Combining search and trust models in unstructured peer-to-peer networks

Effectiveness of Peer-to-Peer (P2P) systems highly depends on efficiency and scalability of their search algorithms. Moreover, managing trust is a key issue for wide acceptance of P2P computing. Surprisingly, the majority of the available trust systems ignore the underlying search algorithm and assume it is preexisting. We claim that combining search and trust systems yields significant performance gains in terms of network traffic and query success rate. In this paper, we propose a robust and efficient trust based search framework for unstructured P2P networks. Our framework maintains limited size routing indexes combining search and trust data to guide queries to most reputable nodes. By dynamically selecting reputable nodes as score managers, our scheme tracks the reputation of participating peers. In an alternative approach, we aggregate partial reputation values obtained from reverse query paths to introduce a low overhead method for estimating reputation scores of peers. Through P2P network simulation experiments, we find significant performance gains in using our framework.     

Collaborative SVM classification in scale-free peer-to-peer networks

Distributed classification in large-scale P2P networks has gained relevance in recent years and support applications like distributed intrusion detection in P2P monitoring environments, online match-making, personalized information retrieval, distributed document classification in a P2P media repository and P2P recommender systems to mention a few. However, classification in a P2P network is a challenging task due to the constraints such as centralization of data is not feasible, scarce communication bandwidth, scalability, synchronization and peer dynamism. Moreover, without considering data distributions and topological scenarios of real world P2P systems, most of the existing distributed classification approaches lack in their predictive and network-cost performance. In this paper, we investigate a collaborative classification method (TRedSVM) based on Support Vector Machines (SVM) in Scale-free P2P networks. In particular, we demonstrate how to construct SVM classifier in real world P2P networks which exhibit inherently skewed distribution of node links and eventually data. The proposed method propagates the most influential instances of SVM models to the vast majority of scarcely connected peers in a controlled way that improves their local classification accuracy and, at the same time, keeps the communication cost low throughout the network. Besides using benchmark Machine Learning data sets for extensive experimental evaluations, we have evaluated the proposed method particularly for music genre classification to exhibit its performance in a real application scenario. Additionally, performance analysis is carried out with respect to centralized approaches, data replication in P2P networks and cost accuracy trade-off. TRedSVM outperforms baseline approaches of model propagation by improving the overall classification performance substantially at the cost of a tolerable increase in communication.     

An inexpensive unstructured platform for wireless mobile peer-to-peer networks

In this paper, we propose an unstructured platform, namely I nexpensive P eer-to- P eer S ubsystem (IPPS), for wireless mobile peer-to-peer networks. The platform addresses the constraints of expensive bandwidth of wireless medium, and limited memory and computing power of mobile devices. It uses a computationally-, memory requirement- and communication- wise inexpensive gossip protocol as the main maintenance operation, and exploits location information of the wireless nodes to minimize the number of link-level messages for communication between peers. As a result, the platform is not only lightweight by itself, but also provides a low cost framework for different peer-to-peer applications. In addition, further enhancements are introduced to enrich the platform with robustness and tolerance to failures without incurring any additional computational and memory complexity, and communication between peers. In specific, we propose schemes for a peer (1) to chose a partner for a gossip iteration, (2) to maintain the neighbors, and (3) to leave the peer-to-peer network. Simulation results are given to demonstrate the performance of the platform.

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

Distributed caching in unstructured peer-to-peer file sharing networks

Nowadays, the peer-to-peer (P2P) system is one of the largest Internet bandwidth consumers. To relieve the burden on Internet backbone and improve the query and retrieve performance of P2P file sharing networks, efficient P2P caching algorithms are of great importance. In this paper, we propose a distributed topology-aware unstructured P2P file caching infrastructure and design novel placement and replacement algorithms to achieve optimal performance. In our system, for each file, an adequate number of copies are generated and disseminated at topologically distant locations. Unlike general believes, our caching decisions are in favor of less popular files. Combined with the underlying topology-aware infrastructure, our strategy retains excellent performance for popular objects while greatly improves the caching performance for less popular files. Overall, our solution can reduce P2P traffic on Internet backbone, and relieve the over-caching problem that has not been properly addressed in unstructured P2P networks. We carry out simulation experiments to compare our approaches with several traditional caching strategies. The results show that our algorithms can achieve better query hit rates, smaller query delay, higher cache hit rates, and lower communication overhead.     

A path-traceable query routing mechanism for search in unstructured peer-to-peer networks

Unstructured Peer-to-Peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. Searching for content in unstructured P2P networks is a challenging task because the distribution of objects has no association with the organization of peers. Proposed methods in recent years either depend too much on objects replication rate or suffer from a sharp decline in performance when objects stored in peers change rapidly, although their performance is better than flooding or random walk algorithms to some extent. In this paper, we propose a novel query routing mechanism for improving query performance in unstructured P2P networks. We design a data structure called traceable gain matrix (TGM) that records every query's gain at each peer along the query hit path, and allows for optimizing query routing decision effectively. Experimental results show that our query routing mechanism achieves relatively high query hit rate with low bandwidth consumption in different types of network topologies under static and dynamic network conditions.     

