Networking and Computation: Technology, Modeling and Performance - Tapa blanda

Acerca del autor

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.

De la contraportada

This revised textbook, intended for use in undergraduate/graduate courses on computer networking, computer systems/architecture and performance evaluation, presents a host of new and revised content and ancillaries. This text presents a balanced approach between technology and mathematical modeling. It covers networking algorithms (routing, error codes, protocol verification, line codes, network coding and quantum encryption) and analysis (probability for networking with technological examples, queueing models, divisible load scheduling theory and Amdahl’s Law). There is also a tutorial chapter providing insights into machine learning for networking, the cutting edge of networking technology.

• Maintains a balanced approach between technology and mathematical modeling
• Features new and revised content covering the latest advances in the field since the original publication
• Includes a host of classroom material for students and instructors