Name
Abstract | ||
|---|---|---|
In this paper, following our recent developed concept of subnet model in mesh networks, we continue to investigate the characterizations of probabilistic fault tolerance for the mesh networks with faulty node. We consider two fault models: each node has deterministic or stochastic failure probability, then we study the fault tolerance of mesh networks based on our novel technique - subnet model. We derive lower bounds on the connectivity probability for mesh networks. Our study shows that mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems under deterministic or stochastic node failure probability. Comparing with deterministic node failure probability, stochastic model is close to realistic case. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/EUC.2010.115 | EUC |
Keywords | Field | DocType |
deterministic node failure probability,faulty node,stochastic processes,multiprocessor interconnection networks,probabilistic analysis,deterministic failure probability,fault tolerant computing,mesh network,subnet model,node failure probability,k-submesh,fault tolerance,connectivity probability,mesh network fault tolerance,stochastic node failure probability,fault model,probabilistic fault tolerance,stochastic failure probability,nickel,mesh networks,lower bound,stochastic model,fault tolerant,manganese | Mesh networking,Computer science,Stochastic process,Subnet,Real-time computing,Probabilistic analysis of algorithms,Fault tolerance,Stochastic modelling,Probabilistic logic,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-0-7695-4322-2 | 2 | 0.37 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaocai Wang | 1 | 5 | 2.25 |
| Taoshen Li | 2 | 19 | 14.68 |
| Jianer Chen | 3 | 2564 | 184.38 |