Multicast Communication (e-bog) af Zitterbart, Martina
Zitterbart, Martina (forfatter)

Multicast Communication e-bog

436,85 DKK (inkl. moms 546,06 DKK)
The Internet is quickly becoming the backbone for the worldwide information society of the future. Point-to-point communication dominates the network today, however, group communication--using multicast technology--will rapidly gain importance as digital, audio, and video transmission, push technology for the Web, and distribution of software updates to millions of end users become ubiquitous....
E-bog 436,85 DKK
Forfattere Zitterbart, Martina (forfatter)
Udgivet 16 juni 2000
Længde 349 sider
Genrer Business and Management
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9780080497341
The Internet is quickly becoming the backbone for the worldwide information society of the future. Point-to-point communication dominates the network today, however, group communication--using multicast technology--will rapidly gain importance as digital, audio, and video transmission, push technology for the Web, and distribution of software updates to millions of end users become ubiquitous. Multicast Communication: Protocols and Applications explains how and why multicast technology is the key to this transition. This book provides network engineers, designers, and administrators with the underlying concepts as well as a complete and detailed description of the protocols and algorithms that comprise multicast.* Presents information on the entire range of multicast protocols, including, PIM-SM, MFTP, and PGM and explains their mechanisms, trade-offs, and solid approaches to their implementation* Provides an in-depth examination of Quality of Service concepts, including: RSVP, ST2, IntServ, and DiffServ* Discusses group address allocation and scoping* Discusses multicast implementation in ATM networks* Builds a solid understanding of the Mbone and surveys the successes and current limitations of real multicast applications on the Internet such as videoconferencing, whiteboards, and distance learning