Most simulation models for data communication networks encompass hundreds of parameters that can each take on millions of values. Such models are difficult to understand, parameterize and investigate. This paper explains how to model a modern data communication network concisely, using only 20 parameters. Further, the paper demonstrates how this concise model supports efficient design of simulation experiments. The model has been implemented as a sequential simulation called MesoNet, which uses Simulation Language with Extensibility (SLX). The paper discusses model resource requirements and the performance of SLX. The model and principles delineated in this paper have been used to investigate parameter spaces for large (hundreds of thousands of simultaneously active flows), fast (hundreds of Gigabits/second) simulated networks under a variety of congestion control algorithms.
INTRODUCTIONPaxson and Floyd (1997) describe many difficult problems that impede simulation of large data communication networks, and recommend two main coping strategies: search for invariants and carefully explore the parameter space. Unfortunately, typical network simulators (e.g., Fall and Varadhan 2009, SSFNet 2009, Tyan et al. 2009) use hundreds of parameters that can each take on millions of values. Such simulations can be difficult to configure and usually require infeasible resources to explore the parameter space. Several researchers (Riley et al. 2004, Yaun et al. 2003, Zeng et al. 1998) investigate parallel techniques as a means to simulate larger, faster networks. Unfortunately, such techniques do not reduce the parameter space, which remains difficult to configure and continues to require significant resources when conducting careful exploration. In this paper, we describe how to model a modern data communication network, including the transmission control protocol (TCP) and Internet protocol (IP), using only 20 parameters. We implemented the model using SLX (Henriksen 2000) as a sequential simulation, called MesoNet. (Any mention of commercial products within this paper is for information only; it does not imply recommendation or endorsement by NIST.) As we demonstrate, a concise parameter space can be searched efficiently and effectively using sequential simulations deployed in parallel, where each simulation explores a selected configuration of parameters. Elsewhere (Mills et al. 2010), we use MesoNet to study a variety of congestion control algorithms proposed for the Internet. In that study, we perform a sensitivity analysis of the model's parameter space, providing key insights that guide design of the experiments. Here, we discuss only two sample experiments to illustrate the utility and resource requirements of MesoNet.