Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework

A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedbacks among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodeling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralized control.