Comprehensive downtime prediction in next generation internet
The benchmark for the reliability quality of networks depends mainly on the accuracy and comprehensiveness of the reliability parameters. Downtime prediction of a communication system is crucial for the quality of service (QoS) offered to the end-user. Markov model enables analytical calculation of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IIUM Press
2004
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/5777/ http://irep.iium.edu.my/5777/1/COMPREHENSIVE_DOWNTIME_PREDICTION_IN_NEXTGENERATION.pdf |
| Summary: | The benchmark for the reliability quality of networks depends mainly on the accuracy and comprehensiveness of the reliability parameters. Downtime prediction of a communication system is crucial for the quality of service (QoS) offered to the end-user. Markov model enables analytical calculation of average single figure cumulative downtime over one year. The single average approach, generally does not adequately describe the wide range of services performance that is likely to be experienced in communications systems due to the random nature of the failure. Therefore, it would be more appropriate to put downtime distribution obtained from network availability models to predict the expected cumulative downtime and other performance parameters among a large number of system populations. The distribution approach provides more comprehensive information about the behavior of the individual systems. Laplace-Stieltjes transform enables analytical solution for simple network architecture, i.e. the simplex system and the parallel system. This paper uses simulation to determine reliability parameters for complex architecture such as the Multiprotocol Label Switching (MPLS) backbone planned for next-generation Internet. In addition to the single figure downtime, simulations provide other reliability parameters such as probability of zero downtime. The paper also considers the downtime distribution among a population of equally designed systems. |
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