OpEM distributed simulation

Abstract

Distributed simulation based on Opera tional Evaluation Modeling (OpEM) is discussed. OpEM, a system design and analysis methodology, uses a two-dimen sional, directed graph language to describe system operation. The OpEM simulation programming system, that implements this methodology, includes object-oriented event processing routines, moving-object routines, an expert system controller, and continuous system simulation routines. These routines are required to model large systems, such as the Strategic Defense Initiative (SDI) systems, that are characterized by closely coupled, context sensitive interactions. These interactions are modeled using a large global state space consisting of both discrete and continuous state variables. Execution of such large, complex models on uniprocessors can be slow; therefore, possible speedup by execution on a multiprocessor system, a set of processors that share memory, is investigated. A generic surface- ship warfare simulation, characteristic of many large models, is executed on a simulation of a multiprocessor system to investigate speedup.

Get full access to this article

View all access options for this article.

References

Ashby, W.R. (1956). An Introduction to Cybernetics. Chapman & Hall, London, U.K.

Ashby, W.R. (1960). Design for a Brain: The Origin of Adaptive Behavior . Chapman & Hall, London, U.K.

Bain, W.L. , and D.S. Scott. (1988). "An Algorithm for Time Synchronization in Distributed Discrete Event Simulation" Distributed Simulation , Simulation Councils, Inc., San Diego, pp. 30-33.

Chandy, K.M. , and J. Misra , (1981). "Asynchronous Distributed Simulation Via a Sequence of Parallel Computations,"Communications of the ACM, Vol. 24, No. 11 (April 1981 )

Clymer, J.R. , (1989). "OpEM Expert System Controller ," In Simulation and AI, 1989, San Diego, CA: The Society of Computer Simulation International, Volume 20, Number 3, pp. 20-26.

Clymer, J.R. , (1990a). Systems Analysis Using Simulation and Markov Models, Englewood Cliffs, N.J.: Prentice-Hall, Inc.

Clymer, J.R. , Corey, P.D. , and N. Nili , (1990b). "Operational Evaluation Modeling," In Simulation, San Diego, CA : The Society of Computer Simulation International , December 1990 issue, pp. 261-270.

Clymer, J.R. , (1990c). "System Design Using OpEM Inductive/Adaptive Expert System Controller," In IASTED International Journal of Modeling & Simulation, Volume 10, Number 4, pp.129-136.

Clymer, J.R. , Corey, P.D. , and J. Gardner , (1992). "Discrete Event Fuzzy Airport Control", In IEEE Transactions on Systems, Man, and Cybernetics, Volume 22, Number 1, January 1992.

10.

Conklin, D. , J. Cleary , and B. Unger , (1990). "The Shark's World", Distributed Simulation, Simulation Councils, Inc., San Diego, pp. 157-160.

11.

Corey, P.D. , and J.R. Clymer , (1991). "Discrete Event Simulation of Object Movement and Interactions," In SIMULATION, San Diego, CA: The Society of Computer Simulation International , March 1991 issue, pp. 167-174.

12.

Cota, B.A. , and R.G. Sargent , (1990). "A Framework for Automatic Lookahead Computation in Conservative Distributed Simulations", Distributed Simulation , Simulation Councils, Inc., San Diego, pp. 56-59.

13.

Fujimoto, R.M. , (1988). "Performance Measurements of Distributed Simulation Strategies", Distributed Simulation, Simulation Councils, Inc., San Diego, pp. 14-20.

14.

Fujimoto, R. M., (1990). "Performance of Time Warp Under Synthetic Workloads", Distributed Simulation, Simulation Councils, Inc., San Diego, pp. 23-28.

15.

Reed, D.A. , and A.D. Malony , (1988). "Parallel Discrete Event Simulation: The Chandy-Misra Approach," Distributed Simulation, Simulation Councils, Inc., San Diego, pp. 8-13.

16.

Sokol, L.M. , D.P. Briscoe , and A.P. Wieland ,( 1988). "MTW: A Strategy for Scheduling Discrete Simulation Events for Concurrent Execution," Distributed Simulation, Simulation Councils, Inc., San Diego, pp. 34-42.

17.

Zeigler, B. , (1984). Multifaceted Modeling and Discrete Event Simulation , London :Academic.