Mimicking Human Behavior in Shared-Resource Computer Networks

Among the many challenges in computer network trace data collection is the automation, or mimicking, of human users in situations where humans-in-the-loop are either impracticable or not possible. While client-side human behavior has been automated in various static settings, autonomous clients which dynamically change their behavior as the environment changes may result in a more accurate representation of human behavior in captured network trace data, and thus may be better suited for problems in which humans-in-the-loop are important. In this work, we set out to create dynamic autonomous client-side behavioral models, which we call agents, that can interact with the network environment in much the same way that humans do, and are scalable in shared-resource environments, such as emulated computer networks. We show through multiple experiments and a web crawling case study on an emulated network that our agents can mimic interactive human behavior, and do so at scale.