Affinity P2P: A self-organizing content-based locality-aware collaborative peer-to-peer network

The last years have brought a dramatic increase in the popularity of collaborative Web 2.0 sites. According to recent evaluations, this phenomenon accounts for a large share of Internet traffic and significantly augments the load on the end-servers of Web 2.0 sites. In this paper, we show how collaborative classifications extracted from Web 2.0-like sites can be leveraged in the design of a self-organizing peer-to-peer network in order to distribute data in a scalable manner while preserving a high-content locality. We propose Affinity P2P (AP2P), a novel cluster-based locality-aware self-organizing peer-to-peer network. AP2P self-organizes in order to improve content locality using a novel affinity-based metric for estimating the distance between clusters of nodes sharing similar content. Searches in AP2P are directed to the cluster of interests, where a logarithmic-time parallel flooding algorithm provides high recall, low latency, and low communication overhead. The order of clusters is periodically changed using a greedy cluster placement algorithm, which reorganizes clusters based on affinity in order to increase the locality of related content. The experimental and analytical results demonstrate that the locality-aware cluster-based organization of content offers substantial benefits, achieving an average latency improvement of 45%, and up to 12% increase in search recall.     

SLACER: a self-organizing protocol for coordination in peer-to-peer networks

Coordination between nodes in open distributed computer systems is a general problem that's becoming increasingly relevant as massive peer-to-peer (P2P) systems are being deployed on the Internet. A major subproblem is establishing and maintaining cooperation between nodes. To solve this problem, we created a simple algorithm, SLACER (a selfish link-based adaptation for cooperation excluding rewiring). When executed in a P2P network's nodes, SLACER self-organizes the network into a robust artificial social network (ASN) with small-world characteristics and high cooperation.     

Asynchronous privacy-preserving iterative computation on peer-to-peer networks

Privacy preserving algorithms allow several participants to compute a global function collaboratively without revealing local information to each other. Examples of applications include trust management, collaborative filtering, and ranking algorithms such as PageRank. Most solutions that can be proven to be privacy preserving theoretically are not appropriate for highly unreliable, large scale, distributed environments such as peer-to-peer (P2P) networks because they either require centralized components, or a high degree of synchronism among the participants. At the same time, in P2P networks privacy preservation is becoming a key requirement. Here, we propose an asynchronous privacy preserving communication layer for an important class of iterative computations in P2P networks, where each peer periodically computes a linear combination of data stored at its neighbors. Our algorithm tolerates realistic rates of message drop and delay, and node churn, and has a low communication overhead. We perform simulation experiments to compare our algorithm to related work. The problem we use as an example is power iteration (a method used to calculate the dominant eigenvector of a matrix), since eigenvector computation is at the core of several practical applications. We demonstrate that our novel algorithm also converges in the presence of realistic node churn, message drop rates and message delay, even when previous synchronized solutions are able to make almost no progress.     

P2P网络行为学研究

对等网络结构不断发展,网络中节点的行为逐渐成为影响网络效率的重要因素.激励、信誉和公平性已经成为对等网络中的三大难题,然而这三者都属于网络行为学的范畴.针对P2P网络行为学中的难题,提出了解决方法即在设计P2P系统的同时要兼顾激励.信誉和公平性.并对这三者分别进行了分析比较,针对当今不同P2P结构中的节点行为进行了评价.最后进一步展望了P2P网络行为学的研究方向.     

