SHRiNK: A method for scaleable performance prediction and efficient network simulation

In networks and in Web server farms, it is useful to collect performance measurements, to monitor the state of the system, and to perform simulations. However, the sheer volume of traffic in large high-speed network systems makes it hard to monitor their performance or to simulate them efficiently. And the heterogeneity of the Internet means it is time-consuming and difficult to devise the traffic models and analytic tools which would allow us to work with summary statistics. We explore a method to side-step these problems by combining sampling, modeling and simulation. Our hypothesis is this: if we take a sample of the input traffic, and feed it into a suitably scaled version of the system, we can extrapolate from the performance of the scaled system to that of the original. Our main findings are: When we scale an IP network which is shared by TCP-like, UDP and Web flows; and which is controlled by a variety of active queue management schemes, then performance measures such as queueing delay and drop probability are left virtually unchanged. We show this in theory and in simulations. This makes it possible to capture the performance of large networks quite faithfully using smaller scale replicas.