A discrete-event simulation tool for the analysis of simultaneous events

Peschlow, Patrick; Martini, Peter

doi:10.4108/nstools.2007.2019

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The topic of event simultaneity and ordering in parallel simulations has been discussed in various contexts over the span of decades [13][14][15][16][17][18][19]22]. In [14], the topic of distributed and physical clocks, causal ordering conditions and happens-before relations is discussed but notes that the included theory makes no requirement about the ordering of incomparable or simultaneous events.…”

Section: Related Workmentioning

confidence: 99%

“…They also briefly argue how different permutations of otherwise incomparable events should be considered and will inherently affect the outcomes of future events but do not elaborate on a solution to effectively explore this space. Similarly, in [15][16][17] the authors note the importance of discovering other potential outcomes of simultaneous event orderings and propose a PDES branching mechanism tool that facilitates this.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unbiased Deterministic Total Ordering of Parallel Simulations with Simultaneous Events

McGlohon,

Carothers

2021

Preprint

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In the area of discrete event simulation (DES), event simultaneity occurs when any two events are scheduled to happen at the same point in simulated time. Simulation determinism is the expectation that the same semantically configured simulation will be guaranteed to repeatedly reproduce identical results. Since events in DES are the sole mechanism for state change, ensuring consistent real-time event processing order is crucial to maintaining determinism. This is synonymous with finding a consistent total ordering of events.In this work, we extend the concept of virtual time to utilize an arbitrary-length series of tie-breaking values to preserve determinism in parallel, optimistically executed simulations without imposing additional bias influencing the ordering of otherwise incomparable events. Furthermore, by changing the core pseudorandom number generator seed at initialization, different orderings of events incomparable by standard virtual time can be observed, allowing for fair probing of other potential simulation outcomes. We implement and evaluate this extended definition of virtual time in the Rensselaer Optimistic Simulation System (ROSS) with three simulation models and discuss the importance of deterministic event ordering given the existence of event ties.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Unbiased Deterministic Total Ordering of Parallel Simulations with Simultaneous Events

McGlohon,

Carothers

2021

Preprint

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“…This approach is not new in simulation. Some examples of its use include (Peschlow and Martini 2007) (Hybinette and Fujimoto 1997) (Hybinette and Fujimoto 2002). In this paper we don't go into the details of how the simulation is forked.…”

Section: Related Workmentioning

confidence: 99%

An Abstract Discrete-Event Simulator Considering Input with Uncertainty

2017

TMS/DEVS Symposium on Theory of Modeling &Amp; Simulation (TMS/DEVS 2017)

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A timeline in Discrete-Event Simulation (DES) is a sequence of events defined in a numerable subset of R +. Discrete-Event Modeling and Simulation try to reproduce the behaviour of real-world experiments. Nevertheless, measuring the experimental data for science and engineering (which is later used for modeling and simulation) introduces the need for uncertainty quantifications. In modeling of Continuous Systems, numerous tools have been defined to propagate uncertainty from the input to the output results. Nonetheless, these tools cannot be applied to the study of propagation of uncertainty in DES. In a previous study, we presented a method for propagating uncertainty for subset of DES models. Here, we introduce a generalization of the previous method that can be used for simulating any DES model. The method defines a new Abstract Simulator for the Discrete-Events System Specification (DEVS). This new Simulator provides the propagation of uncertainty from input to resulting trajectories.

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“…This approach is not new in simulation. Some examples of its use include [17] where forking (or branching) is proposed for studying simultaneous events in DES. Other authors [12,13,11] propose the use of forking (or cloning) in the context of interactive simulations where user may be undecided at some point and simulation needs to keep going.…”

Section: Related Workmentioning

confidence: 99%

Using Finite Forkable DEVS for Decision-Making Based on Time Measured with Uncertainty

Vicino¹,

Dalle²,

Wainer³

2015

Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques

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International audienceThe time-line in Discrete Event Simulation (DES) is a sequence of events defined in a numerable subset of R +. When it comes from an experimental measurement, the timing of these events has a limited precision. This precision is usually well-known and documented for each instruments and procedures used for collecting experimental datas. Therefore, these instruments and procedures produce measurement results expressed using values each associated with an uncertainty quantification, given by uncertainty intervals. Tools have been developed in Continuous Systems modeling for deriving the uncertainty intervals of the final results corresponding to the propagation of the uncertainty intervals being evaluated. These tools cannot be used in DES as they are defined, and no alternative tools that would apply to DES have been developed yet. In this paper, we propose simulation algorithms, based on the Discrete Event System Specification (DEVS) formalism, that can be used to simulate and obtain every possible output and state trajecto-ries of simulations that receive input values with uncertainty quantification. Then, we present a subclass of DEVS models , called Finite Forkable DEVS (FF-DEVS), that can be simulated by the proposed algorithms. This subclass ensures that the simulation is forking only a finite number of processes for each simulation step. Finally, we discuss the simulation of a traffic light model and show the trajectories obtained when it is subject to input uncertainty

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A discrete-event simulation tool for the analysis of simultaneous events

Cited by 6 publications

References 11 publications

Unbiased Deterministic Total Ordering of Parallel Simulations with Simultaneous Events

Unbiased Deterministic Total Ordering of Parallel Simulations with Simultaneous Events

An Abstract Discrete-Event Simulator Considering Input with Uncertainty

Using Finite Forkable DEVS for Decision-Making Based on Time Measured with Uncertainty

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