In this paper we present the AlvisP2P IR engine, which enables efficient retrieval with multi-keyword queries from a global document collection available in a P2P network. In such a network, each peer publishes its local index and invests a part of its local computing resources (storage, CPU, bandwidth) to maintain a fraction of a global P2P index. This investment is rewarded by the network-wide accessibility of the local documents via the global search facility. The AlvisP2P engine uses an optimized overlay network and relies on novel indexing/retrieval mechanisms that ensure low bandwidth consumption, thus enabling unlimited network growth. Our demonstration shows how an easy-to-install AlvisP2P client can be used to join an existing P2P network, index local (text or even multimedia) documents with collection-specific indexing mechanisms, and control access rights to them.

The suitability of peer-to-peer (P2P) approaches for full-text Web retrieval has recently been questioned because of the claimed unacceptable bandwidth consumption induced by retrieval from very large document collections. In this contribution we formalize a novel indexing/retrieval model that achieves high performance, cost-efficient retrieval by indexing with highly discriminative keys (HDKs) stored in a distributed global index maintained in a structured P2P network. HDKs correspond to carefully selected terms and term sets appearing in a small number of collection documents. We provide a theoretical analysis of the scalability of our retrieval model and report experimental results obtained with our HDK-based P2P retrieval engine. These results show that, despite increased indexing costs, the total traffic generated with the HDK approach is significantly smaller than the one obtained with distributed single-term indexing strategies. Furthermore, our experiments show that the retrieval performance obtained with a random set of real queries is comparable to the one of centralized, single-term solution using the best state-of-the-art BM25 relevance computation scheme. Finally, our scalability analysis demonstrates that the HDK approach can scale to large networks of peers indexing Web-size document collections, thus opening the way towards viable, truly-decentralized Web retrieval.

One of the challenges of today's overlay networks, especially P2P, is still scalability. A key issue in almost all of the current overlay architectures is the link count per single node. If the link count is too high, the management overhead in terms of keep-alive messages increases. If the amount of links per node is too low, the resilience of the system against network splits decreases and the system can hardly route in an optimal way. Moreover, if keep-alive messages are not sent frequently enough, outdated information could be propagated, which again could cause net splits. This paper presents a new cooperative keep-alive algorithm that strongly reduces the costs for sending keep- alive messages and, at the same time, preserves the effectiveness and reliability of standard keep-alive mechanisms in today's overlay networks. The algorithm allows to increase the number of links per node, and, thus, to improve the connectivity and routing efficiency in the network, while keeping the keep-alive overhead low. When used without increasing the link count, the algorithm reduces drastically the keep-alive traffic. The properties of the algorithm are evaluated analytically and simulatively and compared to existing keep-alive techniques.

Distributed hash tables (DHTs) provide a scalable mechanism for mapping identifiers to socket addresses. As each peer in the network can initiate lookup requests, a DHT has to process concurrently a potentially very large number of requests. In this paper, we look at congestion control for DHTs. Our goal is to control the flow of lookup requests that are routed in the overlay network. We first show that congestion control is essential for certain applications with high lookup rates. We then present two congestion control mechanisms for DHTs and compare their performances in different network conditions