一种非结构化P2P的随机有向图拓扑模型

在非结构化P2P系统中,P2P拓扑直接影响了网络的性能,如何构建P2P拓扑是一件重要的问题.采用一个简单的映射将非结构化P2P抽象成一个G(V,E)图,在G(V,E)及其非结构化P2P拓扑的特点基础上,提出了一种非结构化P2P的随机有向图拓扑模型,并讨论了该拓扑模型的度分布、连通性和直径,为研究非结构化P2P系统的性能和算法协议提供了仿真的理论基础.     

Building a tuple space on structured peer-to-peer networks

Research on peer-to-peer (P2P) networks has attracted much attention recently. A key factor is their ability to handle dynamics in the system in a distributed fashion, including ad hoc and dynamic join and departure of the peers and dynamic changes in the underlying network environment. As more and more innovating P2P applications appear, the need to support intuitive communication and synchronization among the peers in the P2P system becomes imperative. In this paper, we discuss how to build a tuple space on top of P2P systems and use on-line games as a target application. A simple API is supported for accessing the data stored in the tuple space and communicating between the peers. Through experiments and a demonstration game, we show that our system can facilitate the development of network games in a fully distributed environment.

Semantic indexing in structured peer-to-peer networks

The past few years have seen tremendous advances in distributed storage infrastructure. Unstructured and structured overlay networks have been successfully used in a variety of applications, ranging from file-sharing to scientific data repositories. While unstructured networks benefit from low maintenance overhead, the associated search costs are high. On the other hand, structured networks have higher maintenance overheads, but facilitate bounded time search of installed keywords. When dealing with typical data sets, though, it is infeasible to install every possible search term as a keyword into the structured overlay.     

Performance analysis of structured peer-to-peer overlays for mobile networks

Distributed Hash Table (DHT) based Peer-to-Peer (P2P) overlays have been widely researched and deployed in many applications such as file sharing, IP telephony, content distribution and media streaming applications. However, their deployment has largely been restricted to fixed, wired networks. This is due to the fact that supporting P2P overlays on wireless networks such as the public mobile data network is more challenging due to constraints in terms of data transmissions on cellular networks, limited battery power of the handsets and increased levels of node churn. However, the proliferation of smartphones makes the use of P2P applications on mobile handsets very desirable. In this article, we have analysed and evaluated the performance and efficiency of five popular DHT based structured P2P overlays (Chord, Pastry, Kademlia, Broose and EpiChord) under conditions as commonly experienced in public mobile data networks. Our results show that the conditions in mobile networks, including a high churn rate and the relatively low bandwidth availability is best matched by Kademlia and EpiChord. These overlays exhibit a high lookup success ratio and low hop count while consuming a moderate amount of bandwidth. These characteristics make these two overlays suitable candidates for use in mobile networks.     

A file replication facility for berkeley unix

This paper describes two mechanisms – reproduction sets and metafiles – that provide low-cost, semi-automatic file replication and location transparency facilities for an interconnected collection of machines running Berkeley Unix. A reproduction set is a collection of files that the system attempts to keep identical; this is done on a ‘best effort’ basis, with the system relying on the user to handle unusual situations. A metafile is a special file that contains symbolic pathnames of other files, each of which may be on any machine in the network; opening a metafile results in opening an available constituent file. Examples are given to illustrate the use of these mechanisms. Their implementation and performance are also described.     

Large-scale multimedia content delivery over optical networks for interactive TV services

iTVP is a system built for delivery of live TV programming, video-on-demand and audio-on-demand with interactive access over IP networks. It has a nationwide range and is designed to provide service to a high number of concurrent users. Hence, scalability is one of the most important issues. In this paper we described how scalability is achieved in a two-level hierarchical architecture of the delivery system. We present the principles of content caching mechanisms and the management of the resulting distributed content repository. The system is implemented and currently operates in a test installation. Although the range of the test system operations is limited, as far as the number of users and the size of the content repository is concerned, the experience gained with iTVP so far provides an insight into the expected prototype performance. We present a system performance evaluation and describe the experience gained during the iGrid demonstration.     

