8,473 research outputs found
CliqueStream: an efficient and fault-resilient live streaming network on a clustered peer-to-peer overlay
Several overlay-based live multimedia streaming platforms have been proposed
in the recent peer-to-peer streaming literature. In most of the cases, the
overlay neighbors are chosen randomly for robustness of the overlay. However,
this causes nodes that are distant in terms of proximity in the underlying
physical network to become neighbors, and thus data travels unnecessary
distances before reaching the destination. For efficiency of bulk data
transmission like multimedia streaming, the overlay neighborhood should
resemble the proximity in the underlying network. In this paper, we exploit the
proximity and redundancy properties of a recently proposed clique-based
clustered overlay network, named eQuus, to build efficient as well as robust
overlays for multimedia stream dissemination. To combine the efficiency of
content pushing over tree structured overlays and the robustness of data-driven
mesh overlays, higher capacity stable nodes are organized in tree structure to
carry the long haul traffic and less stable nodes with intermittent presence
are organized in localized meshes. The overlay construction and fault-recovery
procedures are explained in details. Simulation study demonstrates the good
locality properties of the platform. The outage time and control overhead
induced by the failure recovery mechanism are minimal as demonstrated by the
analysis.Comment: 10 page
Multimedia Correlation Analysis in Unstructured Peer-to-Peer Network
Recent years saw the rapid development of peer-topeer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
An Efficient Architecture for Information Retrieval in P2P Context Using Hypergraph
Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of
Internet traffic. P2P systems have emerged as an accepted way to share enormous
volumes of data. Needs for widely distributed information systems supporting
virtual organizations have given rise to a new category of P2P systems called
schema-based. In such systems each peer is a database management system in
itself, ex-posing its own schema. In such a setting, the main objective is the
efficient search across peer databases by processing each incoming query
without overly consuming bandwidth. The usability of these systems depends on
successful techniques to find and retrieve data; however, efficient and
effective routing of content-based queries is an emerging problem in P2P
networks. This work was attended as an attempt to motivate the use of mining
algorithms in the P2P context may improve the significantly the efficiency of
such methods. Our proposed method based respectively on combination of
clustering with hypergraphs. We use ECCLAT to build approximate clustering and
discovering meaningful clusters with slight overlapping. We use an algorithm
MTMINER to extract all minimal transversals of a hypergraph (clusters) for
query routing. The set of clusters improves the robustness in queries routing
mechanism and scalability in P2P Network. We compare the performance of our
method with the baseline one considering the queries routing problem. Our
experimental results prove that our proposed methods generate impressive levels
of performance and scalability with with respect to important criteria such as
response time, precision and recall.Comment: 2o pages, 8 figure
On exploiting social relationship and personal background for content discovery in P2P networks
International audienceContent discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384, 494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features-Knowledge and Similarity-and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node's friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods
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
On exploiting social relationship and personal background for content discovery in P2P networks
Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features – Knowledge and Similarity – and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.This work has been funded by the European Union under the project eCOUSIN (EU-FP7-318398) and the project SITAC (ITEA2-11020). It also has been partially funded by the Spanish Government through the MINEC eeCONTENT project (TEC2011-29688-C02-02)
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