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Full Description
This rigorous, self-contained book describes mathematical and, in particular, stochastic and graph theoretic methods to assess the performance of complex networks and systems. It comprises three parts: the first is a review of probability theory; Part II covers the classical theory of stochastic processes (Poisson, Markov and queueing theory), which are considered to be the basic building blocks for performance evaluation studies; Part III focuses on the rapidly expanding new field of network science. This part deals with the recently obtained insight that many very different large complex networks - such as the Internet, World Wide Web, metabolic and human brain networks, utility infrastructures, social networks - evolve and behave according to general common scaling laws. This understanding is useful when assessing the end-to-end quality of Internet services and when designing robust and secure networks. Containing problems and solved solutions, the book is ideal for graduate students taking courses in performance analysis.
Contents
1. Introduction; Part I. Probability Theory: 2. Random variables; 3. Basic distributions; 4. Correlation; 5. Inequalities; 6. Limit laws; Part II. Stochastic Processes: 7. The Poisson process; 8. Renewal theory; 9. Discrete-time Markov chains; 10. Continuous-time Markov chains; 11. Applications of Markov chains; 12. Branching processes; 13. General queueing theory; 14. Queueing models; Part III. Network Science: 15. General characteristics of graphs; 16. The shortest path problem; 17. Epidemics in networks; 18. The efficiency of multicast; 19. The hopcount and weight to an anycast group; Appendix A. A summary of matrix theory; Appendix B. Solutions to problems.
