共查询到19条相似文献,搜索用时 234 毫秒
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PATCOM:基于分割树的无结构P2P系统一致性维护方法 总被引:2,自引:0,他引:2
无结构P2P技术逐渐被应用在新型的协同计算系统中.这些新型业务支持数据的动态更新,不仅要求副本数据的强一致性,而且要求更新数据的快速传播.高效的一致性维护方法是保证新业务顺利开展的基础.在比较分析现有的P2P系统一致性维护方法的基础上,针对无结构P2P系统,提出了一种基于分割树的一致性维护方法--PATCOM.PATCOM使用Chord协议作为组管理协议,通过不断分割由副本节点组成的Chord环,动态地建立更新消息传播树(Update Message Propagation Tree,UMPT).论文进一步从理论上分析了UMPT的平均高度、PATCOM的性能、容错能力以及算法开销,并和基于Gossip的一致性维护方法进行了比较.理论分析和仿真实验结果表明:PATCOM不仅能够快速地维护P2P系统的强一致性,而且产生的冗余更新消息少. 相似文献
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在对NICE应用层组播协议研究的基础上,提出了一种新的应用层组播方案LCcast。它采取分层分簇的结构特征,从簇中选择出能力强的领导节点用Chord环组织起来。在数据传输方面,使用Chord环中定义的指向表并结合Dijkstra算法生成最小延迟的组播树,从而减少了传输延迟。同时,为了防止数据包丢失,对每一个簇选择了一个备用领导节点集合。仿真结果表明,LCcast组播方案在一定程度上减少了时间延迟,提高了平均数据传输率,降低了控制开销和领导节点的负载。 相似文献
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Dual-Chord:一种更加有效的分布式哈希表 总被引:9,自引:0,他引:9
在基于分布式哈希表构造的对等网中,关键字的搜索效率一直是一个非常重要的指标.Chord提出在Chord环上构建结构化的分布式哈希表.Chord协议中查找的过程是单一的顺时针方向.Dual—Chord通过对路由表的扩展,使得系统的查找策略可以根据关键字在Chord环上位置离当前节点的远近来确定查找的方向,这样大大的提高了在对等网中的查找效率.同时,Dual-Chord综合考虑了网络延时对查找的影响.在设计中也根据节点间的网络延时来优化查找的性能.实验表明,在对等网中Dual—Chord协议定义的查找算法效率比Chord定义的查找算法要高. 相似文献
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一种节能的无线传感器网络路由协议的设计与实现 总被引:1,自引:0,他引:1
在无线传感器网络的路由协议中,基于簇的路由协议在拓扑管理、能量利用、数据融合等方面具有优势。本文针对目前已有协议能量消耗大、网络寿命短等问题,提出了一种能量感知的基于分布式簇算法的无线传感器网络协议EA-HEED。此协议改进了分布式的簇头选举算法,分配时分复用时隙并在簇头节点建立一棵路由树,从而提高簇头选举效率;设计了休眠冗余节点的簇内活动节点调度算法,减少能耗;采用考虑节点能量和节点与基站距离的簇头节点组织路由树方法、最小化网络开销以及能量负载平衡方法,优化路由协议,有效延长网络寿命。仿真结果表明,与LEACH和HEED协议相比,EAHEED协议可以进一步延长网络寿命。 相似文献
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一种Ad hoc网络中动态自适应的路由更新算法 总被引:3,自引:0,他引:3
目前Ad hoc网络中基于簇的路由算法都采用了混合路由策略,其路由信息的更新范围局限在局部网络中(或簇内).提出了一种改进的路由更新算法-基于分簇机制的动态自适应路由更新算法.该算法使用簇头节点来进行簇内路由信息更新,使用簇头和网关节点来进行簇间路由信息更新,同时根据网络拓扑结构变化的快慢,动态地调整路由信息传播的范围.模拟结果显示该算法在使节点获得了较为准确的路由信息的前提下,有效地减少了路由信息更新所带来的控制开销. 相似文献
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针对边缘车联网中移动云计算传输延迟高、车辆高速移动且单一车辆节点存储能力有限而引起内容获取延迟过高的问题,建立基于协同式自适应巡航控制的车辆簇模型,提出基于簇头中继的多层缓存策略(CHRMC),实现内容缓存位置的最优选取。在建立相对稳定的簇内车间通信和多车辆协同缓存的情况下,利用簇头车中继传输的缓存策略,降低车辆移动性和存储资源有限性对内容缓存效率的影响。仿真结果表明,相比已有研究,所提策略能够在有效增加车辆簇内容缓存的同时,降低内容获取的延迟。 相似文献
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概化关联规则挖掘作为数据挖掘领域一个重要的拓展性研究课题,首先提出了一种概化扩展自然序树(generalized extended canonical-order tree,GECT)结构及其增量挖掘算法GECT-IM.该算法对原始分类事务数据库只扫描一次,就可以将所有交易信息映射至一棵压缩格式的GECT,然后通过对更新交易数据集扫描得到更新数据集中各项集的计数,结合相关性质及运算就可以发现大部分更新后的概化频繁项集;其次,针对GECT规模较大以及GECT-IM 算法仍然可能需要遍历初始GECT树的局限,在界定数据库更新和重构概念的基础上,基于一种可量化度量的准最小支持度阈值,提出了一种改进的准频繁概化扩展自然序树(pre-large generalized extended canonical-order tree,PGECT)结构及其增量挖掘算法PGECT-IM.由于有效避免了对初始GECT进行遍历的情形,从而进一步提升了概化关联规则增量挖掘效率.实验证明,提出的概化关联规则增量挖掘算法 GECT-IM 及其优化算法PGECT-IM,比现有增量挖掘算法具有更高的挖掘效率和更好的扩展性. 相似文献
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One of the major challenges in Peer-to-Peer (P2P) file sharing systems is to support content-based search. Although there have been some proposals to address this challenge, they share the same weakness of using either servers or super-peers to keep global knowledge, which is required to identify importance of terms to avoid popular terms in query processing. As a result, they are not scalable and are prone to the bottleneck problem, which is caused by the high visiting load at the global knowledge maintainers. To that end, in this paper, we propose a novel