Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices (The Morgan Kaufmann Series in Networking)
Author:by George Varghese (Author), Jun Xu (Author)
Publisher finelybook 出版社:Morgan Kaufmann
Edition 版本:2nd edition
Publication Date 出版日期:2022-12-1
Language 语言:English
Print Length 页数:594pages
ISBN-10:0128099275
ISBN-13:9780128099278
Book Description
Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices, Second Edition takes an interdisciplinary approach to applying principles for efficient implementation of network devices, offering solutions to the problem of network implementation bottlenecks. In designing a network device, there are dozens of decisions that affect the speed with which it will perform – sometimes for better, but sometimes for worse. The book provides a complete and coherent methodology for maximizing speed while meeting network design goals. The book is uniquely focused on the seamless integration of data structures, algorithms, operating systems and hardware/software co-designs for high-performance routers/switches and network end systems.
Thoroughly updated based on courses taught by the authors over the past decade, the book lays out the bottlenecks most often encountered at four disparate levels of implementation: protocol, OS, hardware and architecture. It then develops fifteen principles key to breaking these bottlenecks, systematically applying them to bottlenecks found in end-nodes, interconnect devices and specialty functions located along the network. Later sections discuss the inherent challenges of modern cloud computing and data center networking.
- Offers techniques that address common bottlenecks of interconnect devices, including routers, bridges, gateways, endnodes, and Web servers
- Presents many practical algorithmic concepts that students and readers can work with immediately
- Revised and updated throughout to discuss the latest developments from authors’ courses, including measurement algorithmics, randomization, regular expression matching, and software-defined networking
- Includes a new, rich set of homework exercises and exam questions to facilitate classroom use
Review
Extensive revision focused on teaching the seamless integration of recent system technologies to create efficient network implementations
From the Back Cover
Network Algorithmics, Second Edition, takes an interdisciplinary approach to applying principles for efficient implementation of network devices, offering solutions to the problem of network implementation bottlenecks. In designing a network device, there are dozens of decisions that affect the speed with which it will perform ― sometimes for better, but sometimes for worse. The book provides a complete and coherent methodology for maximizing speed while meeting network design goals. It is uniquely focused on the seamless integration of data structures, algorithms, operating systems, and hardware/software co-designs for high-performance routers/switches and network end systems.
Thoroughly updated based on courses taught by the authors over the past decade, the book lays out the bottlenecks most often at four disparate levels of implementation: protocol, OS, hardware, and architecture. It then develops fifteen principles key to breaking these bottlenecks, systematically applying them to bottlenecks found in endnodes, interconnect devices, and specialty functions located along the network. Later sections discuss the inherent challenges of modern cloud computing and data center networking. This immensely practical, clearly presented information will benefit anyone involved with network implementation, as well as students who have made this work their goal.
About the Author
Jun Xu has been a Professor in the School of Computer Science at Georgia Tech since 2000. He has worked on the design and analysis of network algorithmics for over two decades. He was instrumental in introducing randomization to the design of network algorithmics. His network algorithmics research jointly with his former students has garnered Best Student Paper Awards in conferences such as ACM Sigmetrics. In the past three years, he has ventured into an emerging research field called big data algorithmics that includes topics such as locality sensitive hashing (LSH) techniques for supporting similarity search in database and machine learning applications, and follows the same guiding principle of network algorithmics: combining algorithmic thinking with systems thinking. He has been an ACM Distinguished Scientist since 2010.
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