AN INNOVATIVE DATA QUERY SYSTEM FOR COMMON INTERESTS OF NEIGHBOURS

Internet recognition bakes an essential motivation towards peer to determine file talking about. For understanding the peer to determine file talking about system, an important qualifying qualifying criterion to is efficiency of file location.  Inside our work we submit a peer to determine file talking about system that's closeness-aware additionally to Interest-clustered based on structured peer to determine system. It forms close nodes to cluster after which groups general interest nodes into sub-cluster that is founded on hierarchical topology and apply a wise file replication to boost file query effectiveness. The forecasted system can keep each and every advantage of distributed hash tables above unstructured peer to determine systems. It's closeness-aware additionally to Interest-clustered utilizes an intellectual file replication to boost file research competence and places files sticking with the same interests with one another which makes them available through routing function. The device will progress intra-sub-cluster file searching completely through several approaches. It evolves an overlay for every group that bond lesser capacity nodes towards advanced capacity nodes for spread file querying during remaining from of node overload. Recommended system utilizes range of positive file data to make sure that file requester can recognize whether requested for file reaches its close by nodes

A CLOSENESS-ALERT INTEREST-GATHERED P2P FOLDER SHARING SYSTEM

Clustering peers by their physical closeness will frequently increase file query performance. However, number of current works can cluster peers according to both peer interest and physical closeness. Although structured P2Ps provide greater file query efficiency than unstructured P2Ps, it is sometimes complicated to understand it because of their strictly defined topologies. During this work, we introduce a Closeness-Aware and Interest-clustered P2P file discussing System with assorted structured P2P, which forms physically-close nodes in a cluster and additional groups physically-close and customary-interest nodes in a sub-cluster with assorted hierarchical topology. Clustering peers by their common interests can considerably enhance the efficiency of file query. PAIS relies on a smart file replication formula to help enhance file query efficiency. Thinking about the lately visited file is usually visited again, the blossom filter based approach is enhanced by only analyzing the recently added blossom filter information to lessen file searching delay. Trace-driven experimental is due to the specific-world Planet Lab test bed show PAIS significantly reduces overhead and boosts the efficiency of file discussing with and without churn. Further, the experimental results show the very best effectiveness within the intra-sub-cluster file searching approaches in enhancing file searching efficiency. PAIS develops an overlay for every group that connects lower capacity nodes to greater capacity nodes for distributed file querying while remaining from node overload. To lessen the overhead within the file information collection, PAIS uses blossom filter based file information collection and corresponding distributed file searching. To improve the file discussing efficiency, PAIS ranks the blossom filter leads to order

A Self-Organizing Wireless Sensor Network and Distributed Computing Engine for Commodity and Future Palmtop Computers

The embedded class processors found in commodity palmtop computers continue to become increasingly capable while retaining an energy-efficient footprint. Palmtop computers themselves, including smartphones and tablets, provide a small form factor system integrating wireless communication and non-volatile storage with these energy-efficient processors. Also, various wireless connectivity functions on mobile devices provide new opportunities in designing more flexible, smarter wireless sensor networks (WSNs), and utilizing the computation power in a way we could never imagine before. In this dissertation, I present a WSN concept for current and future generation tablet devices. My contributions include developments at the system level, architecture level, and collaborative design between different layers of the system. At the system level, I developed Ocelot, a grid-like computing environment for palmtop computers in place of traditional workstation or server class machines to compute highly parallel light to medium-weight tasks in an energy efficient manner. Additionally, I developed Lynx, a self-organizing wireless sensor network, which is a further step taken in exploiting the potential of palmtop computers. At the architecture level, to increase the storage capacity of future palmtop computers, I explore the use of a new storage class magnetic memory, Racetrack Memory (RM), throughout the memory hierarchy. Thus, I developed FusedCache, a naturally inclusive, dual-level private cache design for RM that provides fast uniform access at one level, and non-uniform access at the next, which allows RM to be effective as close to the processor as an L1 cache. For higher levels of the memory hierarchy such as the last level cache, I propose a Multilane Racetrack Cache (MRC), an RM last level cache design utilizing lightweight compression combined with independent shifting. MRCs allow cache lines mapped to the same Racetrack structure to be accessed in parallel when compressed, mitigating potential shifting stalls in an RM cache. Finally, leveraging the lightweight compression from MRC and the need for efficient communication in Lynx, I present a cross-level design combining memory-level lightweight compression with network-level packet transfer, together with a technique called Source-Aware Layout Reorganization (SALR) to increase the compressibility of sensor data

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

The design and evaluation of a self-organizing superpeer network

Superpeer architectures exploit the heterogeneity of nodes in a peer-to-peer (P2P) network by assigning additional responsibilities to higher capacity nodes. In the design of a superpeer network for file sharing, several issues have to be addressed: how client peers are related to superpeers, how superpeers locate files, how the load is balanced among the superpeers, and how the system deals with node failures. In this paper, we introduce a self-organizing superpeer network architecture (SOSPNet) that solves these issues in a fully decentralized manner. SOSPNet maintains a superpeer network topology that reflects the semantic similarity of peers sharing content interests. Superpeers maintain semantic caches of pointers to files, which are requested by peers with similar interests. Client peers, on the other hand, dynamically select superpeers offering the best search performance. We show how this simple approach can be employed not only to optimize searching, but also to solve generally difficult problems encountered in P2P architectures such as load balancing and fault tolerance. We evaluate SOSPNet using a model of the semantic structure derived from eight-month traces of two large file-sharing communities. The obtained results indicate that SOSPNet achieves close-to-optimal file search performance, quickly adjusts to changes in the environment (node joins and leaves), survives even catastrophic node failures, and efficiently distributes the system load taking into account superpeer capacities