adaptive indexing approach for content-based search in P2P systems, which can identify importance of terms without keeping global knowledge. Our method is based on an adaptive indexing structure that combines a Chord ring and a balanced tree. The tree is used to aggregate and classify terms adaptively, while the Chord ring is used to index terms of nodes in the tree. Specifically, at each node of the tree, the system classifies terms as either important or unimportant. Important terms, which can distinguish the node from its neighbor nodes, are indexed in the Chord ring. On the other hand, unimportant terms, which are either popular or rare terms, are aggregated to higher level nodes. Such classification enables the system to process queries on the fly without the need for global knowledge. Besides, compared to the methods that index terms separately, term aggregation reduces the indexing cost significantly. Taking advantage of the tree structure, we also develop an efficient search algorithm to tackle the bottleneck problem near the root. Finally, our extensive experiments on both benchmark and Wikipedia datasets validated the effectiveness and efficiency of the proposed method. 相似文献
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《Advances in Engineering Software》2006,37(1):11-19
With the ever-growing web traffic, cluster-based web servers have become very important to the Internet infrastructure. Thus, making the best use of all available resources in the cluster to achieve high performance is a significant research issue. In this paper, we present Weblins, a cluster-based web server that can achieve good throughput. Weblins has Gobelins operating system as platform. Gobelins is an efficient single system image operating system that transparently makes use of the resources available in the cluster. The architecture of Weblins is fully distributed. Weblins implements a content-aware request distribution policy via a new interface on top of Gobelins. Popular web files are dynamically replicated on all nodes via a cooperative caching mechanism. For the non-popular files, the requests are handed-off to the corresponding nodes via the TCP Handoff protocol. Simulation results show that the strategy used by Weblins is more suitable for cluster-based Web severs in comparison with pure content-aware strategy and pure cooperative caching strategy. 相似文献
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名址分离网络中需要一个高性能、可扩展、分布式的映射解析系统,用来管理名称和地址之间的绑定信息,可靠有效地处理名称的位置查询。在映射系统的设计中,结构化分布式哈希表技术是使用最广的,为解决其中物理网络与逻辑网络的失配问题,以及高移动场景下的高更新成本问题,设计了一个基于位置关联Chord的名址分离映射系统。通过在逻辑网络中节点的路由表内添加物理网络的拓扑信息,改变了Chord环的递归查找过程。此外名称与地址的绑定关系分域内域外两级管理,域内直接绑定IP地址,域外更换绑定信息为名称与网络地址,通过增加一跳的查询将绑定信息更新范围尽可能地缩小在域内,提高了系统的映射解析性能。经理论分析和仿真测试验证,相较于LISP-DHT,基于位置关联Chord的映射系统的平均查询时延更小。 相似文献
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基于模型的数据采集技术可以有效抑制不必要的数据传输,节省能量开销,已经在传感器网络中得到广泛应用。对传统基于模型的数据采集进行了改进,提出基于卡尔曼滤波器的近似数据采样算法ADCA。ADCA可以在一定误差范围内有效获取数据。空间相近的节点被组织成簇,簇头和成员分别建立卡尔曼滤波模型,并保存对方的镜像模型。簇头节点可以为成员节点产生近似的数据,所以用户查询可以通过簇头来回答。实验表明ADCA具有较好的性能。 相似文献
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EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations 总被引:1,自引:0,他引:1
Jun-Ki Min Author Vitae 《Journal of Systems and Software》2010,83(2):271-282
In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES. 相似文献