Home Database Mysql Tutorial P2P技术体系结构与分类

P2P技术体系结构与分类

Jun 07, 2016 pm 03:39 PM
p2p Architecture Classification exist technology structure

P2P 技术存在三种结构模式的体系结构,即以 Napster 为代表的集中目录式结构、以 Gnutella 为代表的纯 P2P 网络结构和混合式 P2P 网络结构。从 P2P 技术的分代来说,到目前为止的 P2P 技术可分为四代:第一代 P2P( 中央控制网络体系结构 ) ,第二代 P2P( 分

 

P2P 技术存在三种结构模式的体系结构,即以Napster 为代表的集中目录式结构、以Gnutella 为代表的纯P2P 网络结构和混合式P2P 网络结构。从 P2P 技术的分代来说,到目前为止的P2P 技术可分为四代:第一代P2P( 中央控制网络体系结构) ,第二代P2P( 分散分布网络体系结构) ,第三代 P2P( 混合网络体系结构) ,第四代P2P( 目前发展中P2P 技术)

  1 、第一代P2P( 中央控制网络体系结构—— 集中目录式结构)

  集中目录式结构采用中央服务器管理P2P 各节点,P2P 节点向中央目录服务器 注册关于自身的信息( 名称、地址、资源和元数据) ,但所有内容存贮在各个节点中而非并服务器上,查询节点根据目录服务器中信息的查询以及网络流量和延迟等 信息来选择与定位其它对等点并直接建立连接,而不必经过中央目录服务器进行。集中目录式结构的优点是提高了网络的可管理性,使得对共享资源的查找和更新非 常方便; 缺点是网络的稳定性( 服务器失效则该服务器下的对等节点全部失效)

  2 、第二代P2P( 分散分布网络体系结构——P2P 网络结构)

  纯P2P 网络结构也被称作广播式的P2P 模型,它没有集中的中央目录服务器, 每个用户随机接入网络,并与自己相邻的一组邻居节点通过端到端连接构成一个逻辑覆盖的网络。对等节点之间的内容查询和内容共享都是直接通过相邻节点广播接 力传递,同时每个节点还会记录搜索轨迹,以防止搜索环路的产生。纯P2P 网络结构解决了网络结构中心化的问题,扩展性和容错性较好。由于没有一个对等节点 知道整个网络的结构,网络中的搜索算法以泛洪的方式进行,控制信息的泛滥消耗了大量带宽并很快造成网络拥塞甚至网络的不稳定,从而导致整个网络的可用性较 差,另外这类系统更容易受到垃圾信息,甚至是病毒的恶意攻击。

  3 、第三代P2P( 混合网络体系结构—— 混合式网络结构)

  混合式网络结构综合了纯P2P 去中心化和集中式P2P 快速查找的优势。按节点 能力不同( 计算能力、内存大小、连接带宽、网络滞留时间等) 区分为普通节点和搜索节点两类。搜索节点与其临近的若干普通节点之间构成一个自治的簇,簇内采 用基于集中目录式的P2P 模式,而整个P2P 网络中各个不同的簇之间再通过纯 P2P 的模式将搜索节点相连起来。可以在各个搜索节点之间再次选取性能最优的节点,或者另外引入一新的性能最优的节点作为索引节点来保存整个网络中可以利 用的搜索节点信息,并且负责维护整个网络的结构。由于普通节点的文件搜索先在本地所属的簇内进行,只有查询结果不充分的时候,再通过搜索节点之间进行有限 的泛洪。这样就极为有效地消除纯P2P 结构中使用泛洪算法带来的网络拥塞、搜索迟缓等不利影响。同时,由于每个簇中的搜索节点监控着所有普通节点的行为, 能确保一些恶意的攻击行为能在网络局部得到控制,在一定程度上提高整个网络的负载平衡。

  4 、第四代P2P( 发展中的P2P 技术)

  应该说第四代P2P 并没有形成真正的代,而是在原有技术的基础上作了改进,提出和应用了一些新技术措施。典型的有:

  (1) 动态口选择之一。目前的P2P 应用一般使用固定的端口,但是一些公司已 经开始引入协议可以动态选择传输口,一般说口的数目在1024~4000 之间。甚至P2P 流可以用原来用于HTTP(SMTP) 的口80(25) 来传输以 便隐藏。这将使得识别跨运营商网络的P2P 流,掌握其流量变得更困难。

  (2) 双向下载。eDBT 等公司进一步发展引入双向流下载。该项技术可以多路并行下载和上载一个文件和/ 或多路并行下载一个文件的一部分。而目前传统的体系结构要求目标在完全下载后才能开始上载。这将大大加快文件分发速度。

  (3) 智能结点弹性重叠网络。智能结点弹性重叠网络是系统应用P2P 技术来调度已有的IP 承载网资源的新技术,在路由器网络层上设置智能结点用各种链路对等连接,构成网络应用层的弹性重叠网。可以在保持互联网分布自治体系结构前提下、改善网络的安全性、QoS 和管理性。智能结点可以在路由器之间交换数 据,能够对数据分类( 分辩病毒、垃圾邮件) 保证安全。通过多个几何上分布的结点观察互联网,共享信息可以了解互联网蠕虫感染范围和性质。提供高性能、可扩 张、位置无关消息选路,以确定最近的本地资源位置。改进内容分发。使用智能结点探测互联网路径踪迹并且送回关于踪迹的数据; 解决目前互联网跨自治区路径选 择方面存在的问题。实现QoS 选路, 减少丢包和时延,快速自动恢复等。

 