Neighbor selection for proportional fairness in P2P networks

This paper analyzes reciprocation strategies in peer-to-peer networks from the point of view of the resulting resource allocation. Our stated aim is to achieve through decentralized interactions a weighted proportionally fair allocation. We analyze the desirable properties of such allocation, as well as an ideal proportional reciprocity algorithm to achieve it, using tools of convex optimization. We then seek suitable approximations to the ideal allocation which impose practical constraints on the problem: numbers of open connections per peer, with transport layer-induced bandwidth sharing, and the need of random exploration of the peer-to-peer swarm. Our solution in terms of a Gibbs sampler dynamics characterized by a suitable energy function is implemented in simulation, comparing favorably with a number of alternatives.     

A hybrid approach for estimating document frequencies in unstructured P2P networks

Scalable search and retrieval over numerous web document collections distributed across different sites can be achieved by adopting a peer-to-peer (P2P) communication model. Terms and their document frequencies are the main components of text information retrieval and as such need to be computed, aggregated, and distributed throughout the system. This is a challenging problem in the context of unstructured P2P networks, since the local document collections may not reflect the global collection in an accurate way. This might happen due to skews in the distribution of documents to peers. Moreover, central assembly of the total information is not a scalable solution due to the excessive cost of storage and maintenance, and because of issues related to digital rights management. In this paper, we present an efficient hybrid approach for aggregation of document frequencies using a hierarchical overlay network for a carefully selected set of the most important terms, together with gossip-based aggregation for the remaining terms in the collections. Furthermore, we present a cost analysis to compute the communication cost of hybrid aggregation. We conduct experiments on three document collections, in order to evaluate the quality of the proposed hybrid aggregation.     

A P2P-based intelligent resource discovery mechanism in Internet-based distributed systems

Internet-based distributed systems enable globally-scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale supercomputing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages, with one message for each attribute, leading to high overhead of memory consumption, node communication, and subsequent merging operation. Another approach can report and discover a multi-attribute resource using one query by reducing multi-attribute to a single index, but it is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This paper presents a P2P-based intelligent resource discovery (PIRD) mechanism that weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P overlay. PIRD can discover resources geographically close to requesters by relying on a hierarchical P2P structure. It significantly reduces overhead and improves search efficiency and effectiveness in resource discovery. It further incorporates the Lempel–Ziv–Welch algorithm to compress attribute information for higher efficiency. Theoretical analysis and simulation results demonstrate the efficiency of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency of resource discovery.     

AutoProc: An automatic proactive replication scheme for P2P storage

Due to the prevalence of peer departures, P2P storage systems need to constantly generate replicas for high durability. However, few existing systems are able to select the right replication rate that balances replication cost and durability, e.g., minimizing replication cost without threatening the durability target. In this paper, we propose a method called AutoProc to automate the replication process in P2P storage systems. AutoProc employs two key techniques to enable cost-effective replication: correct...     

Winnowing: Protecting P2P systems against pollution through cooperative index filtering

Pollution (i.e., sharing of corrupted files, or contaminating index information with bogus index records) is a de facto problem in many file sharing peer-to-peer (P2P) systems in use today. Pollution squanders network resources and frustrates users with unprofitable downloads (due to corrupted files) and unproductive download trials (due to bogus index records). In this paper, we propose a novel distributed hash table (DHT)-based anti-pollution scheme called winnowing. Winnowing aims to reduce or eliminate decoy index records (pointing to nonexisting or corrupted files) held by DHT (i.e., index) nodes in the system, so that download attempts based on the remaining (clean) index records are more likely to yield satisfactory results. To achieve this goal, two techniques are used: (1) publish verification is performed by index nodes to counteract index pollution and (2) privacy-preserving object reputation is integrated into the DHT to reduce the impact of content and metadata pollution. By integrating these techniques, winnowing converges quickly to a near-optimal solution. Winnowing has the added benefit that it does not reveal a peer's download history to other downloading peers.The publish verification of winnowing has been implemented on top of the latest eMule client, and extensive data has been collected from the Kad network using this modified client. The measurement results are summarized, and the findings from the measurement study are incorporated into an analytical model. The model demonstrates the robustness of the privacy-preserving object reputation of winnowing to a variety of pollution attacks, and to attacks on winnowing itself. The results of analysis are confirmed by means of event-driven simulations.     

