Multi-resolution network simulations using dynamic component substitution

Modeling and simulation of large, high resolution network models is a time consuming task even when parallel simulation techniques are employed. Processing voluminous, detailed simulation data further increases the complexity of analysis. Consequently, the models (or parts of the models) are abstracted to improve performance of the simulations by trading-off model details and fidelity. However abstraction defeats the purpose of studying high resolution network models and magnifies the problems of validation! An alternative approach is to dynamically (i.e., during the course of simulation) change the resolution of the model (or parts of the model). In our component based network modeling and simulation framework (NMSF), we have enabled dynamic changes to the resolution of a model using a novel methodology called dynamic component substitution (DCS). Using DCS, a set of components can be substituted by a functionally equivalent component (or vice versa) to change the resolution (or the level of abstraction) of a network model. DCS improves the overall efficiency of simulations through dynamic tradeoffs between resolution of a model, simulation performance, and analysis overheads. This paper presents an overview of DCS and the issues involved in enabling DCS in NMSF, an optimistically synchronized parallel simulation framework. The experiments conducted to evaluate the effectiveness of DCS are also illustrated. Our studies indicate that DCS provides an effective technique to considerably improve the overall efficiency of network simulations.

[1]  Paul F. Reynolds,et al.  MRE: a flexible approach to multi-resolution modeling , 1997 .

[2]  Peter B. Danzig,et al.  Packet network simulation: speedup and accuracy versus timing granularity , 1996, TNET.

[3]  Paul K. Davis,et al.  Families of models that cross levels of resolution: issues for design, calibration and management , 1993, WSC '93.

[4]  David R. Jefferson,et al.  Virtual time , 1985, ICPP.

[5]  S. Srinivasan,et al.  MRE: a flexible approach to multi-resolution modeling , 1997, Workshop on Parallel and Distributed Simulation.

[6]  Philip A. Wilsey,et al.  Process combination to increase event granularity in parallel logic simulation , 1995, Proceedings of 9th International Parallel Processing Symposium.

[7]  Philip A. Wilsey,et al.  An Object-Oriented Time Warp Simulation Kernel , 1998, ISCOPE.

[8]  Sally Floyd,et al.  Why we don't know how to simulate the Internet , 1997, WSC '97.

[9]  Stewart Robinson,et al.  Simulation model verification and validation: increasing the users' confidence , 1997, WSC '97.

[10]  Zhicheng Wang,et al.  LECSIM: a levelized event driven compiled logic simulation , 1991, DAC '90.

[11]  Peter B. Danzig,et al.  Speedup vs. Simulation Granularity , 1996 .

[12]  Deborah Estrin,et al.  Enabling large-scale simulations: selective abstraction approach to the study of multicast protocols , 1998, Proceedings. Sixth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.98TB100247).

[13]  Herb Schwetman Hybrid simulation models of computer systems , 1978, CACM.