Self-managing the Performance of Distributed Computing Systems – An Expert Control Solution

  • G. Ravi Kumar
  • C. Muthusamy
  • A. Vinaya Babu
  • Raj N. Marndi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


The advent of internet and cloud computing trends is increasing the complexity of IT Infrastructures very rapidly. The non-functional requirements availability and performance are becoming increasingly important. IT Service providers constantly facing challenges to meet the performance related SLAs defined with Enterprise Customers. To meet such demands, IT environments are built with self-managing capabilities. Autonomic Computing has emerged to support self-managing features using the feedback control systems. There are investigations in using control systems in different areas of computing such as computer networking, database systems, data centers and distributed computing systems in enterprise and cloud environments. We observe that there is a need for an end-to-end solution starting from design and modeling of the software, deploying and runtime management that enables in building self-managing. In this paper we propose an end-to-end Expert Control System Solution for Distributed Computing Systems and discuss its application in Java Enterprise environments.


Fuzzy Control Cloud Environment Distribute Computing System Autonomic Computing Intelligent Controller 


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  1. 1.
    Akerkar, R., Lingras, P.: An Intelligent Web: Theory and Practice, 1st edn. Johns and Bartlett, Boston (2008)Google Scholar
  2. 2.
    Adaptive Systems Research,
  3. 3.
    Nami, M.R., Sharifi, H.: Autonomic Computing: A New Approach. AMS (2007)Google Scholar
  4. 4.
    Russell, L.W., Morgan, S.P., Chron, E.G.: Clockwork A new movement in autonomic systems. IBM Systems Journal 42(1), 77–84 (2003)CrossRefGoogle Scholar
  5. 5.
    Gullapalli, R.K., Muthusamy, C., Vinaya Babu, A.: Control Systems application in Java based Enterprise and Cloud Environments – A Survey. IJACSA 2(8) (2011)Google Scholar
  6. 6.
    Astrom, K.J., Wittenmark, B.: Adaptive Control. Pearson Education. Control Engineering (2009), (accessed March 30, 2012)
  7. 7.
    Shiy, D., Tsungz, F.: Modelling and diagnosis of feedback-controlled processes using dynamic PCA and neural networks. INT Journal of Prod. and Res. 41(2) (2003)Google Scholar
  8. 8.
    Ryu, S., Cho, C.: PI-PD-controller for robust and adaptive queue management for supporting TCP congestion control, pp. 132–139, 18-22 (2004)Google Scholar
  9. 9.
    Kang, K.-D., Oh, J., Son, S.H.: Chronos: Feedback Control of a Real Database System Performance. In: Real Time Systems Symposium, pp. 267–276 (2007)Google Scholar
  10. 10.
    Zhu, X., Uysal, M., Zhikui, Wang, Singhal, S., Merchant, A., Padala, P., Shin, K.: What Does Control Theory Bring to Systems Research? ACM SIGOPS Operating Systems Review 43(1) (2009)Google Scholar
  11. 11.
    Li, B., Nahrstedt, K.: Impact of Control Theory on QoS Adaptation in Distributed Middleware Systems. In: American Control Conference, vol. 4, pp. 2987–2991 (2001)Google Scholar
  12. 12.
    Zhang, L., Ardagna, D.: SLA Based Profit Optimization in Autonomic Computing Systems. In: ICSOC (2004)Google Scholar
  13. 13.
    Middleware, (accessed March 30, 2012)
  14. 14.
  15. 15.
    Application Server, (accessed March 30, 2012)
  16. 16.
    Klein, C., et al.: A Survey of Context Adaptation in Autonomic Computing. In: ICAS (2008)Google Scholar
  17. 17.
    Lu, Y., Abdelzaher, T., Tao, G.: Direct Adaptive Control of A Web Cache System. In: American Control Conference, Denver, Colorado (2003)Google Scholar
  18. 18.
    Robertsson, A., Wittenmark, B., Kihl, M., Andersson, M.: Design and evaluation of load control in web server systems. In: IEEE American Control ConferenceGoogle Scholar
  19. 19.
    Abdelzaher, T., Lu, Y., Zhana, R., Henriksson, D.: Practical Application of Control Theory to Web Services. In: American Control Conference (2004)Google Scholar
  20. 20.
    Zhang, Y., Qu, W., Liu, A.: Adaptive Self-Configuration Architecture for J2EE-based Middleware. In: HICSS 2006, vol. 9 (2006)Google Scholar
  21. 21.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A.: A Study of Intelligent Controllers Application in Distributed Systems. INDJCSE 2(4)Google Scholar
  22. 22.
    Lama, P., Zhou, X.: Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for End-to-end Delay Guarantee. In: IEEE International Symposium on Modeling, Analysis and Simulation of Computer Telecommunication Systems (2010)Google Scholar
  23. 23.
    Patikirikorala, T., Colman, A.: Feedback controllers in the cloud, Swinburne University (2011)Google Scholar
  24. 24.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A., Marndi, R.N.: Autonomic Database driver – An Adaptive Control Solution. In: ICITEC, BangaloreGoogle Scholar
  25. 25.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A., Marndi, R.N.: A Feedback Control Solution in Improving Database Driver Caching. IJEST 3(7) (2011)Google Scholar
  26. 26.
    USE UML Tool, (accessed December 25, 2011)
  27. 27.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A.: Design and Modeling Autonomic aware Software in UML – A Control System Solution. In: ICCIT, Tirupati (2012)Google Scholar
  28. 28.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A.: Throughput Regulation of Messaging Servers – An Intelligent Control Solution. IJACSA 3(1) (2012)Google Scholar
  29. 29.
    Ravi Kumar, G., Muthusamy, C., Vinaya Babu, A.: Self-Managing Message Throughput in Enterprise Messaging Servers. IJARCSSE 2(4) (2012)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • G. Ravi Kumar
    • 1
    • 2
  • C. Muthusamy
    • 3
  • A. Vinaya Babu
    • 4
  • Raj N. Marndi
    • 1
  1. 1.HPBangaloreIndia
  2. 2.JNTUHHyderabadIndia
  3. 3.YahooBangaloreIndia
  4. 4.JNTUH Coll of EnggHyderabadIndia

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