非结构化对等网信息缓存资源查找算法

泛洪式路由(Flooding)是在非结构化对等网络中进行有效资源查找的主要方式,但其路由过程将产生大量冗余消息,因而严重制约了其可扩展性.随节点数增多,泛洪式路由的网络带宽消耗也变得异常严重.为此,本文提出了改善非结构化对等网络路由和查找效率的LHC (LookaHead with Catch) 算法,该算法利用缓存邻居节点的邻居列表和邻居节点的资源索引表,达到优化路由的目的.仿真实验表明,在保证维护和查询的带宽开销与现有算法基本一致的基础上,LHC算法不仅更有效的减少了查询过程中产生的冗余消息,而且在网络容量一定的情况下,资源命中率更高.     

基于B+树的分布式哈希表路由结构

在基于分布式哈希表构造的对等网络中,路由表的结构影响关键字的查询效率。B+树是一种有效查找的树型索引结构。考虑便于管理网络中众多的节点路由信息,提出一种基于B+树的路由结构,它通过为节点的路由信息建立索引,不仅提高了查询效率,将查找长度控制在树的高度内,而且使每个节点维护的路由信息尽可能少,减少了存储开销。     

基于贝叶斯网的P2P系统路由查找算法

对等网络中的一个关键问题就是如何找到储存有期望数据的节点,因而目标资源的查询算法研究是P2P网络的关键部分,该算法决定了P2P系统的性能。在对等网络中的每一个节点都存有一张记录与之相邻的节点的路由信息表,着重讨论如何根据各节点所存储的路由表建立相应的贝叶斯网络,并分析某一节点接收到查询请求的概率,进而得出一个计算概率的数学公式。为使所得概率计算公式尽可能地符合现实情况,每一个对等点分别被赋予不同的权,随后对所赋权进行调整。最后,对每个节点的可信度提出一个设置方案,并基于可信度提出一个改进的路由算法,试验表明该算法能够一定程度上改善对等网络的性能。     

数据驱动的信息中心网络认知路由协议

随着网络技术的迅速发展和新型应用的不断涌现,网络中的数据急剧增长给基于 TCP/IP的Internet带来严峻挑战。为了支持用户对海量数据的访问,信息中心网络（information‐centric networking , ICN ）成为未来互联网的研究热点之一。提出一种数据驱动的ICN认知路由协议。每个路由节点都被赋予感知、询问、学习、推理和反馈等认知行为;通过感知行为,节点获取本地拓扑、路由请求处理信息和流行内容等;通过询问行为,节点构建邻居缓存表等;通过学习行为,节点得到经验路径信息等;通过推理行为,分析节点间关系,构建朋友缓存表等;通过反馈行为,基于指引信息,作出路由决策等。仿真结果表明,提出的协议是可行和有效的,具有较好的性能。     

一种支持多维数据范围查询的对等计算索引框架

如何有效地支持多维数据范围查询是传统数据管理领域的研究热点之一.但是,在大规模分布式系统中,这仍然是一个具有挑战性的研究工作.VBI-tree是一个对等计算环境下基于平衡树的索引架构,在该架构上可以实现集中式环境下的多种支持多维数据索引的层次化树结构,例如R-tree,X-tree和M-tree等.VBI-tree设计的查询算法保证查询可以从树的任意位置开始,而不是像集中式环境下层次化树结构那样采用从树的根节点开始查询的方法,从而成功地避免了根节点引起的系统性能瓶颈问题.对于有N个节点的网络,索引方法可以保证查询效率是O(log N).VBI-tree提出了基于AVL-tree旋转的网络重构负载均衡策略可以有效地均衡负栽.另外,在数据操作频繁的情况下,为了提高索引的性能,在VBI-tree上建立特殊的祖先-子孙链接形成VBI-tree的结构.通过使用祖先-子孙链接,可保证对于相关查询区域的探索尽量发生在同层节点之间,而不是一直往根节点方向发送,从而减轻上层节点的查询负担,并且显著地降低了更新代价.模拟实验验证了提出的方法的有效性.     

