Leveraging Network Motifs for the Adaptation of Structured Peer-to-Peer-Networks

Topology adaptation is a vital operation in technological networks. It is frequently implemented as either an external process or a distributed online optimization that relies on gathering knowledge on the overall state of the system. In this work we propose MBO, a novel approach that uses network motifs (a local, stochastic metric) for distributed topology optimization of arbitrary, adaptable networks. In order to give a proof of concept we chose to optimize structured Peer-to-Peer overlays towards a fair load balancing. MBO is parametrized using target motif signatures of networks, which are derived from exemplary, generated topologies with the desired properties - a fair load balancing in the demonstrated case. Extensive simulations indicate that for CAN and Kademlia, two different types of P2P systems, MBO leads to a well balanced load, while being minimally intrusive.