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

Repo: How To Revive Teammates
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
1 months ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

This article is enough for you to read about autonomous driving and trajectory prediction! This article is enough for you to read about autonomous driving and trajectory prediction! Feb 28, 2024 pm 07:20 PM

Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p

The Stable Diffusion 3 paper is finally released, and the architectural details are revealed. Will it help to reproduce Sora? The Stable Diffusion 3 paper is finally released, and the architectural details are revealed. Will it help to reproduce Sora? Mar 06, 2024 pm 05:34 PM

StableDiffusion3’s paper is finally here! This model was released two weeks ago and uses the same DiT (DiffusionTransformer) architecture as Sora. It caused quite a stir once it was released. Compared with the previous version, the quality of the images generated by StableDiffusion3 has been significantly improved. It now supports multi-theme prompts, and the text writing effect has also been improved, and garbled characters no longer appear. StabilityAI pointed out that StableDiffusion3 is a series of models with parameter sizes ranging from 800M to 8B. This parameter range means that the model can be run directly on many portable devices, significantly reducing the use of AI

DualBEV: significantly surpassing BEVFormer and BEVDet4D, open the book! DualBEV: significantly surpassing BEVFormer and BEVDet4D, open the book! Mar 21, 2024 pm 05:21 PM

This paper explores the problem of accurately detecting objects from different viewing angles (such as perspective and bird's-eye view) in autonomous driving, especially how to effectively transform features from perspective (PV) to bird's-eye view (BEV) space. Transformation is implemented via the Visual Transformation (VT) module. Existing methods are broadly divided into two strategies: 2D to 3D and 3D to 2D conversion. 2D-to-3D methods improve dense 2D features by predicting depth probabilities, but the inherent uncertainty of depth predictions, especially in distant regions, may introduce inaccuracies. While 3D to 2D methods usually use 3D queries to sample 2D features and learn the attention weights of the correspondence between 3D and 2D features through a Transformer, which increases the computational and deployment time.

Analyze the usage and classification of JSP comments Analyze the usage and classification of JSP comments Feb 01, 2024 am 08:01 AM

Classification and Usage Analysis of JSP Comments JSP comments are divided into two types: single-line comments: ending with, only a single line of code can be commented. Multi-line comments: starting with /* and ending with */, you can comment multiple lines of code. Single-line comment example Multi-line comment example/**This is a multi-line comment*Can comment on multiple lines of code*/Usage of JSP comments JSP comments can be used to comment JSP code to make it easier to read

More than just 3D Gaussian! Latest overview of state-of-the-art 3D reconstruction techniques More than just 3D Gaussian! Latest overview of state-of-the-art 3D reconstruction techniques Jun 02, 2024 pm 06:57 PM

Written above & The author’s personal understanding is that image-based 3D reconstruction is a challenging task that involves inferring the 3D shape of an object or scene from a set of input images. Learning-based methods have attracted attention for their ability to directly estimate 3D shapes. This review paper focuses on state-of-the-art 3D reconstruction techniques, including generating novel, unseen views. An overview of recent developments in Gaussian splash methods is provided, including input types, model structures, output representations, and training strategies. Unresolved challenges and future directions are also discussed. Given the rapid progress in this field and the numerous opportunities to enhance 3D reconstruction methods, a thorough examination of the algorithm seems crucial. Therefore, this study provides a comprehensive overview of recent advances in Gaussian scattering. (Swipe your thumb up

Revolutionary GPT-4o: Reshaping the human-computer interaction experience Revolutionary GPT-4o: Reshaping the human-computer interaction experience Jun 07, 2024 pm 09:02 PM

The GPT-4o model released by OpenAI is undoubtedly a huge breakthrough, especially in its ability to process multiple input media (text, audio, images) and generate corresponding output. This ability makes human-computer interaction more natural and intuitive, greatly improving the practicality and usability of AI. Several key highlights of GPT-4o include: high scalability, multimedia input and output, further improvements in natural language understanding capabilities, etc. 1. Cross-media input/output: GPT-4o+ can accept any combination of text, audio, and images as input and directly generate output from these media. This breaks the limitation of traditional AI models that only process a single input type, making human-computer interaction more flexible and diverse. This innovation helps power smart assistants

Review! Deep model fusion (LLM/basic model/federated learning/fine-tuning, etc.) Review! Deep model fusion (LLM/basic model/federated learning/fine-tuning, etc.) Apr 18, 2024 pm 09:43 PM

In September 23, the paper "DeepModelFusion:ASurvey" was published by the National University of Defense Technology, JD.com and Beijing Institute of Technology. Deep model fusion/merging is an emerging technology that combines the parameters or predictions of multiple deep learning models into a single model. It combines the capabilities of different models to compensate for the biases and errors of individual models for better performance. Deep model fusion on large-scale deep learning models (such as LLM and basic models) faces some challenges, including high computational cost, high-dimensional parameter space, interference between different heterogeneous models, etc. This article divides existing deep model fusion methods into four categories: (1) "Pattern connection", which connects solutions in the weight space through a loss-reducing path to obtain a better initial model fusion

What are the syntax and structure characteristics of lambda expressions? What are the syntax and structure characteristics of lambda expressions? Apr 25, 2024 pm 01:12 PM

Lambda expression is an anonymous function without a name, and its syntax is: (parameter_list)->expression. They feature anonymity, diversity, currying, and closure. In practical applications, Lambda expressions can be used to define functions concisely, such as the summation function sum_lambda=lambdax,y:x+y, and apply the map() function to the list to perform the summation operation.

See all articles