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Multi-Layer Network Performance and Reliability Analysis

Multi-Layer Network Performance and Reliability Analysis

Kostas N. Oikonomou, Rakesh K. Sinha, Robert D. Doverspike
Copyright: © 2010 |Pages: 27
ISBN13: 9781605669861|ISBN10: 1605669865|EISBN13: 9781605669878
DOI: 10.4018/978-1-60566-986-1.ch014
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MLA

Oikonomou, Kostas N., et al. "Multi-Layer Network Performance and Reliability Analysis." Networking and Telecommunications: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2010, pp. 167-193. https://doi.org/10.4018/978-1-60566-986-1.ch014

APA

Oikonomou, K. N., Sinha, R. K., & Doverspike, R. D. (2010). Multi-Layer Network Performance and Reliability Analysis. In I. Management Association (Ed.), Networking and Telecommunications: Concepts, Methodologies, Tools, and Applications (pp. 167-193). IGI Global. https://doi.org/10.4018/978-1-60566-986-1.ch014

Chicago

Oikonomou, Kostas N., Rakesh K. Sinha, and Robert D. Doverspike. "Multi-Layer Network Performance and Reliability Analysis." In Networking and Telecommunications: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 167-193. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-986-1.ch014

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Abstract

The authors describe a methodology for evaluating the performability (combined performance and reliability) of large communications networks. Networks are represented by a 4-level hierarchical model, consisting of traffic matrix, network graph, “components” representing failure modes, and reliability information. Network states are assignments of modes to the network components, which usually represent network elements and their key modules, although they can be more abstract. The components can be binary or multi-modal, and each of their failure modes may change a set of attributes of the graph (e.g. the capacity or cost of a link). Their methodology also captures the effect of automatic restoration against network failures by including two common rerouting methods. To compute network performability measures, including upper and lower bounds on their cumulative distribution functions, we augment existing probabilistic state-space generation algorithms with our new “hybrid” algorithm. To characterize the network failures of highest impact, we compute the Pareto boundaries of the network’s risk space. The authors have developed a network analysis tool called nperf that embodies this methodology. To illustrate the methodology and the practicality of the tool, they describe a performability analysis of three design alternatives for a large commercial IP backbone network.

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