This useful volume adopts a balanced approach between technology and mathematical modeling in computer networks, covering such topics as switching elements and fabrics, Ethernet, and ALOHA design. Examples at the end of each chapter provide ample material for practice.

Maintains a balanced approach between technology and mathematical modeling for classroom use Features new and revised covering the advances in the field since the original publication Includes a host of classroom material for students and instructors Request lecturer material: sn.pub/lecturer-material

Autorentext

Thomas G. Robertazzi received the Ph.D from Princeton University, Princeton, NJ, in 1981 and the B.E.E. from the Cooper Union, New York, NY in 1977. A Fellow of the IEEE, he is presently a Professor in the Dept. of Electrical and Computer Engineering at Stony Brook University, Stony Brook N.Y. He has published extensively in the areas of parallel processing and grid scheduling, ad hoc radio networks, telecommunications network planning, switching , queueing and Petri networks. Prof. Robertazzi has also authored, co-authored or edited six books in the areas of networking, performance evaluation, scheduling and network planning. For eleven years Prof. Robertazzi was the faculty director of the Stony Brook Living Learning Center in Science and Engineering. Since 2008 he has been co-chair of the Stony Brook University Senate Research Committee. Li Shi received his Ph.D. degree from Department of Electrical and Computer Engineering at Stony Brook University, Stony Brook, NY, in 2016. Previous, he received his B.E. degree in electrical and computer engineering from Shanghai Jiao Tong University, Shanghai, China, in 2010. He is working at Snap Inc, Venice, CA. His research interests include task scheduling and resource allocation in data centers, cloud computing, data center network, software defined network, etc.

Inhalt
Introduction.- A Tour through Networking and Computing.- Fundamental Stochastic Models.- Queueing Models.- Fundamental Deterministic Algorithms.- Divisible Load Modeling for Grids.- Amdahl's and Related Laws.- Machine Learning for Networking.- Conclusion.