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.>

Reliability analysis of a computer system for a data collection application is described. The system is built around a central minicomputer platform that serves as a database manager and transaction processor. The components of the platform include the processor, memory, I/O cards, power supply, and the disks and disk controller. Users have workstations that are connected to the central processor through a local area network (LAN). The central processor also has connections to different test and data collection points located in a wide area network (WAN). The analysis identified the disk subsystems as a potential reliability bottleneck, in the system. Disk mirroring was identified as a potential reliability improvement.>

Continuous-time Markov chains (CTMC) are widely used mathematical models. Reliability models, queueing networks, and inventory models all require transient solutions of CTMC. The cost of CTMC transient solution increases with size, stiffness, and mission time. To eliminate stiffness and reduce the cost of solution, approximation techniques have been proposed. In this paper, we describe a software package for the specification and solution of stiff CTMC. As an interface, we use a language for the description of Markov chains. The language also provides facilities for controlling the solution procedure. Both exact and approximate solution techniques are provided. To conclude the paper, we use several examples to show the use of our specification language and the utility of our approximation technique. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

To provide fault tolerance and improve system reliability and performance, a class of fault-tolerant multistage interconnection networks, called augmented shuffle-exchange networks (ASENs) has been proposed. ASENs are gracefully degradable; although an individual component failure reduces ASEN performance, it does not cause a total network failure. The purpose of this work is to analyze how these component failures affect ASEN performance. A key step in performance and reliability modeling is the choice of an appropriate metric for analysis. Because network bandwidth can be an inadequate performance measure when fault are present. The authors consider other network performance measures, including the interreference time distribution for individual outputs (memories).>

Traditional evaluation techniques for multiprocessor systems use Markov chains and Markov reward models to compute measures such as mean time to failure, reliability, performance, and performability. In this paper, we discuss the extension of Markov models to include parametric sensitivity analysis. Using such analysis, we can guide system optimization, identify parts of a system model sensitive to error, and find system reliability and performability bottlenecks.