基于剩余能量的无线传感网路由算法设计

小规模、分布集中的WSNs(Wireless Sensor Networks,无线传感器网络)适宜采用平面路由协议,但在平面路由协议中,传统的洪泛路由算法以广播的方式在整个网络中传输查询请求和查询结果数据帧,消耗了较多的传感器节点能量,导致节点过早失效.论文对传统的洪泛路由算法进行改进,提出并实现了一种节能路由算法BRE-Flooding(Based on the Remaining Energy Flooding,基于剩余能量的洪泛算法).在算法中,节点依据剩余能量决定是否接收和转发数据帧;节点维护由剩余能量、距离网络中聚合节点(Aggregation Node)的跳数等信息组成的邻居路由信息表,并依据数据帧中表示该查询请求关键程度的属性,结合邻居路由信息表动态确定向哪几个(或全部)邻居节点转发数据帧.经实验仿真,表明所提算法在节省节点能量消耗及网络负载均衡方面具有较好的表现.     

基于中间轴的WSN双线路由机制

针对现有的基于虚拟坐标的路由协议不能本地感知、数据传输易受阻及能量消耗不均衡等缺点,设计了一种基于中间轴的双线路由机制,利用网络的中间轴为每个节点分配虚拟坐标,并在该虚拟坐标上实施双线路由机制。用户节点可以在不知道源节点位置的情况下找到自己感兴趣的数据,而且可以在复杂环境中确保数据的发送,有效解决其他路由协议中空洞边界或公共节点的通信热点问题。通过实验,对节点负载的规范化标准偏差及高负载节点数量进行计算,结果表明,设计的方法能获得较好的负载平衡,避免网络热点产生。     

一种有效的空间数据仓库区域聚集查询索引结构

空间数据仓库有效地支持对空间数据的管理和分析，提供更加全面的决策支持．讨论了一种有效的空间决策支持手段——空间区域聚集查询的实现．基于aggregate cubetree和aR—tree提出了一个可以有效地在空间维和非空间维上进行区域聚集查询的索引结构aCR-tree及其相关算法，并计算分析了查询算法的时间复杂度．与现有技术相比aCR-tree降低了存储代价和每次查询访问的节点数，通过实验证明，该索引结构可以提供较好的存储性能和查询性能．     

面向时延优化的Overlay路由策略研究

路由扩展性是Overlay网络的研究热点,其中网络时延作为Overlay路由性能的一个关键指标已成为重点研究内容之一.文中对Overlay路由及其扩展性问题进行描述,并在此基础上进行数学建模和分析.针对时延优化目标,提出一种基于蚁群算法的单跳路由路径策略来处理邻居节点集合维护的问题:通过蚂蚁爬行的过程建立邻居节点集合,并根据设定的质量评估函数进行修剪来控制每个节点上维护的邻居节点集合大小.通过仿真实验对Overlay路由性能进行的分析,证实了Overlay路由在时延优化方面的可行性,同时实验结果表明了ACOHPR在对平均时延影响很小的情况下能够有效地降低每个节点的邻居节点集合大小.     

基于语义查询的P2P路由模型研究

针对结构化P2P网络路由模型中存在难以有效支持内容定位的问题,提出基于语义查询的P2P路由模型.利用概念向量来描述文档、节点、簇和查询请求;通过节点概念向量间的相似度聚成不同层次的语义簇,并采用基于概念相似度计算进行路由选取和转发的路由策略;利用节点的异构性,让性能好的超级节点承担更多的工作量,并引入服务节点便于快速导航语义簇中的语义类别.实验结果表明,该路由模型能有效提高系统的搜索效率.     

Linking identical neighborly partitions for efficient high-dimensional similarity search in unstructured peer-to-peer systems

Peer-to-Peer (P2P) computing has recently attracted a great deal of research attention. In a P2P system, a large number of nodes can potentially be pooled together to share their resources, information, and services. However, existing unstructured P2P systems lack support for content-based search over data objects which are generally represented by high-dimensional feature vectors. In this paper, we propose an efficient and effective indexing mechanism to facilitate high-dimensional similarity query in unstructured P2P systems, named Linking Identical Neighborly Partitions (LINP), which combines both space partitioning technique and routing index technique. With the aid of LINP, each peer can not only process similarity query efficiently over its local data, but also can route the query to the promising peers which may contain the desired data. In the proposed scheme, each peer summarizes its local data using the space partitioning technique, and exchanges the summarized index with its neighboring peers to construct routing indices. Furthermore, to improve the system performance with peer updates, we propose an extension of the LINP, named LINP⁺, where each peer can reconfigure its neighboring peers to keep relevant peers nearby. The performance of our proposed scheme is evaluated over both synthetic and real-life high-dimensional datasets, and experimental results show the superiority of our proposed scheme.     

