Toward Better Service Performance Management via Workload Prediction

  • Hachem MoussaEmail author
  • I-Ling Yen
  • Farokh Bastani
  • Yulin Dong
  • Wei He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11515)


In this paper, we consider managing service performance starting from the composition time, aiming to reduce the risk of execution failures during service composition. We use ARIMA to predict workloads of the services at the time when they are likely to be invoked and subsequently predict the response time and chances that the requests for accessing the services may be declined due to admission control. The in-depth analysis can help avoid timing failures during service execution. However, these analyses may incur overhead and we introduce a two-phase composition algorithm to reduce the potential overhead. Our system also considers continuous monitoring and service recomposition to greatly increase the probability of completing the service execution within the deadline. Experimental results show that our service management approach can greatly improve the success rate for meeting the deadline.


Service performance Performance management Service composition Service execution Workload prediction 


  1. 1.
    Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for web services composition. IEEE Trans. Software Eng. 30(5), 311–327 (2004)CrossRefGoogle Scholar
  2. 2.
    Dai, Y., Yang, L., Zhang, B.: Self-healing web service composition based on performance prediction. J. Comput. Sci. Technol. 24(2), 250–261 (2009)CrossRefGoogle Scholar
  3. 3.
    Yan, Y., Poizat, P., Zhao, L.: Repair vs. recomposition for broken service compositions. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 152–166. Springer, Heidelberg (2010). Scholar
  4. 4.
    Ma, H., Bastani, F., Yen, I.-L., Mei, H.: QoS-driven service composition with reconfigurable services. IEEE Trans. Serv. Comput. 6(1), 20–34 (2011)CrossRefGoogle Scholar
  5. 5.
    Bi, J., Zhu, Z., Tian, R., Wang, Q.: Dynamic provisioning modeling for virtualized multi-tier applications in cloud data center. In: IEEE Cloud (2010)Google Scholar
  6. 6.
    Calheiros, R.N., Ranjany, R., Buyya, R.: Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In: International Conference on Parallel Processing (2011)Google Scholar
  7. 7.
    Nan, X., He, Y., Guan, L.: Optimal resource allocation for multimedia cloud in priority service scheme. In: IEEE International Symposium on Circuits and Systems (2012)Google Scholar
  8. 8.
    Chen, X., Mohapatra, P., Chen, H.: An admission control scheme for predictable server response time for Web accesses. In: WWW10. Citeseer (2001)Google Scholar
  9. 9.
    D’Ambrogio, A., Bocciarelli, P.: A Model-driven approach to describe and predict the performance of composite services. In: WOSP (2007)Google Scholar
  10. 10.
    Van Hoecke, S., Verdickt, T., Dhoedt, B., Gielen, F., Demeester, P.: Modelling the performance of the Web Service platform using layered queueing networks. In: SAVCBS (2005)Google Scholar
  11. 11.
    Wu, Q., Zhang, M., Zheng, R., Lou, Y., Wei, W.: A QoS-satisfied prediction model for cloud-service composition based on a hidden markov model. Math. Probl. Eng. Article ID 387083, 7 p. (2013)Google Scholar
  12. 12.
    Ye, Z., Mistry, S., Bouguettaya, A.: Long-term-aware cloud service composition using multivariate time series analysis. IEEE Trans. Serv. Comput. 9(3), 382–393 (2016)CrossRefGoogle Scholar
  13. 13.
    Hyndman, R.J., Athanasopoulos, G.: Forecasting, Principles and Practice, 2nd edn. Otexts, Melbourne (2018)Google Scholar
  14. 14.
    Reiss, C., Wilkes, J., Hellerstein, J.: Google, 17 November 2014. Accessed 2016
  15. 15.
    Ye, Y., Yen, I.-L., Xiao, L., Thuraisingham, B.: Secure, highly available, and high performance peer-to-peer storage systems. In: IEEE (2008)Google Scholar
  16. 16.
    Zhang, H., Goel, A., Govindan, R.: An empirical evaluation of internet latency expansion. ACM SIGCOMM Comput. Commun. Rev. 35(1), 93–97 (2005)CrossRefGoogle Scholar
  17. 17.
    Moussa, H., Gao, T., Yen, I.-L., Bastani, F., Jeng, J.-J.: Toward effective service composition for real-time SOA-based systems. SOCA 4, 17–31 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Texas at DallasDallasUSA
  2. 2.Shandong UniversityShandongChina

Personalised recommendations