.
    Buluç, A., Duriakova, E., Fox, A., Gilbert, J.R., Kamil, S., Lugowski, A., Oliker, L., Williams, S.: High-productivity and high-performance analysis of filtered semantic graphs. In: IPDPS (2013)Google Scholar
  10. 10.
    Burtscher, M., Nasre, R., Pingali, K.: A quantitative study of irregular programs on GPUS. In: 2012 IEEE International Symposium on Workload Characterization (IISWC), pp. 141–151. IEEE (2012)Google Scholar
  11. 11.
    Cai, J., Poon, C.K.: Path-hop: efficiently indexing large graphs for reachability queries. In: CIKM (2010)Google Scholar
  12. 12.
    Chapin, S.J., Cirne, W., Feitelson, D.G., Jones, J.P., Leutenegger, S.T., Schwiegelshohn, U., Smith, W., Talby, D.: Benchmarks and standards for the evaluation of parallel job schedulers. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1999, IPPS-WS 1999, and SPDP-WS 1999. LNCS, vol. 1659, pp. 67–90. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  13. 13.
    Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Lee, S.H., Skadron, K.: Rodinia: a benchmark suite for heterogeneous computing. In: The 2009 IEEE International Symposium on Workload Characterization, IISWC 2009, pp. 44–54 (2009)Google Scholar
  14. 14.
    Checconi, F., Petrini, F.: Massive data analytics: the graph 500 on IBM blue Gene/Q. IBM J. Res. Dev. 57(1/2), 10 (2013)CrossRefGoogle Scholar
  15. 15.
    Cong, G., Makarychev, K.: Optimizing large-scale graph analysis on multithreaded, multicore platforms. In: IPDPS (2012)Google Scholar
  16. 16.
    Deelman, E., Singh, G., Livny, M., Berriman, J.B., Good, J.: The cost of doing science on the cloud: the montage example. In: SC, p. 50. IEEE/ACM (2008)Google Scholar
  17. 17.
    Downey, A.B., Feitelson, D.G.: The elusive goal of workload characterization. SIGMETRICS Perform. Eval. Rev. 26(4), 14–29 (1999)CrossRefGoogle Scholar
  18. 18.
    Eeckhout, L., Nussbaum, S., Smith, J.E., Bosschere, K.D.: Statistical simulation: adding efficiency to the computer designer’s toolbox. IEEE Micro 23(5), 26–38 (2003)CrossRefGoogle Scholar
  19. 19.
    Folkerts, E., Alexandrov, A., Sachs, K., Iosup, A., Markl, V., Tosun, C.: Benchmarking in the cloud: what it should, can, and cannot be. In: Nambiar, R., Poess, M. (eds.) TPCTC 2012. LNCS, vol. 7755, pp. 173–188. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  20. 20.
    Frachtenberg, E., Feitelson, D.G.: Pitfalls in parallel job scheduling evaluation. In: Feitelson, D.G., Frachtenberg, E., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2005. LNCS, vol. 3834, pp. 257–282. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  21. 21.
    Genbrugge, D., Eeckhout, L.: Chip multiprocessor design space exploration through statistical simulation. IEEE Trans. Comput. 58(12), 1668–1681 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Georges, A., Buytaert, D., Eeckhout, L.: Statistically rigorous java performance evaluation. In: OOPSLA, pp. 57–76 (2007)Google Scholar
  23. 23.
    Graph500 consortium. Graph 500 benchmark specification. Graph500 documentation, September 2011. http://www.graph500.org/specifications
  24. 24.
    Gray, J. (ed.): The Benchmark Handbook for Database and Transasction Systems. Mergan Kaufmann, San Mateo (1993)Google Scholar
  25. 25.
    Guo, Y., Biczak, M., Varbanescu, A.L., Iosup, A., Martella, C., Willke, T.L.: How well do graph-processing platforms perform? an empirical performance evaluation and analysis. In: IPDPS (2014)Google Scholar
  26. 26.
