DNSR: Domain Name Suffix-based Routing in Overlay Networks

Abstract. Overlay Peer-to-Peer (P2P) networks are application layer networks which facilitate users in performing distributed functions such as keyword searches over the data of other users. An important problem in such networks is that the connection among peers are arbitrary, leading in that way to a topology structure which doesn't match the underlying physical topology. This phenomenon leads to excessive network resource consumption in Wide Area Networks as well as degraded user experience because of the incurred network delays. Most state-of-the-art research concentrates on structuring overlay networks in a way that query messages can reach the appropriate nodes within some hop-count boundaries. These approaches are not taking into account the underlying network topology mismatch making it therefore inappropriate for wide area routing. In this work we propose and evaluate DNSR (Domain Name Suffix-based Routing), which is a novel technique to route query messages in Overlay Networks, based on the "domain closeness" of the node sending the message. We describe DNSR and show simulation experiments which are performed over PeerWare, our distributed infrastructure which runs on a network of 50 workstations. Our simulations are based on real data gathered from one of the largest open P2P networks, namely Gnutella

Architecture analysis of peer-to-peer network structure and data exhanges for distribution of contraband material.

Because of the anonymity that P2P networks provide, they are an ideal medium for the exchange of contraband material such as child pornography. Unfortunately, not much research has been conducted on how to best monitor these types of networks for contraband searching and sharing activity. This thesis proposes techniques to advance the state of the art in peer to peer data exchange monitoring and detection of nodes that participate in distributing and sharing contraband material. Because of the legal considerations in working with a live P2P network and the technical di culty in developing and testing a surveillance system for P2P networks, a simulator was developed that attempts to accurately simulate the behavior of users on P2P networks based upon empirical data collected from several researchers. With the help of the simulation platform that has been developed, a complete methodology for monitoring contraband activity and reporting the most proli c contraband users has been created. This methodology, if implemented on an actual P2P network, should allow the detection of members of the network who are the most active sharers and distributors of contraband material

Dynamic probe positioning within peer-to-peer networks for mining contraband file exchanges.

Peer-to-peer networks have been growing in popularity over the past decade. There have been many new innovations that greatly improve access to a wide variety of content. This expanded capability combined with a strong sense of anonymity has given rise to increased proliferation of illicit content. In particular the increase in child pornography has been a growing concern in the United States and other countries. Thus law enforcement is motivated to find improved means for finding those sharing this material online. Due to the dynamic and expansive nature of peer-to-peer networks, there is a need to develop methods that allow law enforcement to monitor with a high degree of confidence that a large percent of perpetrators can be identified. Thus a study of the current state of peer-to-peer networks with an analysis of how best to identify clients sharing contraband files on the network is needed to monitor these criminal elements

Semantic social routing in Gnutella

The objective of this project is to improve the performance of the Gnutella peer-to-peer protocol (version 0.4) by introducing a semantic-social routing model and several categories of interest. The Gnutella protocol requires peers to broadcast messages to their neighbours when they search files. The message passing generates a lot of traffic in the network, which degrades the quality of service. We propose using social networks to optimize the speed of search and to improve the quality of service in a Gnutella based peer-to-peer environment. Each peer creates and updates a “friends list” from its past experience, for each category of interest. Once peers generate their friends lists, they use these lists to semantically route queries in the network. Search messages in a given category are mainly sent to “friends” who have been useful in the past in finding files in the same category. This helps to reduce the search time and to decrease the network traffic by minimizing the number of messages circulating in the system as compared to standard Gnutella. This project will demonstrate by simulating a peer-to-peer type of environment with the JADE multi-agent system platform that by learning other peers’ interests, building and exploiting their social networks (friends lists) to route queries semantically, peers can get more relevant resources faster and with less traffic generated, i.e. that the performance of the Gnutella system can be improved

大規模P2Pシステムにおける資源検索に関する研究

Tohoku University小林広明課

Finding good peers in peer-to-peer networks

peer-to-peer networks, decentralized As computing and communication capabilities have continued to increase, more and more activity is taking place at the edges of the network, typically in homes or on workers desktops. This trend has been demonstrated by the increasing popularity and usability of "peer-to-peer " systems such as Napster and Gnutella. Unfortunately, this popularity has quickly shown the limitations of these systems, particularly in terms of scale. Because the networks form in an ad-hoc manner, they typically make inefficient use of resources. We propose a mechanism, using only local knowledge, to improve the overall performance of peer-to-peer networks based on interests. Peers monitor which other peers frequently respond successfully to their requests for information. When a peer is discovered to frequently provide good results, the peer attempts to move closer to it in the network by creating a new connection with that peer. This leads to clusters of peers with similar interests, and in turn allows us to limit the depth of searches required to find good results. We have implemented our algorithm in the context of a distributed encyclopedia-style information sharing application which is built on top of the gnutella network. In our testing environment, we have shown the ability to greatly reduce the amount of communication resources required to find the desired articles in the encyclopedia

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network