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Stephen S. Lavenberg

Bio: Stephen S. Lavenberg is an academic researcher from IBM. The author has contributed to research in topics: Queueing theory & Mean value analysis. The author has an hindex of 6, co-authored 6 publications receiving 1353 citations.

Papers
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Journal ArticleDOI
Martin Reiser1, Stephen S. Lavenberg1
TL;DR: It is shown that mean queue sizes, mean waiting times, and throughputs in closed multiple-chain queuing networks which have product-form solution can be computed recursively without computing product terms and normalization constants.
Abstract: It is shown that mean queue sizes, mean waiting times, and throughputs in closed multiple-chain queuing networks which have product-form solution can be computed recursively without computing product terms and normalization constants. The resulting computational procedures have improved properties (avoidance of numerical problems and, in some cases, fewer operations) compared to previous algorithms. Furthermore, the new algorithms have a physically meaningful interpretation which provides the basis for heuristic extensions that allow the approximate solution of networks with a very large number of closed chains, and which is shown to be asymptotically valid for large chain populations.

1,192 citations

Journal ArticleDOI
TL;DR: A computational algorithm is developed for closed multichain product-form queueing networks based on a recursion that is quite different in form from the recursion used in the well-known mean value analysis (MVA) algorithm and has quite different computational and storage costs.
Abstract: A computational algorithm is developed for closed multichain product-form queueing networks. For networks that consist of only single-server fixed rate and infinite-server service centers, it involves only mean performance measures. The algorithm, called mean value analysis by chain (MVAC), is based on a recursion that is quite different in form from the recursion used in the well-known mean value analysis (MVA) algorithm and has quite different computational and storage costs. For networks with few service centers and many chains, MVAC typically has much lower costs than MVA, although it becomes more costly than MVA as the number of service centers increases. The MVAC recursion is similar in structure to a recursion involving normalizing constants that was derived by A.E. Conway and N.D. Georganas (1986). That recursion formed the basis for their recursion by chain (RECAL) algorithm for computing the normalizing constant and from it the mean performance measures. The computational and storage costs for MVAC are shown to be similar to those for RECAL. >

41 citations


Cited by
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Journal ArticleDOI
TL;DR: Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concurrency control.
Abstract: Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concurrency control. Virtual time provides a flexible abstraction of real time in much the same way that virtual memory provides an abstraction of real memory. It is implemented using the Time Warp mechanism, a synchronization protocol distinguished by its reliance on lookahead-rollback, and by its implementation of rollback via antimessages.

2,280 citations

Journal ArticleDOI
TL;DR: This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes and introduces interesting synchronization problems that are at the heart of the PDES problem.
Abstract: Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

1,615 citations

Book
01 Jan 2000
TL;DR: The article gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems, with particular emphasis on synchronization (also called time management) algorithms as well as data distribution techniques.
Abstract: Originating from basic research conducted in the 1970's and 1980's, the parallel and distributed simulation field has matured over the last few decades. Today, operational systems have been fielded for applications such as military training, analysis of communication networks, and air traffic control systems, to mention a few. The article gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems. Particular emphasis is placed on synchronization (also called time management) algorithms as well as data distribution techniques.

1,217 citations

Proceedings ArticleDOI
01 Oct 1989
TL;DR: This tutorial surveys the state of the art in executing discrete event simulation programs on a parallel computer, and focuses attention on asynchronous simulation programs where few events occur at any single point in simulated time.
Abstract: This tutorial surveys the state of the art in executing discrete event simulation programs on a parallel computer. Specifically, we will focus attention on asynchronous simulation programs where few events occur at any single point in simulated time, necessitating the concurrent execution of events occurring at different points in time. We first describe the parallel discrete event simulation problem, and examine why it so difficult. We review several simulation strategies that have been proposed, and discuss the underlying ideas on which they are based. We critique existing approaches in order to clarify their respective strengths and weaknesses.

1,201 citations

Journal ArticleDOI
TL;DR: The focus of this work is on the theory of distributed discrete-event simulation, which may provide better performance by partitioning the simulation among the component processors.
Abstract: Traditional discrete-event simulations employ an inherently sequential algorithm. In practice, simulations of large systems are limited by this sequentiality, because only a modest number of events can be simulated. Distributed discrete-event simulation (carried out on a network of processors with asynchronous message-communicating capabilities) is proposed as an alternative; it may provide better performance by partitioning the simulation among the component processors. The basic distributed simulation scheme, which uses time encoding, is described. Its major shortcoming is a possibility of deadlock. Several techniques for deadlock avoidance and deadlock detection are suggested. The focus of this work is on the theory of distributed discrete-event simulation.

968 citations