MOTIVATION Information processing system designers need methods for the quantification of system design factors such as performance and reliability. Modern computerr communicationI’ and production line systems process complex workloads with random service demands. Probabilistic and statistical methods are commonly employed for the purpose of performance and reliability evaluation. The purpose of this book is to explore major probabilistic modeling techniques for the performance analysis of information processing systems | Queueing Networks and Markov Chains Gunter Botch Stefan Greiner Hermann de Meer Kishor S. Trivedi Copyright 1998 John Wiley Sons Inc. Print ISBN 0-471-19366-6 Online ISBN 0-471-20058-1 1 Introduction MOTIVATION Information processing system designers need methods for the quantification of system design factors such as performance and reliability. Modern com-puterT communicationF and production line systems process complex workloads with random service demands. Probabilistic and statistical methods are commonly employed for the purpose of performance and reliability evaluation. The purpose of this book is to explore major probabilistic modeling techniques for the performance analysis of information processing systems. Statistical methods are also of great importance but we refer the reader to other sources Jain91PTriv82 for this topic. Although we concentrate on performance analysis we occasionally consider reliabilityTavailabilityTand combined performance and reliability analysis. Performance measures that are commonly of interest include throughputPresource utilizationPloss probabili-tyTand delay or response time . The most direct method for performance evaluation is based on actual measurement of the system under study. HoweverP during the design phase the system is not available for such experiments and yet performance of a given design needs to be predicted to verify that it meets design requirements and to carry out necessary trade-offs. Hence abstract models are necessary for performance prediction of designs. The most popular models are based on discrete-event simulation DES . DES can be applied to almost all problems of interestPand system details to the desired degree can be captured in such simulation models. Furthermore many software packages are available that facilitate the construction and execution of DES models. 1 2 INTRODUCTION The principal drawback of DES models however is the time taken to run such models for large realistic systems .
