Identifying Root Causes of Web Performance Degradation Using Changepoint Analysis

  • Jürgen Cito
  • Dritan Suljoti
  • Philipp Leitner
  • Schahram Dustdar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8541)


The large scale of the Internet has offered unique economic opportunities, that in turn introduce overwhelming challenges for development and operations to provide reliable and fast services in order to meet the high demands on the performance of online services. In this paper, we investigate how performance engineers can identify three different classes of externally-visible performance problems (global delays, partial delays, periodic delays) from concrete traces. We develop a simulation model based on a taxonomy of root causes in server performance degradation. Within an experimental setup, we obtain results through synthetic monitoring of a target Web service, and observe changes in Web performance over time through exploratory visual analysis and changepoint detection. Finally, we interpret our findings and discuss various challenges and pitfalls.


Control Chart Performance Degradation Periodic Delay Performance Change Service Level Agreement Violation 
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.


  1. 1.
    Aguilera, M.K., Mogul, J.C., Wiener, J.L., Reynolds, P., Muthitacharoen, A.: Performance debugging for distributed systems of black boxes. ACM SIGOPS Operating Systems Review 37, 74–89 (2003)CrossRefGoogle Scholar
  2. 2.
    Borzemski, L.: The experimental design for data mining to discover web performance issues in a wide area network. Cybernetics and Systems 41(1), 31–45 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Borzemski, L., Drwal, M.: Time series forecasting of web performance data monitored by MWING multiagent distributed system. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010, Part I. LNCS, vol. 6421, pp. 20–29. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Borzemski, L., Kamińska-Chuchmała, A.: Knowledge discovery about web performance with geostatistical turning bands method. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 581–590. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Borzemski, L., Kaminska-Chuchmala, A.: Knowledge engineering relating to spatial web performance forecasting with sequential gaussian simulation method. In: KES, pp. 1439–1448 (2012)Google Scholar
  6. 6.
    Borzemski, L., Kliber, M., Nowak, Z.: Using data mining algorithms in web performance prediction. Cybernetics and Systems 40(2), 176–187 (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Bouch, A., Kuchinsky, A., Bhatti, N.: Quality is in the eye of the beholder: meeting users’ requirements for internet quality of service. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 297–304. ACM (2000)Google Scholar
  8. 8.
    Chen, M.Y., Kiciman, E., Fratkin, E., Fox, A., Brewer, E.: Pinpoint: Problem determination in large, dynamic internet services. In: Proceedings of International Conference on Dependable Systems and Networks, DSN 2002, pp. 595–604. IEEE (2002)Google Scholar
  9. 9.
    Chen, Y., Mahajan, R., Sridharan, B., Zhang, Z.-L.: A provider-side view of web search response time. SIGCOMM Comput. Commun. Rev. 43(4), 243–254 (2013)Google Scholar
  10. 10.
    Cherkasova, L., Ozonat, K., Mi, N., Symons, J., Smirni, E.: Automated anomaly detection and performance modeling of enterprise applications. ACM Transactions on Computer Systems (TOCS) 27(3), 6 (2009)CrossRefGoogle Scholar
  11. 11.
    Cohen, I., Zhang, S., Goldszmidt, M., Symons, J., Kelly, T., Fox, A.: Capturing, indexing, clustering, and retrieving system history. ACM SIGOPS Operating Systems Review 39, 105–118 (2005)CrossRefGoogle Scholar
  12. 12.
    Heikes, R.G., Montgomery, D.C., Rardin, R.L.: Using common random numbers in simulation experiments - an approach to statistical analysis. Simulation 27(3), 81–85 (1976)CrossRefGoogle Scholar
  13. 13.
    Jaccard, P.: The distribution of the flora in the alpine zone. 1. New Phytologist 11(2), 37–50 (1912)CrossRefGoogle Scholar
  14. 14.
    King, A.: Speed up your site: Web site optimization. New Riders, Indianapolis (2003)Google Scholar
  15. 15.
    Leitner, P., Ferner, J., Hummer, W., Dustdar, S.: Data-Driven Automated Prediction of Service Level Agreement Violations in Service Compositions. Distributed and Parallel Databases 31(3), 447–470 (2013)CrossRefGoogle Scholar
  16. 16.
    Liu, Z., Niclausse, N., Jalpa-Villanueva, C., Barbier, S.: Traffic Model and Performance Evaluation of Web Servers. Technical Report RR-3840, INRIA (December 1999)Google Scholar
  17. 17.
    Magalhaes, J.P., Silva, L.M.: Anomaly detection techniques for web-based applications: An experimental study. In: 2012 11th IEEE International Symposium on Network Computing and Applications (NCA), pp. 181–190. IEEE (2012)Google Scholar
  18. 18.
    Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS) 8(1), 3–30 (1998)CrossRefzbMATHGoogle Scholar
  19. 19.
    Nguyen, T.H., Adams, B., Jiang, Z.M., Hassan, A.E., Nasser, M., Flora, P.: Automated detection of performance regressions using statistical process control techniques. In: Proceedings of the Third Joint WOSP/SIPEW International Conference on Performance Engineering, pp. 299–310. ACM (2012)Google Scholar
  20. 20.
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, (2013)Google Scholar
  21. 21.
    Forrester research. Ecommerce web site performance today: An updated look at consumer reaction to a poor online shopping experience (August 2009)Google Scholar
  22. 22.
    Shirazi, B.A., Kavi, K.M., Hurson, A.R. (eds.): Scheduling and Load Balancing in Parallel and Distributed Systems. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jürgen Cito
    • 1
  • Dritan Suljoti
    • 2
  • Philipp Leitner
    • 1
  • Schahram Dustdar
    • 3
  1. 1.s.e.a.l. – Software Evolution & Architecture LabUniversity of ZurichSwitzerland
  2. 2.Catchpoint Systems, Inc.New YorkUSA
  3. 3.Distributed Systems GroupVienna University of TechnologyAustria

Personalised recommendations