P2Care: A dynamic peer-to-peer network for collaboration in personalized healthcare service delivery

In the healthcare domain, the quality of personalized service delivery is strongly tied with collaboration among multiple stakeholders for accurate and reliable access to a variety of distributed and heterogeneous information and services. Existing healthcare information systems lack collaboration since they offer limited accessibility to resources and pose many restrictions in terms of scalability and flexibility. In this work, we propose P2Care, a decentralized, dynamic, scalable and self-organized network that exploits the functionalities of the structured peer-to-peer networks in order to provide collaboration. P2Care provides efficient ubiquitous access to medical information by organizing participating entities into groups according to common characteristics and interests. It incorporates enhanced lookup mechanisms with low response time and signaling overhead. The performance of the P2Care system has been evaluated through simulations. A prototype application demonstrates the effectiveness of the proposed system for cooperation and collaboration in order to facilitate personalized service delivery.     

基于历史信息的对等网络负载均衡算法

针对当前对等网络中存在的负载不均衡问题,提出了基于历史信息查询的负载均衡算法(HILBA),以提高系统的整体心?实验结果表明,此算法在节点同构和异构环境下都提高了系统的负载均衡能力.     

Practical load balancing for content requests in peer-to-peer networks

This paper studies the problem of balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentralized load balancing techniques in distributed systems base their decisions on periodic updates containing information about load or available capacity observed at the serving entities. We show that these techniques do not work well in the peer-to-peer context; either they do not address peer node heterogeneity, or they suffer from significant load oscillations which result in unutilized capacity. We propose a new decentralized algorithm, Max-Cap, based on the maximum inherent capacities of the replica nodes. We show that unlike previous algorithms, it is not tied to the timeliness or frequency of updates, and consequently requires significantly less update overhead. Yet, Max-Cap can handle the heterogeneity of a peer-to-peer environment without suffering from load oscillations. Mema Roussopoulos is an Assistant Professor of Computer Science on the Gordon McKay Endowment at Harvard University. Before joining Harvard, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University. She received her PhD and Master’s degrees in Computer Science from Stanford, and her Bachelor’s degree in Computer Science from the University of Maryland at College Park. Her interests are in the areas of distributed systems, networking, and mobile and wireless computing. Mary Baker is a Senior Research Scientist at HP Labs. Her research interests include distributed systems, networks, mobile systems, security, and digital preservation. Before joining HP Labs she was on the faculty of the computer science department at Stanford University where she ran the MosquitoNet project. She received her PhD from the University of California at Berkeley.     

Solution for the broadcasting in the Kademlia peer-to-peer overlay

Structured peer-to-peer networks are capable of fast and efficient lookup operations as a distributed hash table. The topology of these networks makes it possible to send broadcast messages among nodes, either for the purpose of providing a complex query service for participants, or to disseminate information valuable for all nodes. In this article a broadcast algorithm for the Kademlia XOR topology is presented. The algorithm, which was developed specifically for Kademlia, uses replication mechanisms similar to that of the storage and retrieval service of this overlay topology. This allows for increased reliability and speed of the broadcast, and also efficient operation, as the routing table for lookups are already available and can be used. An analytical model is presented, which can be used to calculate the required level of replication for any desired reliability at runtime, and is validated with simulation as well.     

Defending passive worms in unstructured P2P networks based on healthy file dissemination

Propagation of passive worms in unstructured peer-to-peer (P2P) networks can result in significant damages and the loss of network security. This paper obtains the average delay for all peers in the entire transmitting process, and proposes a mathematical model for simulating unstructured P2P networks-based passive worms' propagation taking into account network throughput. According to the file popularity which follows the Zipf distribution, we propose a new healthy file dissemination-based defense strategy. Some parameters related to the propagation of passive worms are studied based on the proposed model. Finally, the simulation results verify the effectiveness of our model, which can provide an important guideline in the control of passive worms in unstructured P2P networks.     

Characterizing the file hosting ecosystem: A view from the edge

We present a comprehensive, longitudinal characterization study of the file hosting ecosystem using HTTP traces collected from a large campus network over a one-year period. We performed detailed multi-level analysis of the usage behavior, infrastructure properties, content characteristics, and user-perceived performance of the top five services in terms of traffic volume, namely RapidShare, Megaupload, zSHARE, MediaFire, and Hotfile. We carefully devised methods to identify user clickstreams in the HTTP traces, including the identification of free and premium user instances, as well as the identification of content that is split into multiple pieces and downloaded using multiple transactions. Throughout this characterization, we compare and contrast these services with each other as well as with peer-to-peer file sharing and other media sharing services.     

A distributed data clustering algorithm in P2P networks

Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.