Self-* and P2P for Network Management [electronic resource] :Design Principles and Case Studies / by Clarissa Cassales Marquezan, Lisandro Zambenedetti Granville.
by Marquezan, Clarissa Cassales [author.]; Granville, Lisandro Zambenedetti [author.]; SpringerLink (Online service).
Material type:
BookSeries: SpringerBriefs in Computer Science: Publisher: London : Springer London : 2012.Description: XIII, 105 p. 22 illus. online resource.ISBN: 9781447142010.Subject(s): Computer science | Computer Communication Networks | Computer Science | Computer Communication NetworksDDC classification: 004.6 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| MAIN LIBRARY | TK5105.5-5105.9 (Browse shelf) | Available |
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Foreword -- Preface -- Introduction -- State of the Art -- Principles of the Self-* P2P Design -- Case Study I: Reliability of Monitoring Platforms -- Case Study II: Resource Management of Network Virtualization -- Results Discussion -- Conclusions.
The network management community has been pushed towards the design of alternative management approaches able to support heterogeneity, scalability, reliability, and minor human intervention. The employment of self-* properties and Peer-To-Peer (P2P) are seen as promising alternatives, able to provide the sophisticated solutions required. Despite being developed in parallel, and with minor direct connections perceived between them, self-* properties and P2P can be used concurrently. In Self-* and P2P for Network Management: Design Principles and Case Studies, the authors explore the issues behind the joint use of self-* properties and P2P, and present: a survey relating autonomic computing and self-* properties, P2P, and network and service management; the design of solutions that explore parallel and cooperative behavior of management peers; the change in angle of network management solution development from APIs, protocols, architectures, and frameworks to the design of management algorithms.
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