Flow-based partitioning of network testbed experiments

Understanding the behavior of large-scale systems is challenging, but essential when designing new Internet protocols and applications. It is often infeasible or undesirable to conduct experiments directly on the Internet. Thus, simulation, emulation, and testbed experiments are important techniques for researchers to investigate large-scale systems.In this paper, we propose a platform-independent mechanism to partition a large network experiment into a set of small experiments that are sequentially executed. Each of the small experiments can be conducted on a given number of experimental nodes, e.g., the available machines on a testbed. Results from the small experiments approximate the results that would have been obtained from the original large experiment. We model the original experiment using a flow dependency graph. We partition this graph, after pruning uncongested links, to obtain a set of small experiments. We execute the small experiments iteratively. Starting with the second iteration, we model dependent partitions using information gathered about both the traffic and the network conditions during the previous iteration. Experimental results from several simulation and testbed experiments demonstrate that our techniques approximate performance characteristics, even with closed-loop traffic and congested links. We expose the fundamental tradeoff between the simplicity of the partitioning and experimentation process, and the loss of experimental fidelity.