GenSoFNN: a generic self-organizing fuzzy neural network

Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. No initial rule base needs to be specified prior to training. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. However, most existing neural fuzzy systems (whether they belong to the first or second group) encountered one or more of the following major problems. They are (1) inconsistent rule-base; (2) heuristically defined node operations; (3) susceptibility to noisy training data and the stability-plasticity dilemma; and (4) needs for prior knowledge such as the number of clusters to be computed. Hence, a novel neural fuzzy system that is immune to the above-mentioned deficiencies is proposed in this paper. This new neural fuzzy system is named the generic self-organizing fuzzy neural network (GenSoFNN). The GenSoFNN network has strong noise tolerance capability by employing a new clustering technique known as discrete incremental clustering (DIC). The fuzzy rule base of the GenSoFNN network is consistent and compact as GenSoFNN has built-in mechanisms to identify and prune redundant and/or obsolete rules. Extensive simulations were conducted using the proposed GenSoFNN network and its performance is encouraging when benchmarked against other neural and neural fuzzy systems.     

一种位置感知的对等网络模型研究*

为了尽可能地在本地访问网络资源,从而缩短网络访问时间,提出了一种位置感知的分布式生成树LDST模型,使得地理上邻近的节点被分在逻辑上靠近的组中。通过采用底层网络中节点间跳数或消息延迟作为覆盖网络中两个节点间的距离,制定了节点在LDST模型中代表元和邻近组的选取规则,给出了建立无标杆的、分层的、位置感知的覆盖网络的构造算法。数学分析和仿真结果表明,LDST模型具有小世界特性,节点加入算法具有对数时间复杂度,LDST模型具有较好的易扩展性和应用价值。     

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。     

Automatic triangular grid generation on a free-form surface using a particle self-organizing system

Engineering with Computers - A novel method for automatic triangular grid generation on a free-form surface based on Coulomb’s law is proposed in this paper, which we refer to as the particle...     

Feature recognition using ART2: a self-organizing neural network

A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation scheme is adopted to represent the features by continuous-valued vectors, suitable to be input to the network. The face score is a measure of the face complexity based upon the convexity or concavity of the surrounding region. The face score vector depicts the topological relations between a face and its neighbouring faces. The ART2 network clusters similar features together. The similarity of the features within a cluster is controlled by a vigilance parameter. A new feature presented to the net is associated with one of the existing clusters, if the feature is similar to the members of the cluster. Otherwise, the net creates a new cluster. An algorithm of the ART2 network is implemented and tested with nine different features. The results obtained indicate that the network has significant potential for application to the problem of feature recognition.     

Category classification method using a self-organizing neural network

We apply a new category classification method to remote sensing data. This is a supervised and non-parametric method and employs both a selforganizing neural network and a k -nearest neighbour method. One of the features of the category is represented by the neuron weights after training the neural network based on a competitive learning role. From experimental results, we can see that the proposed method obtains superior classification results compared to other methods.     

Optimization via intermittency with a self-organizing neural network

One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem.     

IP core implementation of a self-organizing neural network

This paper reports on the design issues and subsequent performance of a soft intellectual property (IP) core implementation of a self-organizing neural network. The design is a development of a previous 0.65-/spl mu/m single silicon chip providing an array of 256 neurons, where each neuron stores a 16 element reference vector. Migrating the design to a soft IP core presents challenges in achieving the required performance as regards area, power, and clock speed. This same migration, however, offers opportunities for parameterizing the design in a manner which permits a single soft core to meet the requirements of many end users. Thus, the number of neurons within the single instruction multiple data (SIMD) array, the number of elements per reference vector, and the number of bits of each such element are defined by synthesis time parameters. The construction of the SIMD array of neurons is presented including performance results as regards power, area, and classifications per second . For typical parameters (256 neurons with 16 elements per reference vector) the design provides over 2 000 000 classifications per second using a mainstream 0.18-/spl mu/m digital process. A RISC processor, the array controller (AC), provides both the instruction stream and data to the SIMD array of neurons and an interface to a host processor. The design of this processor is discussed with emphasis on the control aspects which permit supply of a continuous instruction stream to the SIMD array and a flexible interface with the host processor.     

