Neural Adaptive Control in Application Service Management Environment

  • Tomasz Sikora
  • George D. Magoulas
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


This paper presents a method and a framework for adaptive control in Application Service Management environments. The controlled system is treated as a “black-box” by observing its operation during normal work or load conditions. Run-time metrics are collected and persisted creating a Knowledge Base of actual system states. Equipped with such knowledge we define system inputs, outputs and effectively select high/low Service Level Agreements values, and good/bad control actions from the past. On the basis of gained knowledge a training set is constructed, which determines the operation of a neural controller deployed in the application run-time. Control actions are executed in the background of the current system state, which is then again monitored and stored extending the states repository, giving views on the appropriateness of the control, which is frequently evaluated.


Application Service Management Adaptive Controller Service Level Agreement Knowledge Base Neural Networks Performance Metrics 


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  1. 1.
    Bigus, J.P.: Applying neural networks to computer system performance tuning. In: IEEE International Conference on Neural Networks, pp. 2442–2447 (1994)Google Scholar
  2. 2.
    Parekh, S., Ghandi, N., Hellberstein, J., Tilbury, D., Jayram, T.S., Bigus, J.: Using Control Theory to Achieve Service Level Objectives In Performance Management. Real-Time Systems 23(1-2), 127–141 (2000)Google Scholar
  3. 3.
    Bigus, J.P., Hellerstein, J.L., Jayram, T.S., Squillante, M.S.: Autotune: A generic agent for automated performance tuning. In: Practical Application of Intelligent Agents and Multi Agent Technology (2000)Google Scholar
  4. 4.
    Abdelzaher, T.F., Lu, C.: Modeling and performance control of Internet servers. In: Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, NSW, Australia, vol. 3, pp. 2234–2239 (2000)Google Scholar
  5. 5.
    Abdelzaher, T.F., Shin, K.G., Bhatti, N.: Performance guarantees for web server end-systems: A control-theoretical approach. IEEE Transactions on Parallel and Distributed Systems (TPDS) 13(1), 80–96 (2002)CrossRefGoogle Scholar
  6. 6.
    Ying, L., Abdelzaher, T., Chenyang, L., Gang, T.: An adaptive control framework for QoS guarantees and its application to differentiated caching. In: Tenth IEEE International Workshop on Issue Quality of Service, pp. 23–32 (2002)Google Scholar
  7. 7.
    Zhang, R., Lu, C., Abdelzaher, T.F., Stankovic, J.A.: ControlWare: a middleware architecture for feedback control of software performance. In: Proceedings of 22nd International Conference on Distributed Computing Systems, pp. 301–310 (2002)Google Scholar
  8. 8.
    Abdelzaher, T.F., Stankovic, J., Lu, C.: Feedback performance control in software services. IEEE Control System Magazine 23, pt. 3, 74–89 (2003)CrossRefGoogle Scholar
  9. 9.
    Lu, Y., Abdelzaher, T., Lu, C., Sha, L., Liu, X.: Feedback Control with Queueing-Theoretic Prediction for Relative Delay Guarantees. In: Ninth IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2003) in Web Servers, Toronto, Canada (2003)Google Scholar
  10. 10.
    Evans, E.: Domain-Driven Design: Tackling Complexity in the Heart of Software, August 20. Addison Wesley (2003) ISBN: 0321125215Google Scholar
  11. 11.
    Welsh, M., Culler, D.: Adaptive overload control for busy internet servers. In: Proceedings of the 4th Conference on USENIX Symposium on Internet Technologies and Systems, USITS 2003, vol. 4. USENIX Association, Berkeley (2003)Google Scholar
  12. 12.
    Hellerstein, J.L., Diao, Y., Parekh, S., Tilbury, D.M.: Feedback control of computing systems. Wiley Interscience Publication, John Wiley & Sons (2004) ISBN 9780471266372Google Scholar
  13. 13.
    Hellerstein, J.L.: Challenges in control engineering of computing systems. In: Proceedings of the American Control Conference, Boston, MA, USA, vol. 3, pp. 1970–1979 (2004)Google Scholar
  14. 14.
    Abrahao, B., Almeida, V., Almeida, J., Zhang, A., Beyer, D., Safai, F.: Self-Adaptive SLA-Driven Capacity Management for Internet Services. In: 10th IEEE/IFIP Network Operations and Management Symposium - NOMS 2006 (2006)Google Scholar
  15. 15.
    Bodik, P., Griffith, R., Sutton, C., Fox, A., Jordan, M., Patterson, D.: Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. In: Proceedings of the 2009 Conference on Hot Topics in Cloud Computing, HotCloud 2009. USENIX Association, Berkeley (2009)Google Scholar
  16. 16.
    Wang, Z., Chen, Y., Gmach, D., Singhal, S., Watson, B.J., Rivera, W., Zhu, X., Hyser, C.D.: AppRAISE: Application-level Performance Management in Virtualized Server Environment. IEEE Transactions on Network and Service Management 6(4), 240–254 (2009)CrossRefGoogle Scholar
  17. 17.
    Xiong, P., Wang, Z., Jung, G., Pu, C.: Study on performance management and application behavior in virtualized environment. In: IEEE Network Operations and Management Symposium (NOMS), Osaka, pp. 841–844 (April 2010)Google Scholar
  18. 18.
    Boniface, M., Nasser, B., Papay, J., Phillips, S.C., Servin, A., Xiaoyu, Y., Zlatev, Z., Gogouvitis, S.V., Katsaros, G., Konstanteli, K., Kousiouris, G., Menychtas, A., Kyriazis, D.: Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds. In: Fifth International Conference on Internet and Web Applications and Services (ICIW), p. 155 (May 2010)Google Scholar
  19. 19.
    Chen, Y., Gmach, D., Hyser, C., Wang, Z., Bash, C., Hoover, C., Singhal, S.: Integrated Management of Application Performance, Power and Cooling in Data Centres. HP Laboratories (2010)Google Scholar
  20. 20.
    Bertoncini, M., Pernici, B., Salomie, I., Wesner, S.: GAMES: Green Active Management of Energy in IT Service Centres. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 238–252. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary algorithms for solving multi-objective problems. Genetic algorithms and evolutionary computation, vol. 5. Springer (2002) ISBN 0306467623Google Scholar
  22. 22.
    The Apache Software Foundation, Apache License, Version 2.0 (January 2004),
  23. 23.
    Open Source Initiative OSI - The MIT License,
  24. 24.
    Open Source Initiative OSI - The BSD License,

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tomasz Sikora
    • 1
  • George D. Magoulas
    • 1
  1. 1.Department of Computer Science and Information SystemsBirkbeck, University of LondonLondonUK

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