Performance Modeling and Engineering

With the fast development of networking and software technologies, information processing infrastructure and applications have been growing at an impressive rate in both size and complexity, to such a degree that the design and development of high performance and scalable data processing systems and networks have become an ever-challenging issue. As a result, the use of performance modeling and m- surementtechniquesas a critical step in designand developmenthas becomea c- mon practice. Research and developmenton methodologyand tools of performance modeling and performance engineering have gained further importance in order to improve the performance and scalability of these systems. Since the seminal work of A. K. Erlang almost a century ago on the mod- ing of telephone traf c, performance modeling and measurement have grown into a discipline and have been evolving both in their methodologies and in the areas in which they are applied. It is noteworthy that various mathematical techniques were brought into this eld, including in particular probability theory, stochastic processes, statistics, complex analysis, stochastic calculus, stochastic comparison, optimization, control theory, machine learning and information theory. The app- cation areas extended from telephone networks to Internet and Web applications, from computer systems to computer software, from manufacturing systems to s- ply chain, from call centers to workforce management.

From the reviews: "This is a nice reference book that provides an essential set of literature of available methods, models, and techniques for the study of performance modeling and analysis. It presents seven select topics related to performance modeling and engineering, while paying specific attention to disciplines involving computer, communication, and network technologies; the contributors belong to both academia and the industry. … this book is very valuable as a cohesive, collected summary for readers interested in performance modeling." (Srini Ramaswamy and Sithu D. Sudarsan, ACM Computing Reviews, January, 2009)