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| Version | Summary | Created by | Modification | Content Size | Created at | Operation |
|---|---|---|---|---|---|---|
| 1 | Camila Xu | -- | 1509 | 2022-11-02 01:49:51 |
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A discrete-event simulation (DES) models the operation of a system as a discrete sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Between consecutive events, no change in the system is assumed to occur; thus the simulation can directly jump in time from one event to the next. This contrasts with continuous simulation in which the simulation continuously tracks the system dynamics over time. Instead of being event-based, this is called an activity-based simulation; time is broken up into small time slices and the system state is updated according to the set of activities happening in the time slice. Because discrete-event simulations do not have to simulate every time slice, they can typically run much faster than the corresponding continuous simulation. A more recent method is the three-phased approach to discrete event simulation (Pidd, 1998). In this approach, the first phase is to jump to the next chronological event. The second phase is to execute all events that unconditionally occur at that time (these are called B-events). The third phase is to execute all events that conditionally occur at that time (these are called C-events). The three phase approach is a refinement of the event-based approach in which simultaneous events are ordered so as to make the most efficient use of computer resources. The three-phase approach is used by a number of commercial simulation software packages, but from the user's point of view, the specifics of the underlying simulation method are generally hidden.
References
- Aditya Kurve; Khashayar Kotobi; George Kesidis. "An agent-based framework for performance modeling of an optimistic parallel discrete event simulator". Complex Adaptive Systems Modeling 1: 12. doi:10.1186/2194-3206-1-12. https://link.springer.com/article/10.1186/2194-3206-1-12.
- Douglas W. Jones, ed. Implementations of Time, Proceedings of the 18th Winter Simulation Conference, 1986. http://portal.acm.org/citation.cfm?id=318242.318467
- Douglas W. Jones, Empirical Comparison of Priority Queue and Event Set Implementations, Communications of the ACM, 29, April 1986, pages 300–311. http://doi.acm.org/10.1145/5684.5686
- Kah Leong Tan and Li-Jin Thng, SNOOPy Calendar Queue, Proceedings of the 32nd Winter Simulation Conference, 2000 http://portal.acm.org/citation.cfm?id=510378.510453
- Tom Dickman, Sounak Gupta and Philip A. Wilsey, Event pool structures for PDES on many-core Beowulf clusters, Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation https://doi.org/10.1145/2486092.2486106
- Angelo Furfaro and Ludovica Sacco, Adaptive Ladder Queue: Achieving O(1) Amortized Access Time in Practice, Proceedings of the 2018 ACM SIGSIM conference on Principles of advanced discrete simulation https://doi.org/10.1145/3200921.3200925
