Exploiting semantic locality to improve peer-to-peer search mechanisms

A Peer-to-Peer(P2P) network is the most popular technology in file sharing today. With the advent of various commercial and non-commercial applications like KaZaA, Gnutella, a P2P network has exercised its growth and popularity to the maximum. Every node (peer) in a P2P network acts as both a client and a server for other peers. A search in P2P network is performed as a query relayed between peers until the peer that contains the searched data is found. Huge data size, complex management requirements, dynamic network conditions and distributed systems are some of the difficult challenges a P2P system faces while performing a search. Moreover, a blind and uninformed search leads to performance degradation and wastage of resources. To address these weaknesses, techniques like Distributed Hash Table (DHT) has been proposed to place a tight constraint on the node placement. However, it does not considers semantic significance of the data. We propose a new peer to peer search protocol that identities locality in a P2P network to mitigate the complexity in data searching. Locality is a logical semantic categorization of a group of peers sharing common data. With the help of locality information, our search model offers more informed and intelligent search for different queries. To evaluate the effectiveness of our model we propose a new P2P search protocol - LocalChord. LocalChord relies on Chord and demonstrates potential of our proposed locality scheme by re-modelling Chord as a Chord of sub-chords

Content Distribution in P2P Systems

The report provides a literature review of the state-of-the-art for content distribution. The report's contributions are of threefold. First, it gives more insight into traditional Content Distribution Networks (CDN), their requirements and open issues. Second, it discusses Peer-to-Peer (P2P) systems as a cheap and scalable alternative for CDN and extracts their design challenges. Finally, it evaluates the existing P2P systems dedicated for content distribution according to the identied requirements and challenges

Leveraging P2P overlays for Large-scale and Highly Robust Content Distribution and Search

International audienceIn the last decade, there has been a tendency of shifting content distribution towards peer-to-peer (P2P) technology. The reason behind this is the self-scalability of P2P systems provided by the principles of communal collaboration and resource sharing in P2P systems. By building a P2P Content Distribution Network}(CDN), peers collaborate to distribute the content of under-provisioned websites and to serve queries for large audiences on behalf of the websites. When designing a P2P CDN, the main challenge is to actually maintain an acceptable level of performance in terms of client-perceived latency and hit ratio while minimizing the incurred overhead. This is not a straightforward endeavor given that the P2P CDN relies on autonomous and dynamic peers rather than a dedicated infrastructure. Indeed, the distribution of duties and content over peers should take into account their interests in order to give them proper incentives to cooperate. Moreover, the P2P-CDN should adapt to increasing numbers of participants and provide robust algorithms under high levels of churn because these issues have a key impact on performance. Finally, the routing of queries should aim peers close in locality and serve content from close-by providers to achieve short latencies. This paper gives an overview of our contributions in designing and maintaining a P2P CDN that tackles the issues identified above. First, we present Flower-CDN, a P2P content distribution network (CDN) that tackles some of these issues. Peers store only content of websites they are interested in and serve them to others. Furthermore, peers can find close-by content providers by a locality aware P2P directory structure. Secondly, we present a highly scalable approach of Flower-CDN called PetalUp-CDN which dynamically adjusts the directory structure in order to avoid overload situations and to keep the index information any peer must maintain at an acceptable level. Thirdly, we discuss maintenance protocols for Flower-CDN and PetalUp-CDN to cope with the worst scenarios of churn. The performance evaluation wrt. scalability and churn management shows that our generic approach enhances hit ratio by 40% and reduces response time by a factor of 12, compared to a well-known P2P-CDN

Locaware: Index Caching in Unstructured P2P-file Sharing Systems

International audienceThough widely deployed for file-sharing, unstructured P2P systems aggressively exploit network resources as they grow in popularity. The P2P traffic is the leading consumer of bandwidth, mainly due to search inefficiency, as well as to large data transfers over long distances. This critical issue may compromise the benefits of such systems by drastically limiting their scalability. In order to reduce the P2P redundant traffic, we propose Locaware, which performs index caching while supporting keyword search. Locaware aims at reducing the network load by directing queries to available nearby results. For this purpose, Locaware leverages natural file replication and uses topological information in terms of file physical distribution

Efficient Node Proximity and Node Significance Computations in Graphs

abstract: Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds. In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.Dissertation/ThesisDoctoral Dissertation Computer Science 201