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Using Control Theory to Achieve Service Level Objectives In Performance Management

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Abstract

A widely used approach to achieving service level objectives for a software system (e.g., an email server) is to add a controller that manipulates the target system's tuning parameters. We describe a methodology for designing such controllers for software systems that builds on classical control theory. The classical approach proceeds in two steps: system identification and controller design. In system identification, we construct mathematical models of the target system. Traditionally, this has been based on a first-principles approach, using detailed knowledge of the target system. Such models can be complex and difficult to build, validate, use, and maintain. In our methodology, a statistical (ARMA) model is fit to historical measurements of the target being controlled. These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems. When applied to a Lotus Notes groupware server, we obtain model-fits with R2 no lower than 75% and as high as 98%. In controller design, an analysis of the models leads to a controller that will achieve the service level objectives. We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target. The objective is to maintain a reference queue length. Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system's response. Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective. We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis. This indicates that the control theoretic analysis is sufficient to select controller parameters that meet the desired goals, and the need for simulations is reduced.

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References

  • Aman, J., Eilert, C. K., Emmes, D., Yocom, P., and Dillenberger, D. 1997. Adaptive algorithms for managing a distributed data processing workload, IBM Systems Journal 36(2): 242-283.

    Google Scholar 

  • Basseville, M., and Nikiforov, I. 1993. Detection of Abrupt Changes: Theory and Applications. Prentice Hall.

  • Benmohamed, L., and Meerkov, S. M. 1993. Feedback control of congestion in packet switching networks: the case of a single congested node. IEEE Transactions on Networking 1: 693-708.

    Google Scholar 

  • Bigus, J. P. 1993. Adaptive Operating System Control using Neural Networks. Phd Thesis, Lehigh University.

  • Chiu, D.-M., and Jain, R. 1989. Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Computer Networks and ISDN systems 17.

  • Essick, R. B. 1990. An event-based fair share scheduler. In Proceedings of ACM USENIX, pp. 147-161.

  • Hunt, G., Goldszmidt, G., King, R., and Mukherjee, R. 1998. Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, April 1998 (published as Computer Networks and ISDN Systems, Vol. 30, pp. 347-357.

    Google Scholar 

  • Iyenger, A., Challenger, J., Dias, D., and Dantizig, P. 2000. High-performance web site design techniques, IEEE Internet Computing 4: 17-26.

    Google Scholar 

  • Keshav, S. 1991. Proceedings of ACM SIGCOMM 91 (September 1991), Zurich, Switzerland (published as Computer Communication Review, Vol. 21,No. 4, pp. 3-15).

    Google Scholar 

  • Li, B., and Nahrstedt, K. 1990. Control-based middleware framework for quality of service applications. IEEE Journal on Selected Areas in Communication, pp. 1632-1650.

  • Lu, Y., Saxena, A., and Abdelzaher, T. F. 2001. Differentiated caching services: A control-theoretic approach. In International Conference on Distributed Computing Systems, Phoenix, Arizona.

  • Mascolo, S., Cavendish, D., and Gerla, M. 1996. ATM rate based congestion control using a smith predictor: an EPRCA implementation. In Proceedings of IEEE INFOCOM'96, pp. 569-576.

  • Mascolo, S. 1999. Classical control theory for congestion avoidance in high-speed internet. In Proceedings of the 38th Conference on Decision & Control, pp. 2709-2714, Phoenix, Arizona.

  • Ogata, K. 1997. Modern Control Engineering. Prentice Hall, 3rd edn.

  • Shor, M. H., Li, K., Walpole, J., Steere, D. C., and Pu, C. 2000. Application of control theory to modeling and analysis of computer systems. In Proceedings of the Japan-USA-Vietnam RESCCE Workshop, Ho Chi Mihn City, Vietnam.

  • Steere, D. C., Goel, A., Gruenberg, J., McNamee, D., Pu, C., and Walpole, J. 1999. A feedback-driven proportion allocator for real-rate scheduling. In Proceedings of Operating Systems Design and Implementation (OSDI), pp. 145-158.

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Parekh, S., Gandhi, N., Hellerstein, J. et al. Using Control Theory to Achieve Service Level Objectives In Performance Management. Real-Time Systems 23, 127–141 (2002). https://doi.org/10.1023/A:1015350520175

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  • DOI: https://doi.org/10.1023/A:1015350520175

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