    Guo, Y., Iosup, A.: The game trace archive. In: NETGAMES, pp. 1–6 (2012)Google Scholar
  27. 27.
    Guo, Y., Varbanescu, A.L., Iosup, A., Martella, C., Willke, T.L.: Benchmarking graph-processing platforms: a vision. In: ICPE, pp. 289–292 (2014)Google Scholar
  28. 28.
    Han, M., Daudjee, K., Ammar, K., Özsu, M.T., Wang, X., Jin, T.: An experimental comparison of pregel-like graph processing systems. PVLDB 7(12), 1047–1058 (2014)Google Scholar
  29. 29.
    Iosup, A.: Iaas cloud benchmarking: approaches, challenges, and experience. In: HotTopiCS, pp. 1–2 (2013)Google Scholar
  30. 30.
    Iosup, A., Epema, D.H.J.: GrenchMark: a framework for analyzing, testing, and comparing grids. In: CCGrid, pp. 313–320 (2006)Google Scholar
  31. 31.
    Iosup, A., Epema, D.H.J., Franke, C., Papaspyrou, A., Schley, L., Song, B., Yahyapour, R.: On grid performance evaluation using synthetic workloads. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2006. LNCS, vol. 4376, pp. 232–255. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  32. 32.
    Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.H.J.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Par. Dist. Syst. 22(6), 931–945 (2011)CrossRefGoogle Scholar
  33. 33.
    Iosup, A., Prodan, R., Epema, D.: Iaas cloud benchmarking: approaches, challenges, and experience. In: Li, X., Qiu, J. (eds.) Cloud Computing for Data-Intensive Applications. Springer, New York (2015)Google Scholar
  34. 34.
    Iosup, A., Prodan, R., Epema, D.H.J.: Iaas cloud benchmarking: approaches, challenges, and experience. In: SC Companion/MTAGS (2012)Google Scholar
  35. 35.
    Jackson, K.R., Muriki, K., Ramakrishnan, L., Runge, K.J., Thomas, R.C.: Performance and cost analysis of the supernova factory on the amazon aws cloud. Sci. Program. 19(2–3), 107–119 (2011)Google Scholar
  36. 36.
    Jain, R. (ed.): The Art of Computer Systems Performance Analysis. Wiley, New York (1991)zbMATHGoogle Scholar
  37. 37.
    Jiang, W., Agrawal, G.: Ex-MATE: data intensive computing with large reduction objects and its application to graph mining. In: CCGRID (2011)Google Scholar
  38. 38.
    Katz, G.J., Kider Jr., J.T.: All-pairs shortest-paths for large graphs on the GPU. In: 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, pp. 47–55 (2008)Google Scholar
  39. 39.
    LDBC consortium. Social network benchmark: Data generator. LDBC Deliverable 2.2.2, September 2013. http://ldbc.eu/sites/default/files/D2.2.2_final.pdf
  40. 40.
    Leskovec, J.: Stanford Network Analysis Platform (SNAP). Stanford University, California (2006)Google Scholar
  41. 41.
    Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, pp. 177–187, 21–24 August 2005Google Scholar
  42. 42.
    Lu, Y., Cheng, J., Yan, D., Wu, H.: Large-scale distributed graph computing systems: an experimental evaluation. PVLDB 8(3), 281–292 (2014)Google Scholar
  43. 43.
    Mell, P., Grance, T.: The NIST definition of cloud computing. National Institute of Standards and Technology (NIST) Special Publication 800–145, September 2011. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf. Accessed October 2012
  44. 44.
    de Laat, C., Verstraaten, M., Varbanescu, A.L.: State-of-the-art in graph traversals on modern arhictectures. Technical report, University of Amsterdam, August 2014Google Scholar
  45. 45.
    Merrill, D., Garland, M., Grimshaw, A.: Scalable GPU graph traversal. SIGPLAN Not. 47(8), 117–128 (2012)CrossRefGoogle Scholar
  46. 46.
    Nasre, R., Burtscher, M., Pingali, K.: Data-driven versus topology-driven irregular computations on GPUs. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing (IPDPS), pp. 463–474. IEEE (2013)Google Scholar
  47. 47.