基于非结构化对等网络的分散式桌面网格平台

描述了一个基于非结构化对等网络、可以共享网络上空闲资源的JVMs虚拟机的桌面网格平台UDDG(unstructureddecentralized desktop grid)。提出了对等实体的最小活跃邻居节点数、更新时间域值等概念,每个对等实体维护了一个最小活跃邻居数的列表,结合非结构化对等网络的网络跳数的机制,通过广播查找消息来寻找资源的虚拟机。通过构建评测环境,运行并行案例程序计算结果表明,UDDG提供了一种构建高性能的桌面网格平台的新思路。     

基于子树间快捷连接的非结构化P2P资源搜索方法

通过对非结构化P2P网络资源搜索方法的研究,提出了一种P2P覆盖网络。网络采用多路平衡树形拓扑结构,当新节点加入时在其与根节点的各子树间创建仅参与查询消息第一跳转发的快捷连接,搜索过程中利用快捷连接实现查询消息在根的各子树间并行转发并且不依赖根节点。仿真比较了所提网络同Gnutella和随机漫步网络的性能,所提网络具有低消息冗余率、高搜索成功率和较低的平均搜索时间复杂度。分析结果表明所提网络在资源搜索方面是高效可行的。     

A novel approach to improving search efficiency in unstructured peer-to-peer networks

The decentralized peer-to-peer (P2P) technique has been widely used to implement scalable file sharing systems. It organizes nodes in a system into a structured or unstructured network. The advantages of the unstructured P2P systems are that they have lower maintenance complexity and can better adapt to node heterogeneity as well as network dynamics. However, the search process in unstructured systems is not as efficient as in structured P2P systems because the same search message may go through a node multiple times. To facilitate the complex search and improve the search efficiency, we propose a novel approach of assigning identifications to nodes in an unstructured system. Our method can prevent a node from receiving duplicate search messages and retain the low maintenance overhead for the system. The performance evaluations demonstrate that the proposed approach can improve the search efficiency of unstructured P2P systems while keeping the maintenance overhead at a comparable or even lower level, compared with the traditional unstructured systems.     

改进的非结构化P2P网络搜索算法

如何高效地搜索资源是P2P网络中最为关键的问题.非结构化的对等网络,一般以广播方式作为其搜索的基本策略,引发较大的网络流量.针对以上问题,提出了一种利用节点积累的经验指导节点传播查询的路由搜索算法.在该算法中,通过记录节点关注的主题、主题的信息量大小和满足主题的目标节点,并建立对应关系表.当节点收到查询后,就利用该表来指导节点选择查询,以便更快地找到查询结果.仿真结果表明,该算法有效地减少了查询带来的网络流量,提高了查找的成功率.     

Adding structure to unstructured peer-to-peer networks: the use of small-world graphs

The “small-world” graph structure is pervasive and is observed to arise “without-design” or “naturally” in many practical systems such as the World Wide Web. In contrast to natural systems, overlay networks provide an opportunity to design structure. We seek the advantages of designing overlay topologies with small-world properties to support file sharing in peer-to-peer networks. We focus on two metrics of performance: (a) search protocol performance, a local gain perceived directly by peer-to-peer network users and (b) network utilization, a global property that is of interest to network service providers. We propose a class of overlay topologies and show, by simulation, that a particular topology instance of this class where every node has many close neighbors and few random neighbors (i.e., a small-world graph) exhibits very good properties. In this overlay topology, the chances of locating files are high, and the nodes where these files are found are, on average, close to the query source. This improvement in search protocol performance is achieved while decreasing the traffic load on the links in the underlying network. We propose a simple greedy algorithm to construct such overlay topologies where each node operates independently and in a decentralized manner to select its neighbors.     

