Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models

Improving Large-Scale Network Traffic Simulation with Multi-Resolution Models Dartmouth Computer Science Technical Report TR2005-558 by Guanhua Yan Doctor of Philosophy in Computer Science Dartmouth College, Hanover, NH September 2005 Simulating a large-scale network like the Internet is a challenging undertaking because of the sheer volume of its traffic. Packet-oriented representation provides high-fidelity details but is computationally expensive; fluid-oriented representation offers high simulation efficiency at the price of losing packet-level details. Multi-resolution modeling techniques exploit the advantages of both representations by integrating them in the same simulation framework. This dissertation presents solutions to the problems regarding the efficiency, accuracy, and scalability of the traffic simulation models in this framework. The “ripple effect” is a well-known problem inherent in eventdriven fluid-oriented traffic simulation, causing explosion of fluid rate changes. Integrating multiresolution traffic representations requires estimating arrival rates of packet-oriented traffic, calculating the queueing delay upon a packet arrival, and computing packet loss rate under buffer overflow. Real time simulation of a large or ultra-large network demands efficient background traffic simulation. The dissertation includes a rate smoothing technique that provably mitigates the “ripple effect”, an accurate and efficient approach that integrates traffic models at multiple abstraction levels, a sequential algorithm that achieves real time simulation of the coarse-grained traffic in a network with 3 tier-1 ISP (Internet Service Provider) backbones using an ordinary PC, and a highly scalable parallel algorithm that simulates network traffic at coarse time scales.