Predictable Scaling Behaviour in the Data Centre with Multiple Application Servers

  • Mark Burgess
  • Gard Undheim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4269)


Load sharing in the data centre is an essential strategy for meeting service levels in high volume and high availability services. We investigate the accuracy with which simple, classical queueing models can predict the scaling behaviour of server capacity in an environment of both homogeneous and inhomogeneous hardware, using known traffic patterns as input. We measure the performance of three commonly used load sharing algorithms and show that the simple queueing models underestimate performance needs significantly at high load. Load sharing based on real-time network monitoring performs worst on average. The work has implications for the accuracy of Quality of Service estimates.


Service Level Service Level Agreement Round Robin Load Sharing Request Rate 
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  1. 1.
    British Standards Institute. BS15000 IT Service Management (2002)Google Scholar
  2. 2.
    Sauvé, J., et al.: Sla design from a business perspective. In: Schönwälder, J., Serrat, J. (eds.) DSOM 2005. LNCS, vol. 3775, pp. 72–83. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Xu, W., Zhu, X., Singhal, S., Wang, Z.: Predictive control for dynamic resource allocation in enterprise data centers. In: Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium (NOMS 2006), pp. 115–126. IEEE Press, Los Alamitos (2006)Google Scholar
  4. 4.
    Li, X., Sha, L., Zhu, X.: Adaptive control of multi-tiered web applications using queueing predictor. In: Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium (NOMS 2006), pp. 106–114. IEEE Press, Los Alamitos (2006)Google Scholar
  5. 5.
    Bjørnstad, J.H., Burgess, M.: On the reliability of service level estimators in the data centre. In: State, R., van der Meer, S., O’Sullivan, D., Pfeifer, T. (eds.) DSOM 2006. LNCS, vol. 4269. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Menascé, D.A., Almeida, V.A.F.: Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning. Prentice Hall, Englewood Cliffs (2000)CrossRefGoogle Scholar
  7. 7.
    Burgess, M.: Analytical Network and System Administration — Managing Human-Computer Systems. J. Wiley & Sons, Chichester (2004)CrossRefGoogle Scholar
  8. 8.
    Rodosek, G.B.: Quality aspects in it service management. In: Feridun, M., Kropf, P.G., Babin, G. (eds.) DSOM 2002. LNCS, vol. 2506, p. 82. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Høyland, A., Rausand, M.: System Reliability Theory: Models and Statistical Methods. J. Wiley & Sons, New York (1994)MATHGoogle Scholar
  10. 10.
    Teo, Y.M., Ayani, R.: Comparison of load balancing strategies on cluster-based web servers. Transactions of the Society for Modeling and Simulation (2001)Google Scholar
  11. 11.
    Barford, P., Crovella, M.: Generating representative web workloads for network and server performance evaluation. In: SIGMETRICS 1998/PERFORMANCE 1998: Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, pp. 151–160. ACM Press, New York (1998)Google Scholar
  12. 12.
    Cardellini, V., Colajanni, M.: Dynamic load balancing on web-server systems. Internet Computing IEEE 3(4), 28–39 (1999)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Mark Burgess
    • 1
  • Gard Undheim
    • 1
  1. 1.Oslo University CollegeNorway

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