In recent years, many approximate methods have been developed for analyzing queueing models of complex computer systems. These ad hoc methods usually focus on specific aspects of system operation, and appear to be different from one another, making it difficult to see the underlying principles of model development, to understand the relationship between different models of the same system, or to apply the existing methods to new situations. This book presents the first systematic study of approximation methods in queueing network modeling and of the way these methods are developed.Metamodeling identifies the underlying modeling process and provides tools and techniques for model development that will allow students and researchers to sort through the many different methods, understand them, and apply them to new problems. Using the metamodeling characterization, the book surveys and classifies a large number of approximation methods, catalogs a large number of useful model transformations, characterizes iterative solution procedures and gives theorems for proofs of convergence. This work has led to several other significant results, most notably: an approximation that works well for systems containing semaphores that serialize processes; and the discovery of multiple stable operating points for systems in which there are processes at several priority levels.Contents: Queueing Network Models of Computer Systems; The Structure of the Modeling Process; Behavior Sequence Transformations and Models with Shadow Servers; State Space Transformations; Consistent Solution, Iteration, and Convergence.Subhash Chandra Agrawal is R&D Project Manager with BGS Systems, Inc., Waltham, Massachusetts. This book inaugurates the MIT Press series in Computer Systems (Research Reports and Notes), edited by Herb Schwetman.