Continually Answering Constraint k-NN Queries in Unstructured P2P Systems

We consider the problem of efficiently computing distributed geographical k-NN queries in an unstructured peer-to-peer (P2P) system,in which each peer is managed by an individual organization and can only communicate with its logical neighboring peers.Such queries are based on local filter query statistics,and require as less communication cost as possible,which makes it more difficult than the existing distributed k-NN queries.Especially,we hope to reduce candidate peers and degrade communication cost.In this paper,we propose an efficient pruning technique to minimize the number of candidate peers to be processed to answer the k-NN queries.Our approach is especially suitable for continuous k-NN queries when updating peers,including changing ranges of peers,dynamically leaving or joining peers,and updating data in a peer. In addition,simulation results show that the proposed approach outperforms the existing Minimum Bounding Rectangle (MBR.)-based query approaches,especially for continuous queries.     

Maintenance strategies for routing indexes

Query processing in large-scale unstructured P2P networks is a crucial part of operating such systems. In order to avoid expensive flooding of the network during query processing so-called routing indexes are used. Each peer maintains such an index for its neighbors. It provides a compact representation (data summary) of data accessible via each neighboring peer. An important problem in this context is to keep these data summaries up-to-date without paying high maintenance costs. In this paper, we investigate the problem of maintaining distributed data summaries in P2P-based environments without global knowledge and central instances. Based on a classification of update propagation strategies, we discuss several approaches to reduce maintenance costs and present results from an experimental evaluation.     

PSON: A scalable P2P file sharing system with efficient complex query support

A desired P2P file sharing system is expected to achieve the following design goals: scalability, routing efficiency and complex query support. In this paper, we propose a powerful P2P file sharing system, PSON, which can satisfy all the three desired properties. PSON is essentially a semantic overlay network of logical nodes. Each logical node represents a cluster of peers that are close to each other. A powerful peer is selected in each cluster to support query routing on the overlay network while the less powerful peers are responsible for the maintenance of shared contents. To facilitate query routing, super peers are organized in form of a balanced binary search tree. By exploiting the concept of semantics, PSON can support complex queries in a scalable and efficient way. In this paper, we present the basic system design such as the semantic overlay construction, query routing and system dynamics. A load balancing scheme is proposed to further enhance the system performance. By simulation experiments, we show that PSON is scalable, efficient and is able to support complex queries.     

Web search results caching service for structured P2P networks

This paper proposes a two-level P2P caching strategy for Web search queries. The design is suitable for a fully distributed service platform based on managed peer boxes (set-top-box or DSL/cable modem) located at the edge of the network, where both boxes and access bandwidth to those boxes are controlled and managed by an ISP provider. Our solution significantly reduces user query traffic going outside of the ISP provider to get query results from the respective Web search engine. Web users are usually very reactive to worldwide events which cause highly dynamic query traffic patterns leading to load imbalance across peers. Our solution contains a strategy to quickly ease imbalance on peers and spread communication flow among participating peers. Each peer maintains a local result cache used to keep the answers for queries originated in the peer itself and queries for which the peer is responsible for by contacting the Web search engine on-demand. When query traffic is predominantly routed to a few responsible peers our strategy replicates the role of “being responsible for” to neighboring peers so that they can absorb query traffic. This is a fairly slow and adaptive process that we call mid-term load balancing. To achieve a short-term fair distribution of queries we introduce a location cache in each peer which keeps pointers to peers that have already requested the same queries in the recent past. This lets these peers share their query answers with newly requesting peers. This process is fast as these popular queries are usually cached in the first DHT hop of a requesting peer which quickly tends to redistribute load among more and more peers.     

基于IPv6的三层混合P2P覆盖网络模型

研究现有的非结构化对等(P2P)网络,结合IPv6以及人类社会资源查找方法,提出一种三层混合P2P覆盖网络模型。在该模型中,具有相同IPv6网络前缀的节点组成一个域,域内节点根据属性相似性划分为若干个兴趣组,组内节点管理功能由智能节点完成,域内路由节点负责跨域路由消息。仿真结果表明,该模型能提高资源查找效率,缩短查找时延,减少消息路由跳数。     

HAPS: Supporting Effective and Efficient Full-Text P2P Search with Peer Dynamics

Recently, peer-to-peer (P2P) search technique has become popular in the Web as an alternative to centralized search due to its high scalability and low deployment-cost. However, P2P search systems are known to suffer from the problem of peer dynamics, such as frequent node join/leave and document changes, which cause serious performance degradation. This paper presents the architecture of a P2P search system that supports full-text search in an overlay network with peer dynamics. This architecture, namely HAPS, consists of two layers of peers. The upper layer is a DHT (distributed hash table) network interconnected by some super peers (which we refer to as hubs). Each hub maintains distributed data structures called search directories, which could be used to guide the query and to control the search cost. The bottom layer consists of clusters of ordinary peers (called providers), which can receive queries and return relevant results. Extensive experimental results indicate that HAPS can perform searches effectively and efficiently. In addition, the performance comparison illustrates that HAPS outperforms a flat structured system and a hierarchical unstructured system in the environment with peer dynamics.     

