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Tutorials Program - SIGMETRICS 2001 / Performance 2001

An Introduction to Control Theory and Its Application to Computer Science

Joe Hellerstein <> and Sujay Parekh <>

Performance Management
IBM T.J. Watson Research Center
Hawthorne, New York

Dynamic management of resources is essential to building high performance systems. Often, these dynamics involve feedback loops that are difficult to design and analyze. It turns out, however, that other engineering disciplines (e.g., mechanical and electrical engineering) have good approaches for dealing with analogous problems in their field. For the most part, these approaches are based on control theory.

This tutorial provides an introduction to classical control theory (linear, deterministic, time invariant) and presents two applications to computer science. We begin with a review of continuous time, linear system theory. Included here is (a) use of the Laplace transform to describe time domain functions, (b) the final value theorem, (c) modeling with block diagrams, (d) transient response analysis, and (e) conditions for stability and oscillatory response. This material is presented in the context of a single class fluid flow model. Considered next are the basic control actions: proportional, integral, and differential control. The characteristics of these controllers are analyzed in terms of linear system theory to identify issues of stability, bias, and settling time. Root locus analysis is discussed as well. We then address control in discrete time and related issues (e.g., sampling times). We also consider system identification (determining the model for the system being controlled). An extensive example is presented of a controller for an email server. We show that root locus analysis correctly predicts where controller-induced oscillations occur. The tutorial concludes by touching on a number of topics of use in applyikng control theory to computer science. Included here are: frequency response techniques, state space (multiple input, multiple output) models, and nonlinear control.

Joseph L. Hellerstein <>

Joseph L Hellerstein is a research staff member at the IBM Thomas J Watson Research Center where he manages the systems management research department. Dr. Hellerstein received his PhD from the University of California in Los Angeles. Since then his research has addressed various aspects of managing service levels, including: predictive detection, automated diagnosis, expert systems, and the application of control theory to resource management. Dr. Hellerstein has published approximately 50 papers and an Addison-Wesley book on expert systems.

Sujay Parekh <>

Sujay Parekh received his B.S. degree in Computer Science from Cornell University in 1993, and his M.S. from University of Washington in 1996. From 1993 to 1994, he was a member of the Technical Staff at Oracle Corporation. He is currently a Research Associate at the IBM T.J. Watson Research Center while also working on his PhD at University of Washington. His research interests include adaptive algorithms, systems management and artificial intelligence.

[Last updated Wed Jan 3 2001]