Gnutella网络映射:大规模P2P系统的宏观属性

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"这篇文章是关于对Gnutella网络进行映射和分析的研究,探讨了大型对等(P2P)系统的一些宏观属性。作者Matei Ripeanu和Ian Foster通过研究Gnutella的网络拓扑结构和路由机制,评估其性能、可靠性和可扩展性。他们发现Gnutella网络虽非纯粹的幂律网络,但其当前配置具有幂律结构的特征,并估计了网络中的数据流量。" Gnutella网络是一个早期的对等文件共享系统,它的设计基于P2P架构,即用户之间直接交换数据,无需中心服务器。这种去中心化的模式在2000年代初引起了广泛的关注,因为它允许大规模的分布式文件共享。尽管P2P技术发展迅速,但对于这类系统的量化评估却相对较少。 论文中提到的“映射Gnutella网络”指的是研究人员使用的技术来探索和理解Gnutella网络的结构。他们专注于网络的覆盖层(overlay network),这是一个由Gnutella应用构建的虚拟网络,拥有自己的路由策略。覆盖层的拓扑和路由机制对于决定P2P应用的关键性能指标,如速度、稳定性和可扩展性,有着重要影响。 研究者发现,Gnutella网络并非严格遵循幂律分布,这是一种在许多复杂网络中观察到的特征,其中少数节点拥有大量连接,而大多数节点则有较少的连接。幂律网络在某些方面是有益的,比如可以容忍节点的随机失联,但也可能导致部分节点过载。尽管Gnutella网络不完全符合这一模式,但它仍然展现出类似幂律的特性,这在实际操作中可能既有优点也有缺点。 此外,研究者估计了整个Gnutella网络中的数据传输总量,这有助于理解网络的负载情况以及对网络基础设施的需求。这项工作揭示了Gnutella网络的实际运行情况,为优化P2P系统设计提供了宝贵的数据和见解,对于后续的P2P系统开发和性能改进具有重要意义。 “Mapping the Gnutella Network”这篇论文是P2P网络研究的重要贡献,它深入探讨了Gnutella网络的特性,不仅增加了我们对P2P系统行为的理解,也为未来的设计和优化提供了理论基础。

A. Encoding Network of PFSPNet The encoding network is divided into three parts. In the part I, RNN is adopted to model the processing time pij of job i on all machines, which can be converted into a fixed dimensional vector pi. In the part II, the number of machines m is integrated into the vector pi through the fully connected layer, and the fixed dimensional vector p˜i is output. In the part III, p˜i is fed into the convolution layer to improve the expression ability of the network, and the final output η p= [ η p1, η p2,..., η pn] is obtained. Fig. 2 illustrates the encoding network. In the part I, the modelling process for pij is described as follows, where WB, hij , h0 are k-dimensional vectors, h0, U, W, b and WB are the network parameters, and f() is the mapping from RNN input to hidden layer output. The main steps of the part I are shown as follows. Step 1: Input pij to the embedding layer and then obtain the output yij = WB pij ; Step 2: Input yi1 and h0 to the RNN and then obtain the hidden layer output hi1 = f(yi1,h0; U,W, b). Let p1 = h1m ; Step 3: Input yij and hi,j−1, j = 2, 3 ··· , m into RNN in turn, and then obtain the hidden layer output hij = f(yij ,hi,j−1; U,W, b), j = 2, 3 ··· , m. Let pi = him . In the part II, the number of machines m and the vector pi are integrated by the fully connected layer. The details are described as follows. WB and h˜i are d-dimensional vectors, WB W and ˜b are network parameters, and g() denotes the mapping from the input to the output of full connection layer. Step 1: Input the number of machines m to the embedding layer, and the output m = WB m is obtained。Step 2: Input m and pi to the fully connected layer and then obtain the output hi = g([m, pi];W, b); Step 3: Let pi = Relu(hi). In the part III, pi, i = 1, 2,...,n are input into onedimensional convolution layer. The final output vector η pi, i = 1, 2, ··· , n are obtained after the output of convolutional layer goes through the Relu layer.首先逐行仔细的分析此过程,其次怎么使用pytorch用EncoderNetwork类完全实现这个过程的所有功能和步骤

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