集成P2P模式的网格资源发现算法研究

网格计算和对等计算有很多可以融合的特征。在传统的网格环境中,资源的发现和定位主要用集中式或者分层式来解决,随着网格规模的扩大,这种方式明显不适合网格环境。另一方面,P2P为大规模分布式环境下有效地发现资源提供了可扩展性方案。首先提出了一种集成P2P模式的网格资源管理模型,然后基于该模型提出了一种融合遗传和蚂蚁算法的资源发现算法。理论分析和仿真证明了遗传蚂蚁算法能有效地提高P2PGrid环境下的资源发现性能。     

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.     

Distributed context aware collaborative filtering approach for P2P service selection and recovery in wireless mesh networks

With the evolution of large number of social networking sites in which various users share the information at various levels in Peer-to-Peer (P2P) manner, there is a need of efficient P2P collaborative mechanisms to achieve efficiency and accuracy at each level. To achieve high level of accuracy and scalability, a distributed collaborative filtering (CF) approach for P2P service selection and recovery is proposed in this paper. The proposed approach is different from the traditional centralized approaches as both user and network views are modelled and an estimation of the service recovery time is included if some of the services are failed during execution. A novel Context Aware P2P Service Selection and Recovery (CAPSSR) algorithm is proposed. To filter the relevant contents for user needs, a new Distributed Filtering Metric (DFM) is included in the algorithm which selects the contents based upon the user input. The performance of the proposed algorithm is evaluated with traditional centralized algorithm with respect to scalability and accuracy. The results obtained show that the proposed approach is better than the existing approaches in terms of accuracy and scalability.     

Fighting pollution attacks in P2P streaming

In recent years, the demand for multimedia streaming over the Internet is soaring. Due to the lack of a centralized point of administration, Peer-to-Peer (P2P) streaming systems are vulnerable to pollution attacks, in which video segments might be altered by any peer before being shared. Among existing proposals, reputation-based defense mechanisms are the most effective and practical solutions. We performed a measurement study on the effectiveness of this class of solutions. We implemented a framework that allows us to simulate different variations of the reputation rating systems, from the centralized global approaches to the decentralized local approaches, under different parameter settings and pollution models. One key finding is that a centralized reputation system is only effective in static network and in defending against light pollution attacks. In general, a fully distributed reputation system is more suitable for the “real-time” P2P streaming system, since it is better in handling network dynamics and fast in detecting the polluters. Based on this key finding, we propose DRank, a fully distributed rank-based reputation system. Experimental results show that this technique is more flexible and robust in fighting pollution attacks.     

基于投影寻踪和MapReduce的并行案例推理模型

在大规模、高维度的数据环境下,传统的案例推理具有计算复杂度高、实时性差等缺点。为在大数据环境下进行案例推理,提出了一种基于投影寻踪和MapReduce的并行推理模型dpCBR。在数据预处理阶段,计算源案例到基准向量的一维投影距离并缓存,降低计算复杂度并减少重复计算开销。在案例检索阶段,先根据投影距离裁剪案例库,再进行相似度匹配,减少不必要的案例匹配开销。应用MapReduce进行分布式并行处理,使dpCBR具备对大规模案例库的推理能力。实验结果表明,dpCBR模型可以明显提高大数据环境下案例推理的效率。     

一种基于DHT的P2P数据存储模型的设计

P2P（peer-to-peer）组织模式已经成为新一代互联网应用的重要形式,它为应用带来了更好的扩展性、容错性和高性能。P2P数据存储模式一直是业界所关注的热点,被认为是P2P最具前途的应用之一。设计了一种基于DHT（DistributedHashTable）路由的结构化P2P网络为架构的分布式数据存储模型,通过应用测试证明该模型能稳定运行。     

Combining feature reduction and case selection in building CBR classifiers

CBR systems that are built for the classification problems are called CBR classifiers. This paper presents a novel and fast approach to building efficient and competent CBR classifiers that combines both feature reduction (FR) and case selection (CS). It has three central contributions: 1) it develops a fast rough-set method based on relative attribute dependency among features to compute the approximate reduct, 2) it constructs and compares different case selection methods based on the similarity measure and the concepts of case coverage and case reachability, and 3) CBR classifiers built using a combination of the FR and CS processes can reduce the training burden as well as the need to acquire domain knowledge. The overall experimental results demonstrating on four real-life data sets show that the combined FR and CS method can preserve, and may also improve, the solution accuracy while at the same time substantially reducing the storage space. The case retrieval time is also greatly reduced because the use of CBR classifier contains a smaller amount of cases with fewer features. The developed FR and CS combination method is also compared with the kernel PCA and SVMs techniques. Their storage requirement, classification accuracy, and classification speed are presented and discussed.     

