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Building Scalable Network Services: Theory and Practice
 
 
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Building Scalable Network Services: Theory and Practice [Hardcover]

Cheng Jin (Author), Sugih Jamin (Author), Danny Raz (Author), Yuval Shavitt (Author)

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Book Description

November 30, 2003
Building Scalable Network Services: Theory and Practice is on building scalable network services on the Internet or in a network service provider's network. The focus is on network services that are provided through the use of a set of servers. The authors present a tiered scalable network service model and evaluate various services within this architecture. The service model simplifies design tasks by implementing only the most basic functionalities at lower tiers where the need for scalability dominates functionality. The book includes a number of theoretical results that are practical and applicable to real networks, such as building network-wide measurement, monitoring services, and strategies for building better P2P networks. Various issues in scalable system design and placement algorithms for service nodes are discussed. Using existing network services as well as potentially new but useful services as examples, the authors formalize the problem of placing service nodes and provide practical solutions for them.

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Inside This Book (learn more)
First Sentence:
Today, computer networks have become an inseparable component of daily computing. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
network distance service, traceroute gateways, client download time, cache location problem, latency estimation, scalable network services, greedy placement, constrained mirror placement, reduced mesh, latency estimates, policy routing, latency information, closest mirror, vertex cover size, candidate hosts, backbone routers, client clusters, placement algorithm, placement solution, measurement infrastructure, random placement, virtual topology, random topologies, cache locations, network diameter
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Bell Labs, Amnesty International
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