We study call admission rates in a linear communication network with each call identified by an arrival time, duration, bandwidth requirement, and origin-destination pair. Network links all have the same bandwidth capacity, and a call can be admitted only if there is sufficient bandwidth available on every link along the call's path. Calls not admitted are held in a queue, in contrast to the protocol of loss networks. We determine maximum admission rates (capacities) under greedy call allocation rules such as First Fit and Best Fit for several baseline models and prove that the natural necessary condition for stability is sufficient. We establish the close connections between our new problems and the classic problems of bin packing and interval packing. In view of these connections, it is surprising to find that Best Fit allocation policies are inferior to First Fit policies in the models studied.

This paper is devoted to the analysis of a fluid queue with a buffer content that varies linearly during periods that are governed by a three-state semi-Markov process. Two cases are being distinguished: (i) two upward slopes and one downward slope, and (ii) one upward slope and two downward slopes. In both cases, at least one of the period distributions is allowed to be completely general. We obtain exact results for the buffer content distribution, the busy period distribution, and the distribution of the maximal buffer content during a busy period. The results are obtained by establishing relations between the fluid queues and ordinary queues with instantaneous input and by using level crossing theory.

In this paper we consider an infinite buffer fluid model whose input is driven by independent semi-Markov processes. The output capacity of the buffer is a constant. We derive upper and lower bounds for the limiting distribution of the stationary buffer content process. We discuss examples and applications where the results can be used to determine bounds on the loss probability in telecommunication networks.

Consider a system consisting of n components in series, subject to failures, with one repairman. No preemption is permitted during a repair. The repairman has a list of components and when he becomes available he repairs the failed component which is closest to the top of the list. The goal is to minimize the mean time until all the components operate—the mean down time. For systems consisting of two components, we assume that the operation and repair times are generally distributed and that the operation times are stochastically ordered. For systems consisting of n components we assume exponentially distributed operation times with rate λi and fixed repair times. We prove that lists in which the components are ordered in an increasing order of their expected operation times are optimal.

A recent interesting paper (Cooper, Niu, and Srinivasan [2]) shows how for some cyclic production systems reducing setup times can surprisingly increase work in process. There the authors show how in these situations the introduction of forced idle time can be used to optimize system performance. Here we show that introducing forced idle time at different points during the production cycle can further improve performance in these situations and also in situations where the suggestions from [2] yield no improvement.

Components which have bathtub shapes are reasonable candidates for burn-in in the sense that if these components are exposed to standard or elevated operating conditions for a short period of time, they tend to improve. In this paper we study the principle which states that burn-in should occur at or before the point at which a bathtub-shaped failure function starts increasing. To this end we develop three sign change results which characterize bathtub-shaped failure rates. These results are used to show that if a failure rate function is bathtub-shaped, then various other related functions have bathtub or upside-down bathtub shape. Finally, we give a framework for determining when the above principle holds.

The GI/M/1 queueing system was long ago studied by considering the embedded discrete-time Markov chain at arrival epochs and was proved to have remarkably simple product-form stationary distributions both at arrival epochs and in continuous time. Although this method works well also in several variants of this system, it breaks down when customers arrive in batches. The resulting GIX/M/1 system has no tractable stationary distribution. In this paper we use a recent result of Miyazawa and Taylor (1997) to obtain a stochastic upper bound for the GIX/M/1 system. We also introduce a class of continuous-time Markov chains which are related to the original GIX/M/1 embedded Markov chain that are shown to have modified geometric stationary distributions. We use them to obtain easily computed stochastic lower bounds for the GIX/M/1 system. Numerical studies demonstrate the quality of these bounds.