Associative search in peer to peer networks: Harnessing latent semantics

The success of a P2P file-sharing network highly depends on the scalability and versatility of its search mechanism. Two particularly desirable search features are scope (ability to find infrequent items) and support for partial-match queries (queries that contain typos or include a subset of keywords). While centralized-index architectures (such as Napster) can support both these features, existing decentralized architectures seem to support at most one: prevailing unstructured P2P protocols (such as Gnutella and FastTrack) deploy a “blind” search mechanism where the set of peers probed is unrelated to the query; thus they support partial-match queries but have limited scope. On the other extreme, the recently-proposed distributed hash tables (DHTs) such as CAN and CHORD, couple index location with the item’s hash value, and thus have good scope but can not effectively support partial-match queries. Another hurdle to DHTs deployment is their tight control of the overlay structure and the information (part of the index) each peer maintains, which makes them more sensitive to failures and frequent joins and disconnects.We develop a new class of decentralized P2P architectures. Our design is based on unstructured architectures such as Gnutella and FastTrack, and retains many of their appealing properties including support for partial match queries, and relative resilience to peer failures. Yet, we obtain orders of magnitude improvement in the efficiency of locating rare items. Our approach exploits associations inherent in human selections to steer the search process to peers that are more likely to have an answer to the query. We demonstrate the potential of associative search using models, analysis, and simulations.     

基于拥塞控制的非结构化P2P网络的路由负载均衡策略

非结构化P2P(unstructured peer-to-peer network)对等网络中的节点资源定位的路由查询是对等网络研究中的一个主要难题,特别是当网络中客户端节点由于其频繁加入、离开导致网络结构动态变化所带来的资源查询难题.提出了一种新的基于拥塞控制的路由查询方法来实现动态网络下的资源查询.该方法分两部分实现:首先是网络资源的分组与节点重连策略.该策略使得具有同等资源的节点相互连接,并周期性地调整节点上的节点连接数量以减少同组资源节点上的负载.通过以上策略,使得网络的拓扑结构自动地从随机网络结构进化到以资源组为单位的聚类网络,从而使得网络中形成网络资源组间的查询负载均衡.另一方面,组内的节点之间的路由负载均衡是通过节点间协同学习实现的.采用协同Q-学习方法,所研究的方法不仅从节点上学习其处理能力、连接数和资源的个数等参数,还将节点的拥塞状态作为协同Q-学习的重要参数,并建立模型.通过这种技术,同一组节点上的资源查询被有目的地引导,以避开那些组内拥塞的节点,从而最终实现资源组内节点之间的查询均衡.仿真实验结果表明,相比常用的random walk资源查找方法,该研究所实现的资源定位方法能够更迅速地实现网络的资源查询.仿真结果还表明,相比random walk方法,所提出的方法在网络高强度查询和网络节点动态加入和退出的情况下进行查询具有更高的鲁棒性和适应性.     

Continually Answering Constraint <Emphasis Type="Italic">k</Emphasis>-<Emphasis Type="Italic">NN</Emphasis> Queries in Unstructured P2P Systems

We consider the problem of efficiently computing distributed geographical k-NN queries in an unstructured peer-to-peer （P2P） system, in which each peer is managed by an individual organization and can only communicate with its logical neighboring peers. Such queries are based on local filter query statistics, and require as less communication cost as possible which makes it more difficult than the existing distributed k-NN queries. Especially, we hope to reduce candidate peers and degrade communication cost. In this paper, we propose an efficient pruning technique to minimize the number of candidate peers to be processed to answer the k-NN queries. Our approach is especially suitable for continuous k-NN queries when updating peers, including changing ranges of peers, dynamically leaving or joining peers, and updating data in a peer. In addition, simulation results show that the proposed approach outperforms the existing Minimum Bounding Rectangle （MBR）-based query approaches, especially for continuous queries.