Structure of Peer-to-Peer Social Networks

This paper presents a statistical analysis of the structure of Peer-to-Peer (P2P) social networks that captures social associations of distributed peers in resource sharing. Peer social networks appear to be mainly composed of pure resource providers that guarantee high resource availability and reliability of P2P systems. The major peers that both provide and request resources are only a small fraction. The connectivity between peers, including undirected, directed (out and in) and weighted connections, is scale-free and the social networks of all peers and major peers are small world networks. The analysis also confirms that peer social networks show in general disassortative correlations, except that active providers are connected between each other and by active requesters. The study presented in this paper gives a better understanding of peer relationships in resource sharing, which may help a better design of future P2P networks and open the path to the study of transport processes on top of real P2P topologies.Comment: APS Style, 8 pages, 5 figures and 4 tables. Final versio

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network