Distributed computer architectures labeled “peer-to-peer ” are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and

Peer-to-Peer networks are gaining increasing attention from both the scientific and the large Internet user community. Popular applications utilizing this new technology offer many attractive features to a growing number of users. At the heart of such networks lies the search algorithm. Proposed methods either depend on the network-disastrous flooding and its variations or utilize various indices too expensive to maintain. In this paper, we describe an adaptive, bandwidth-efficient algorithm for search in unstructured Peer-to-Peer networks, the Adaptive Probabilistic Search method (APS). Our scheme utilizes feedback from previous searches to probabilistically guide future ones. It performs efficient object discovery while inducing zero overhead over dynamic network operations. Extensive simulation results show that APS achieves high success rates, increased number of discovered objects, very low bandwidth consumption and adaptation to changing topologies. 1.

A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems. Existing approaches

We present SETS, an architecture for building topic-segmented networks for efficient search. The key idea is to arrange participants in a topic-segmented topology where most of the links are short-distance links joining pairs of sites with similar content. The resulting topically focused regions are joined together into a single network by long-distance links. Queries are then matched and routed to only the topically closest regions. We draw on ideas from machine learning and social network theory to build an efficient search network. We discuss a variety of design issues and tradeoffs that an implementor of SETS would face. We show that SETS is ecient in network traffic and query processing load.

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Peer-to-peer (P2P) architectures are commonly used for file-sharing applications. The reasons for P2P's popularity in file sharing -- fault tolerance, scalability, and ease of deployment -- also make it a good model for distributed data management. In this paper, we introduce a scalable P2P framework for distributed data management applications using mutant query plans: XML serializations of algebraic query plan graphs that can include verbatim XML data, references to resource locations (URLs), and abstract resource names (URNs). We show how we can build distributed catalogs based on multihierarchic namespaces that can efficiently handle content indexing and query routing. We also discuss how peers can convey the currency and coverage of their data, and how queries can use this information to manage the inherent tradeoffs between answer completeness, timeliness, and latency.

Peer-to-Peer networks have become a major research topic over the last few years. Object location is a major part in the operation of these distributed systems. In this work, we present an overview of several search methods for unstructured peer-to-peer networks. Popular file-sharing applications, through which enormous amounts of data are daily exchanged, operate on such networks. We analyze the performance of the algorithms relative to their success rates, bandwidth consumption and adaptation to changing topologies. Simulation results are used to empirically evaluate their behavior in direct comparison. 1.

CiteSeer is a well-known online resource for the computer science research community, allowing users to search and browse a large archive of research papers. Unfortunately, its current centralized incarnation is costly to run. Although members of the community would presumably be willing to donate hardware and bandwidth at their own sites to assist CiteSeer, the current architecture does not facilitate such distribution of resources. OverCite is a design for a new architecture for a distributed and cooperative research library based on a distributed hash table (DHT). The new architecture harnesses donated resources at many sites to provide document search and retrieval service to researchers worldwide. A preliminary evaluation of an initial OverCite prototype shows that it can service more queries per second than a centralized system, and that it increases total storage capacity by a factor of n/4 in a system of n nodes. OverCite can exploit these additional resources by supporting new features such as document alerts, and by scaling to larger data sets.

Structured peer-to-peer (P2P) systems are considered as the next generation application backbone on the Internet. An important problem of these systems is load balancing in the presence of non-uniform data distributions. In this paper we propose a completely decentralized mechanism that in parallel addresses a local and a global load balancing problem: (1) balancing the storage load uniformly among peers participating in the network and (2) uniformly replicating different data items in the network while optimally exploiting existing storage capacity. Our approach is based on the P-Grid P2P system which is our variant of a structured P2P network. Problem (1) is solved by directly adapting the search structure to the data distribution. This may result in an unbalanced search structure, but we will show that the expected search cost in P-Grid in number of messages remains logarithmic under all circumstances.

People search for people with suitable expertise all of the time in their social networks – to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might be useful. In this paper, we examine three families of searching strategies that we believe may be useful in expertise location. We do so through a simulation, based on the Enron email data set. (We would be unable to suitably experiment in a real organization, thus our need for a simulation.) Our emphasis is not on graph theoretical concerns, but on the social characteristics involved. The goal is to understand the tradeoffs involved in the design of social network based searching engines.