    Oskin, M., Chong, F.T., Farrens, M.K.: Hls: combining statistical and symbolic simulation to guide microprocessor designs. In: ISCA, pp. 71–82 (2000)Google Scholar
  48. 48.
    Penders, A.: Accelerating graph analysis with heterogeneous systems. Master’s thesis, PDS, EWI, TUDelft, December 2012Google Scholar
  49. 49.
    Pingali, K., Nguyen, D., Kulkarni, M., Burtscher, M., Hassaan, M.A., Kaleem, R., Lee, T.-H., Lenharth, A., Manevich, R., Méndez-Lojo, M., et al.: The tao of parallelism in algorithms. ACM SIGPLAN Not. 46(6), 12–25 (2011)CrossRefGoogle Scholar
  50. 50.
    Que, X., Checconi, F., Petrini, F.: Performance analysis of graph algorithms on P7IH. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2014. LNCS, vol. 8488, pp. 109–123. Springer, Heidelberg (2014) Google Scholar
  51. 51.
    Raicu, I., Zhang, Z., Wilde, M., Foster, I.T., Beckman, P.H., Iskra, K., Clifford, B.: Toward loosely coupled programming on petascale systems. In: SC, p. 22. ACM (2008)Google Scholar
  52. 52.
    Hong, T.O.S., Kim, S.K., Olukotun, K.: Accelerating CUDA graph algorithms at maximum warp. In: Principles and Practice of Parallel Programming, PPoPP 2011 (2011)Google Scholar
  53. 53.
    Saavedra, R.H., Smith, A.J.: Analysis of benchmark characteristics and benchmark performance prediction. ACM Trans. Comput. Syst. 14(4), 344–384 (1996)CrossRefGoogle Scholar
  54. 54.
    Schroeder, B., Wierman, A., Harchol-Balter, M.: Open versus closed: a cautionary tale. In: NSDI (2006)Google Scholar
  55. 55.
    Sharkawi, S., DeSota, D., Panda, R., Indukuru, R., Stevens, S., Taylor, V.E., Wu, X.: Performance projection of HPC applications using SPEC CFP2006 benchmarks. In: IPDPS, pp. 1–12 (2009)Google Scholar
  56. 56.
    Shun, J., Blelloch, G.E.: Ligra: a lightweight graph processing framework for shared memory. In: PPOPP (2013)Google Scholar
  57. 57.
    Spacco, J., Pugh, W.: Rubis revisited: why J2EE benchmarking is hard. Stud. Inform. Univ. 4(1), 25–30 (2005)Google Scholar
  58. 58.
    Varbanescu, A.L., Verstraaten, M., de Laat, C., Penders, A., Iosup, A., Sips, H.: Can portability improve performance? an empirical study of parallel graph analytics. In: ICPE (2015)Google Scholar
  59. 59.
    Villegas, D., Antoniou, A., Sadjadi, S.M., Iosup, A.: An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. In: 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012, pp. 612–619, Ottawa, Canada, 13–16 May 2012Google Scholar
  60. 60.
    Wang, N., Zhang, J., Tan, K.-L., Tung, A.K.H.: On triangulation-based dense neighborhood graphs discovery. VLDB 4(2), 58–68 (2010)Google Scholar
  61. 61.
    Yigitbasi, N., Iosup, A., Epema, D.H.J., Ostermann, S.: C-meter: a framework for performance analysis of computing clouds. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2009, Shanghai, China, pp. 472–477, 18–21 May 2009Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexandru Iosup
    • 1
  • Mihai Capotă
    • 1
  • Tim Hegeman
    • 1
  • Yong Guo
    • 1
  • Wing Lung Ngai
    • 1
  • Ana Lucia Varbanescu
    • 2
  • Merijn Verstraaten
    • 2
  1. 1.Delft University of TechnologyDelftThe Netherlands
  2. 2.University of AmsterdamAmsterdamThe Netherlands

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