A case study in exploiting temporal uncertainty in parallel simulations

Approximate Time (AT) has been proposed as a means for expressing temporal uncertainty in distributed simulation applications in order to enhance parallel performance. This is accomplished by specifying time intervals rather than precise values to indicate when an event might occur. This paper describes a case study in applying AT to a queueing simulation application in order to assess the performance and accuracy that can be obtained using this technique. Up to an order of magnitude speedup was obtained, with error in throughput statistics less than 3%, although somewhat larger error was reported in delay statistics.

[1]  LamportLeslie Time, clocks, and the ordering of events in a distributed system , 1978 .

[2]  Jean-Xavier Rampon,et al.  On-line recognition of interval orders , 1993 .

[3]  Richard M. Fujimoto,et al.  Exploiting temporal uncertainty in parallel and distributed simulations , 1999, Proceedings Thirteenth Workshop on Parallel and Distributed Simulation. PADS 99. (Cat. No.PR00155).

[4]  Mukesh Singhal,et al.  Logical Time: Capturing Causality in Distributed Systems , 1996, Computer.

[5]  Richard M. Fujimoto,et al.  Approximate time and temporal uncertainty in parallel and distributed simulation , 2002 .

[6]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[7]  Philip A. Wilsey,et al.  Unsynchronized parallel discrete event simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[8]  Didier Dubois,et al.  Processing fuzzy temporal knowledge , 1989, IEEE Trans. Syst. Man Cybern..

[9]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[10]  S. Dutta,et al.  An event based fuzzy temporal logic , 1988, [1988] Proceedings. The Eighteenth International Symposium on Multiple-Valued Logic.

[11]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[12]  Ramon E. Moore Methods and applications of interval analysis , 1979, SIAM studies in applied mathematics.

[13]  André Schiper,et al.  Lightweight causal and atomic group multicast , 1991, TOCS.

[14]  Jouni Ikonen,et al.  Applying a modified Chandy-Misra algorithm to the distributed simulation of a cellular network , 1998, Workshop on Parallel and Distributed Simulation.

[15]  M. Vitek Fuzzy Information and Fuzzy Time , 1983 .