Bootstrapping Skynet: Calibration and Autonomic Self-Control of Structured Peer-to-Peer Networks

Peer-to-peer systems scale to millions of nodes and provide routing and storage functions with best effort quality. In order to provide a guaranteed quality of the overlay functions, even under strong dynamics in the network with regard to peer capacities, online participation and usage patterns, we propose to calibrate the peer-to-peer overlay and to autonomously learn which qualities can be reached. For that, we simulate the peer-to-peer overlay systematically under a wide range of parameter configurations and use neural networks to learn the effects of the configurations on the quality metrics. Thus, by choosing a specific quality setting by the overlay operator, the network can tune itself to the learned parameter configurations that lead to the desired quality. Evaluation shows that the presented self-calibration succeeds in learning the configuration-quality interdependencies and that peer-to-peer systems can learn and adapt their behavior according to desired quality goals.