Distributed multi-source streaming models in peer-to-peer overlay networks

A multimedia contents are distributed to peer computers (peers) and a contents peer which holds contents can provide other peers with the contents in peer-to-peer (P2P) overlay networks. Here, contents peers are mainly realized in less-reliable and low-performance personal computers. Multimedia streaming is more significant than downloading ways in multimedia applications from security and economical reasons. We discuss distributed multi-source streaming models to support peers with reliable and scalable multimedia streaming service. Here, a collection of multiple contents peers in parallel transmit packets of a multimedia content to a leaf peer to realize the reliability and scalability. Each of the contents peers send different packets from the other contents peers at slower rate. Even if not only some number of peers stop by fault and are degraded in performance but also some number of packets are lost and delayed in networks, a leaf peer has to receive every data of a content at the required rate. We discuss how to replicate data of a multimedia content by creating a parity packet for some number of packets and to allocate packets to each contents peer so that a leaf peer can deliver a packet without waiting for preceding packets from other contents peers in presence of the faults. Next, multiple contents peers are required to be synchronized to send packets to a leaf-peer so that the leaf-peer can receive every data of a content at the required rate. We discuss a pair of gossip-based flooding-based protocols, directed acyclic graph (DAG)-based coordination protocol (DCoP) and tree-based (TCoP) coordination protocol to synchronize multiple contents peers to send in parallel send to a leaf peer. First, some number of contents peers are selected and start transmitting packets to a leaf peer. Then, each of the selected peers selects some number of peers. Here, a peer can be selected by multiple peers in DCoP but by at most one peer in TCoP. Finally, every contents peer transmits packets to the leaf peer at the allocated rate. We evaluate the coordination protocols DCoP and TCoP in terms of how long it takes and how many messages are transmitted to synchronize multiple contents peers.     

A dynamic coalition formation game for search efficient adaptive overlay construction in unstructured peer-to-peer networks

A great number of recent works deal with improving search in peer-to-peer systems, specifically by clustering peers into semantic groups. When the process of clustering is predetermined and static, it suffers from lack of adaptation to highly dynamic peer-to-peer environments. We model the problem as a non-superadditive coalition game with non-transferable utility characteristic function, and propose a distributed dynamic coalition formation algorithm through myopic best-reply with experiment rule to solve the coalition formation problem. Coalitions are formed by peers with similar interests considering geographical proximity. The overlay network is dynamically reconfigured over time based on the changes in the interests or locations of the individual peers. The convergence of the proposed algorithm using “core solution” concept is studied. The simulation results show that the proposed algorithm can efficiently reduce the search time, although the overhead of the overlay adaptation is slightly higher.     

Location awareness in unstructured peer-to-peer systems

Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.     

An incentive mechanism for message relaying in unstructured peer-to-peer systems

Distributed message relaying is an important function of a peer-to-peer system to discover service providers. Existing search protocols in unstructured peer-to-peer systems create huge burden on communications, cause long response time, or result in unreliable performance. Moreover, with self-interested peers, these systems are vulnerable to the free-riding problem. In this paper we present an incentive mechanism that not only mitigates the free-riding problem, but also achieves good system efficiency in message relaying for peer discovery. In this mechanism promised rewards are passed along the message propagation process. A peer is rewarded if a service provider is found via a relaying path that includes this peer. The mechanism allows peers to rationally trade-off communication efficiency and reliability while maintaining information locality. We provide some analytic insights to the symmetric Nash equilibrium strategies of this game, and an approximate approach to calculate this equilibrium. Experiments show that this incentive mechanism brings a system utility generally higher than breadth-first search and random walks, based on both the estimated utility from our approximate equilibrium and the utility generated from learning in the incentive mechanism.     

Searching in peer-to-peer networks

As peer-to-peer networks are proving capable of handling huge volumes of data, the need for effective search tools is lasting and imperative. During the last years, a number of research studies have been published, which attempt to address the problem of search in large, decentralized networks. In this article, we mainly focus on content and concept-based retrieval. After providing a useful discussion on terminology, we introduce a representative sample of such studies and categorize them according to basic functional and non-functional characteristics. Following our analysis and discussion we conclude that future work should focus on information filtering, re-ranking and merging of results, relevance feedback and content replication as well as on related user-centric aspects of the problem.     

Meta-searches in peer-to-peer networks

We propose a method for carrying out enhanced collaborative searches, called meta-searches, in peer-to-peer networks. In addition to performing regular searches, our method supports searches based on other network users’ previous searches on the same or similar topic. In essence, when a user performs a search, s/he will receive not only the usual result set, but also information on other users’ previous results, as well as relevancy information (such as how many times a resource that appeared in the result set was successfully downloaded). The core components of meta-search are query relevancy calculation, query matching algorithms, and relevancy file format. In this paper we discuss the underlying concepts and principles, and describe the component design in detail. Meta-search provides a way of benefiting from other users’ successful searches without any additional effort, thus potentially improving the efficiency and experience of a search.     

一种基于本体的P2P网络搜索方法

现有P2P网络规模大、动态性高、异构性强.有效的搜索技术一直是P2P系统研究中的核心问题.本文针对无结构P2P网络泛洪搜索机制的盲目性所导致的查询开销大、效率低的问题,提出了一种基于本体的P2P网络搜索方法OA.该方法给出了一个基于本体的P2P网络搜索模型,并设计了一个全局共享的节点本体来表示和组织节点知识,以提高检索质量.同时,设计了一个节点聚类算法PCA来改善消息路由.实验结果表明,OA算法比Gnutella算法实现了更高的查准率和查全率.