基于P2P的隐含语义索引模型的研究

P2P作为一种新型的网络结构正受到越来越多的关注。目前在大多数P2P网络中的信息检索方法都是依据关键词匹配,通过查询请求与信息标识之间的简单匹配关系来获得查询结果。但是关键词匹配会产生很多用户不需要的结果。隐含语义索引是基于文本语义的检索模型。为提高系统的查准率,扩展在P2P下的查询方式,本文提出了在P2P网络中引入隐含语义索引模型进行信息检索,并模拟实现了一个基于P2P网络的隐含语义索引模型的试验平台。     

相似案例自适应选择算法及其应用

为提高相似案例选择的效率和准确性，将有向无环图支持向量机（DAGSVM）多类分类器应用到相似案例选择中．提出多类分类器有效分辨阈值的概念，在保证一定案例选择准确度的前提下．对自适应构造案例集进行相似案例选择．提高相似案例选择的效率．将该方法应用于光动力治疗（PDT）鲜红斑痣（PWS）案例推理专家系统．实验结果表明了该方法的有效性．     

分布式环境下基于语义相似的案例检索

分布式环境下的异构案例表达制约了案例检索过程中案例属性之间的可比性，进而成为分布式环境下案例推理系统成败的一个关键问题。该文提出基于语义相似的案例检索，通过利用Ontology技术来理解案例属性的内在含义，在此基础上定义并计算属性之间的相似程度。对原型系统的初步测试证明了基于语义相似的案例检索有效性。     

基于分组式P2P网络的空间区域查询

针对传统集中式空间数据应用出现的性能瓶颈以及结构化P2P系统中由于数据的一致性分布而导致的空间数据物理特性丢失等问题,提出一种分组式P2P网络系统,并描述在该网络系统下的数据插入和删除、节点的加入和离开以及空间区域查询。通过仿真验证了其有效性。     

基于分布化元数据管理的P2P文件共享模型研究

P2P模式的文件共享系统在Internet上得到广泛应用,但在无中央服务器的纯P2P文件共享系统中,多关键词检索问题还没有得到很好解决。针对此问题,文章提出基于分布化元数据管理DMM(DistributedMetadataManagement)的P2P文件共享模型,基于模型对多关键词检索进行分析,并以资源描述框架RDF[1]和结构化纯P2P结构P-Grid[9]为基础,介绍了支持多关键词检索的P2P文件共享模型的实现。     

基于对等监控网络的云监控系统设计与实现

为解决传统方案中单节点带来的性能瓶颈和低可靠性问题,基于对等监控网络设计并实现了云监控系统解决方案。在硬件部署上,该解决方案将监控节点封装在应用容器中,分布式部署在不同机架上,组建对等的监控网络;监控节点间采用非关系型数据库构建分布式存储集群,实现了监控数据的异地访问和备份。在软件实现上,该解决方案进行了分层设计,采用推拉结合的方式收集数据,对采集的数据进行可信度评估和分布式存储,使用阈值控制和主机空闲评估相结合的策略对云中主机进行智能化管控。通过系统测试,发现该监控系统对计算资源的平均占用率仅有2.17%;而1 ms内响应读写请求的平均比率达到93%以上,表明该解决方案具有资源消耗率低、高频次读写效率高的性能优势。     

Hybrid genetic algorithms and case-based reasoning systems for customer classification

Abstract: Because of its convenience and strength in complex problem solving, case-based reasoning (CBR) has been widely used in various areas. One of these areas is customer classification, which classifies customers into either purchasing or non-purchasing groups. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most previous studies have tried to optimize the weights of the features or the selection process of appropriate instances. But these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than naive models. In particular, there have been few attempts to simultaneously optimize the weights of the features and the selection of instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm. To validate the usefulness of our approach, we apply it to two real-world cases for customer classification. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.     

Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system

Abstract: Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of complex and unstructured decision-making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence techniques. In this study, we introduce a genetic algorithm to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build a prediction model for customer profitability level. Experimental results show that our genetic-algorithm-optimized CBR approach outperforms other artificial intelligence techniques for this multi-class classification problem.     

