Recall-based cluster reformulation by selfish peers

Recently, clustered overlays in which peers are grouped based on the similarity of their content or interests have been proposed to improve performance in peer-to-peer systems. Since such systems are highly dynamic, the overlay network needs to be updated frequently to cope with changes. In this paper, we introduce an approach for updating a clustered overlay based on local decisions made by individual peers. We model the cluster-reformulation problem as a game where peers determine their cluster membership based on potential gains in the recall of their queries. We also define global criteria for the overall quality of the system and propose strategies for peer relocation that consider different behavioral patterns for the peers. Our preliminary experimental evaluation shows that our strategies cope well with changes in the overlay network.