Network reliability and algebraic structures
Network reliability and algebraic structures
Communications of the ACM
Freenet: a distributed anonymous information storage and retrieval system
International workshop on Designing privacy enhancing technologies: design issues in anonymity and unobservability
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Building Low-Diameter P2P Networks
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
On the characterization of the domination of a diameter-constrained network reliability model
Discrete Applied Mathematics - Special issue: Traces of the Latin American conference on combinatorics, graphs and applications: a selection of papers from LACGA 2004, Santiago, Chile
Push-pull incentive-based P2P live media streaming system
WSEAS TRANSACTIONS on COMMUNICATIONS
International Journal of Metaheuristics
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In this paper we provide a summary of results and applications pertaining a Diameter-constrained Network reliability model. Classical network reliability models measure the probability that there exist end-to-end paths between network nodes, not taking into account the length of these paths. For many applications this is inadequate because the connection will only be established or attain the required quality if the distance between the connecting nodes does not exceed a given value. The Diameter-constrained reliability of a network (DCR) introduced recently considers not only the underlying topology, but also imposes a bound on the diameter, which is the maximum distance between the nodes of the network. We present a synopsis of the known results and applications of the DCR for networks that can either be modeled by directed as well as undirected graphs. Moreover important combinatorial and computational properties of this reliability measure are discussed. As the DCR subsumes the classical reliability measure (i.e., where no distance constraints are imposed on the paths connecting the nodes), as a by-product we prove well-known classical results.