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| M. Balakrishnan, A. Reibman, "Reliability Models for Fault-Tolerant Private Network Applications," IEEE Transactions on Computers, vol. 43, no. 9, pp. 1039-1053, September, 1994. | |||
| BibTex | x | ||
| @article{ 10.1109/12.312113, author = {M. Balakrishnan and A. Reibman}, title = {Reliability Models for Fault-Tolerant Private Network Applications}, journal ={IEEE Transactions on Computers}, volume = {43}, number = {9}, issn = {0018-9340}, year = {1994}, pages = {1039-1053}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.312113}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Computers TI - Reliability Models for Fault-Tolerant Private Network Applications IS - 9 SN - 0018-9340 SP1039 EP1053 EPD - 1039-1053 A1 - M. Balakrishnan, A1 - A. Reibman, PY - 1994 KW - fault tolerant computing; reliability; Markov processes; state-space methods; system recovery; telephone networks; telecommunication network routing; lumped parameter networks; parameter estimation; reliability models; fault-tolerant private network applications; corporate network; leased private lines; reliability; state-space model; dynamic rerouting; repair strategies; application level; call level; approximate Markov model; lumped model; coverage parameter estimation; network simulation; time-scales. VL - 43 JA - IEEE Transactions on Computers ER - | |||
A private or corporate network connects the offices of a single large organization, such as an airline or a bank, using leased private lines. To improve the reliability of network applications, fault-tolerance can be incorporated directly into the private network. In this paper, we use a state-space model to capture the effect of dynamic rerouting and repair and investigate the effect on reliability of different repair-and-rerouting strategies at the application or call level. To reduce the potentially large state space that results, we construct an approximate Markov model with a smaller state space by lumping together similar states. The lumped model includes coverage parameters that can be estimated without considering the original model in its entirety. This allows the state-space model to be solved accurately and efficiently. We compare results of the approximation technique with results obtained by a complete simulation of the original network. We expect similar approximation techniques to be effective on models with large state spaces which obtain processes with many time-scales.
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