对等网联下NAT穿越问题的研究

采用"打洞"穿越锥型NAT与使用端口预测穿越对称型NAT相结合的方法,成功穿越了各种NAT,解决了对等联网下NAT的穿越问题。该方案既无需改变现有网络设备,又能确保内网的安全性,同时还解决了目前穿越方式存在的只能穿越部分类型的NAT、丢包、延时的问题。     

一种提高僵尸网络鲁棒性的对等网络拓扑结构

通过对现有的基于P2P技术的僵尸网络进行综述,通过研究分析发现,现有的P2P技术已不能满足僵尸网络的需求,为此我们在充分借鉴非结构化对等协议、结构化对等协议的基础上,提出了一种具有新型拓扑结构的对等网络协议AATP。经过实验表明,AATP具有良好的抗网络波动性。     

一种基于对等网络的云资源定位算法

提出用分布式哈希表(DHT)为每台云服务器产生一个唯一的节点编号,该编号作为网络拓扑结构、检索信息存储和信息查询共同的标志符,从而形成一个适合分布式计算的结构化P2P覆盖网.设计了新的拓扑和路由协议来解决云资源的常数跳定位问题.仿真实验表明,经典的P2P算法平均查找跳数与网络规模成正相关,无法依据云计算的实际需要人为地控制查找跳数;该算法的平均查找跳数与网络规模无关,随着网络规模的增大而趋向于设定值,可以解决云资源的常数跳定位问题.     

Blind source separation based on self-organizing neural network

This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. This is often true for real life applications. We propose an online learning solution using a neural network and use the nonstationarity of the sources to achieve the separation. The learning rule for the network's parameters is derived from the steepest descent minimization of a time-dependent cost function that takes the minimum only when the network outputs are uncorrelated with each other. In this process divide the problem into two learning problems one of which is solved by an anti-Hebbian learning and the other by an Hebbian learning process. We also compare the performance of our algorithm with other solutions to this task.     

Manufacturing cell formation using a new self-organizing neural network

Cellular manufacturing consists of grouping similar machines in cells and dedicating each of them to process a family of similar part types. In this paper, grouping parts into families and machines into cells is done in two steps: first, part families are formed and then machines are assigned. In phase one, weighted similarity coefficients are computed and parts are clustered using a new self-organizing neural network. In phase two, a linear network flow model is used to assign machines to families. To test the proposed approach, different problems from the literature have been solved. As benchmarks we have used a Maximum Spanning Tree heuristic.     

基于结构化对等网络的云资源查询算法

为实现云计算中云资源的快速查询,针对资源查找过程中查询效率较低以及网络维护成本较高等问题,提出一种基于结构化对等网络的云资源查询算法,实现对待查询云资源进行快速有效定位。首先设计一种新型超级节点拓扑结构,对网络拓扑中各节点进行唯一性编码,构造二元组路由信息索引列表,并设计相应的路由算法;然后给出了分层象限超级节点算法的查询效率与稳定性分析。仿真实验结果表明,分层象限超级节点算法查询效率较高,且随着网络规模增加,查询路径长度趋于稳定,同时对于超级节点失效带来的网络维护成本较低。     

一种基于对等网络的云资源多属性区间查询算法

设计了n元属性组来描述云资源, 并为属性组中的每个属性都划分区间。为解决云资源的多关键字高效查找问题, 对不同属性的不同区间的任意组合都建立索引。针对云资源属性变动时导致索引更新时网络开销太大的缺点, 提出依据索引中属性的个数对全部索引进行归类存储。仿真实验表明, 在云资源的属性发生变动时, 该算法在更新索引时在网络中产生的信息个数是一个常数n, 数目远远小于其他的多关键字区间查询算法, 查找资源时网络开销不仅小而且稳定。     

基于P2P技术的Gnutella网络搜索路由机制的改进

介绍了分布式P2P网络Gnutella模型消息搜索路由机制,在分析其存在大量冗余数据包传输问题的基础上,提出了一种基于分布式存储路由信息的搜索路由机制的改进策略,能有效地提高网络可扩展性和减少消息冗余。