Scalable and topology-aware reconciliation on P2P networks

Collaborative applications are characterized by high levels of data sharing. Optimistic replication has been suggested as a mechanism to enable highly concurrent access to the shared data, whilst providing full application-defined consistency guarantees. Nowadays, there are a growing number of emerging cooperative applications adequate for Peer-to-Peer (P2P) networks. However, to enable the deployment of such applications in P2P networks, it is required a mechanism to deal with their high data sharing in dynamic, scalable and available way. Previous work on optimistic replication has mainly concentrated on centralized systems. Centralized approaches are inappropriate for a P2P setting due to their limited availability and vulnerability to failures and partitions from the network. In this paper, we focus on the design of a reconciliation algorithm designed to be deployed in large scale cooperative applications, such as P2P Wiki. The main contribution of this paper is a distributed reconciliation algorithm designed for P2P networks (P2P-reconciler). Other important contributions are: a basic cost model for computing communication costs in a DHT overlay network; a strategy for computing the cost of each reconciliation step taking into account the cost model; and an algorithm that dynamically selects the best nodes for each reconciliation step. Furthermore, since P2P networks are built independently of the underlying topology, which may cause high latencies and large overheads degrading performance, we also propose a topology-aware variant of our P2P-reconciler algorithm and show the important gains on using it. Our P2P-reconciler solution enables high levels of concurrency thanks to semantic reconciliation and yields high availability, excellent scalability, with acceptable performance and limited overhead.     

Reputation-based dependable scheduling of workflow applications in Peer-to-Peer Grids

Grids facilitate creation of wide-area collaborative environment for sharing computing or storage resources and various applications. Inter-connecting distributed Grid sites through peer-to-peer routing and information dissemination structure (also known as Peer-to-Peer Grids) is essential to avoid the problems of scheduling efficiency bottleneck and single point of failure in the centralized or hierarchical scheduling approaches. On the other hand, uncertainty and unreliability are facts in distributed infrastructures such as Peer-to-Peer Grids, which are triggered by multiple factors including scale, dynamism, failures, and incomplete global knowledge.In this paper, a reputation-based Grid workflow scheduling technique is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Peer-to-Peer Grid environments. The proposed approach builds upon structured peer-to-peer indexing and networking techniques to create a scalable wide-area overlay of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers mediated via Grid resource brokers. The proposed algorithm dynamically adapts to changing resource conditions and offers significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. The results evaluated through an extensive trace driven simulation show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.     

Global optimization of case-based reasoning for breast cytology diagnosis

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways – (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.     

Lifetime and availability of data stored on a P2P system: Evaluation of redundancy and recovery schemes

This paper studies the performance of Peer-to-Peer storage and backup systems (P2PSS). These systems are based on three pillars: data fragmentation and dissemination among the peers, redundancy mechanisms to cope with peers churn and repair mechanisms to recover lost or temporarily unavailable data. Usually, redundancy is achieved either by using replication or by using erasure codes. A new class of network coding (regenerating codes) has been proposed recently. Therefore, we will adapt our work to these three redundancy schemes. We introduce two mechanisms for recovering lost data and evaluate their performance by modeling them through absorbing Markov chains. Specifically, we evaluate the quality of service provided to users in terms of durability and availability of stored data for each recovery mechanism and deduce the impact of its parameters on the system performance. The first mechanism is centralized and based on the use of a single server that can recover multiple losses at once. The second mechanism is distributed: reconstruction of lost fragments is iterated sequentially on many peers until that the required level of redundancy is attained. The key assumptions made in this work, in particular, the assumptions made on the recovery process and peer on-times distribution, are in agreement with the analysis in [1] and in [2] respectively. The models are thereby general enough to be applicable to many distributed environments as shown through numerical computations. We find that, in stable environments such as local area or research institute networks where machines are usually highly available, the distributed-repair scheme in erasure-coded systems offers a reliable, scalable and cheap storage/backup solution. For the case of highly dynamic environments, in general, the distributed-repair scheme is inefficient, in particular to maintain high data availability, unless the data redundancy is high. Using regenerating codes overcomes this limitation of the distributed-repair scheme. P2PSS with centralized-repair scheme are efficient in any environment but have the disadvantage of relying on a centralized authority. However, the analysis of the overhead cost (e.g. computation, bandwidth and complexity cost) resulting from the different redundancy schemes with respect to their advantages (e.g. simplicity), is left for future work.     

基于P-范式模型的P2P网络分组查询算法

P2P系统的可用性取决于查找数据的有效方法。利用节点兴趣和节点与中心节点的通信延迟建立链接,动态分组P2P网络的节点,查询节点通过中心节点转发搜索请求给其他中心节点,中心节点收到搜索请求后,若查找资源的主题排在本组关注的前K（K一般取1～3）位,则搜索本组内所有节点。在此基础上,提出了一种基于P-范式模型的P2P网络分组查询算法。算法分析和实验结果表明该算法的性能优于MSW查询算法。