Catálogo de publicaciones - libros

In a peer-to-peer (P2P) system, nodes typically connect to a small set of random nodes (their neighbors), and queries are propagated along these connections. Such query flooding tends to be very expensive. We propose that node connections be influenced by content, so that for example, nodes having many “Jazz” files will connect to other similar nodes. Thus, semantically related nodes form a Semantic Overlay Network (SON). Queries are routed to the appropriate SONs, increasing the chances that matching files will be found quickly, and reducing the search load on nodes that have unrelated content. We have evaluated SONs by using an actual snapshot of music-sharing clients. Our results show that SONs can significantly improve query performance while at the same time allowing users to decide what content to put in their computers and to whom to connect.

Performing efficient decentralized search is a fundamental problem in Peer-to-Peer (P2P) systems. There has been a significant amount of research recently on developing robust self-organizing P2P topologies that support efficient search. In this paper we discuss four structured and unstructured P2P models (CAN, Chord, PRU, and Hypergrid) and three characteristic search algorithms (BFS, k-Random Walk, and GAPS) for unstructured networks. We report on the results of simulations of these networks and provide measurements of search performance, focusing on search in unstructured networks. We find that the proposed models produce small-world networks, and yet none exhibit power-law degree distributions. Our simulations also suggest that random graphs support decentralized search more effectively than the proposed unstructured P2P models. We also find that on these topologies, the basic breadth-first search algorithm and its simple variants have the lowest search cost.

We introduce a new distributed data structure, the Distributed-Hash Queue, which enables communication between Network-Address Translated (NATed) peers in a P2P network. DHQs are an extension of distributed hash tables (DHTs) which allow for push and pop operators vs. the traditional DHT put and get operators. We describe the architecture in detail and show how it can be used to build a delay-tolerant network for use in P2P applications such as delayed-messaging. We have developed an initial prototype implementation of the DHQ which runs on PlanetLab using the Pastry key-based routing protocol.

A well-known problem found in peer-to-peer systems is how to efficiently and scalably locate the peer that stores a particular data item. In a typical formulation of the problem solution, each data item is mapped to a key; every peer stores data items corresponding to a contiguous range of keys, and locating an item requires identifying the host that holds that item’s key. Here we describe Distributed Search Tree (DiST), a distributed lookup protocol based on a straightforward extension of the search tree concept. In DiST peers are assigned to groups, each group is responsible for a range of keys, and groups are located at the nodes of logical search tree. While our approach has comparable complexity to the best algorithms proposed so far (complexity is O ( logN )), we show that its flexible design puts it at an advantage when it comes to the application of common performance enhancing techniques such as caching and replication. As an example of such advantage we describe the improvement in data lookup time and resilience obtained with key caching and table lookup replication.

Unstructured P2P networks, despite having good characteristics such as the nonexistence of a single point of failure, the high levels of anonymity in the search operations and the exemplary dependability, have been found to be much less scalable than first expected. The flooding protocol, which is used for the discovery of peers and for the main operation of searching, seems to be responsible for this weakness. The adoption of some major improvements, such as the distinction between Leaf-nodes and Ultra-Peers, has partially overcome the scalability problems, but there is still a need for further optimization. Our proposed idea, aims to improve the effectiveness of the hierarchical scheme by applying some new criteria in the selection of potentially promotable nodes.

This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information relevant to a user query, an agent uses two histories : a query/retrieved document history(Q/RDH) and a query/sender agent history(Q/SAH). The former is a list of pairs of a query and retrieved document information, where the queries were sent by the agent itself. The latter is a list of pairs of a query and the address of a sender agent and shows “who sent what query to the agent”. This is useful for finding a new information source. Making use of the Q/SAH is expected to have a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads involved in performing a search. As an agent receives more queries, then more links to new knowledge are acquired. From this behavior, a “give and take”(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agent Kodama , and conducted experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing ’multicast’ techniques only to look up a target agent.

Several different forms of peer-to-peer interactions, associations and interpersonal relations between human and artificial intelligences are described. We build upon a new form of grid computing which integrates human and artificial ‘processes’ in electronically saturated physical spaces, called socio-cognitive grids . We start from the analysis of three scenarios in P2P applications: digital rights management , mass user support and customer-to-customer interaction . These enable us to identify those factors that motivate the computing components in the socio-cognitive grids to form social structures, individually incorporating socio-cognitive intelligence and social awareness. In order to study the emergent properties of these social structures, such as reciprocity, social exchange and social networking, we need a theory that will help us understand the dynamics of social integration and support. We explore the use of a classical sociological theory of social structures and interpersonal relations. Subsequently we outline the components of a software simulation built on this theory and designed to formalize and evaluate this socio-computational intelligence. Ultimately our main aim is to analyse and understand those emergent properties that lead to the formation of stable and scalable social structures in socio-cognitive grids.

We study the design of policies for virtual communities of agents based on peer-to-peer systems or the grid infrastructure. In a virtual community agents can play both the role of resource consumers and the role of resource providers. Moreover, the agents remain in control of their resources, and therefore we distinguish between the authorization to access a resource given by the virtual community and the permission to do so issued by the resource providers. We propose a logical multiagent framework for virtual communities that distinguishes three roles: resource consumption, provision, as well as authorization.

Peer-to-peer (P2P) architectures exhibit attractive properties for a wide range of real world systems. As a result they are increasingly being applied in the design of applications ranging from high-capacity file sharing and global scale distributed computing to business team-ware. The objective of this paper is to outline a number of areas in which Agent techniques for the management of social problems such as decision making or fair trading amongst autonomous agents could be used to help structure P2P actions. In particular we focus on approaches from mechanism design, argumentation theory and norms / rules and electronic institutions.

Peer-to-Peer (P2P) data management systems combine traditional schema-based integration techniques with the P2P infrastructure. In this paper, we propose a P2P data management framework named PEPSINT that semantically integrates heterogeneous XML and RDF data sources, using a hybrid architecture and a global-as-view approach. Our focus is on the query processing techniques over heterogeneous data. Queries in PEPSINT are expressed in XQuery and in RDQL. We consider two types of queries, depending on whether the query is first posed on the super peer or on one of the peers.