Characterizing traffic-aware overlay topologies: a machine learning approach
networks attractive for a variety of network applications.
Recently, game-theoretic approaches to constructing overlay network topologies have
been proposed. In these approaches, nodes establish logical links toward other nodes in a decentralized and sel?sh manner. Despite the sel?sh behavior, it has been shown that desirable global network properties emerge. These approaches, however, neglect the traffic-demand between nodes. In this thesis, a game-theoretical approach is presented to constructing overlay network topologies that considers the traffic-demand between nodes. This thesis shows that the traffic-demand between nodes has a signi?cant effect on the topologies formed. Nodes with statistically higher traffic-demand from others become members of the graph center, while nodes that have statistically higher traffic-demand toward others establish logical links toward members of the graph center. This thesis also shows that a traffic-demand aware overlay network topology is better suited to transport the required tra?c in the overlay network.
Unfortunately, the game-theoretic approach is intractable. In order to construct larger
overlay networks, approximate or heuristic approaches are required. In this thesis, a machine learning approach is proposed that characterizes the attributes of neighbor nodes during the construction of the overlay network topology. The approach proposed uses this knowledge and experience to learn a set of human-readable rules. This rule set is then used to decide whether to construct a logical link toward a node. This thesis shows that the machine learning approach results in similar overlay network topologies as the game-theoretic approach.
Additionally, it is shown that the machine learning approach is tractable and scales to larger networks.
School:Kansas State University
School Location:USA - Kansas
Source Type:Master's Thesis
Keywords:overlay networks machine learning computer science 0984 engineering electronics and electrical 0544
Date of Publication:01/01/2007