数据不完全的局域网拓扑发现模型与方法

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2021-02-01 DOI:10.15622/IA.2021.20.1.3
A. Aleshkin, S. Balakirev, V. Nevzorov, P. Savochkin
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引用次数: 0

摘要

许多网络管理任务需要对计算机网络的逻辑和物理拓扑结构进行描述。由于网络结构上的初始数据可能不完整和不正确,以自动方式获得这种描述是复杂的。本文研究了链路层网络设备连通性的不完全初始数据的性质。给出了链路层异构输入数据的通用处理方法。我们描述了推导缺失部分数据的模型和方法,以及可能获得单个正确网络拓扑描述的条件。本文包括从不完整的数据中构建链路层拓扑描述的算法,当这些数据有可能达到所需的级别时。此外,我们还提供了检测和解决数据歧义的方法以及改进不正确初始数据的方法。文中提供的测试和评估证明了构建方法在发现各种异构现实网络方面的适用性和有效性。此外,我们展示了所提供的方法相对于之前的类似方法的优点:我们的方法能够在多项式时间内推导出高达99%的链路层连通性数据;能够从模棱两可的数据中提供正确的解决方案。
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Models and Methods for Discovery of Local Area Network Topology with Incomplete Data
A lot of network  management tasks require a description of the logical and physical computer network topology. Obtaining such a description in an automatic way is complicated due to the possibility of incompleteness and incorrectness of the initial data on the network structure. This article provides  a study on the properties of incomplete initial data on network device connectivity on the link layer. Methods for generalized handling of the heterogeneous input data on the link layer are included. We describe models and methods for deriving  a missing part of the data, as well as the condition in which it is possible to get a single correct network topology description. The article includes algorithms for building a link layer topology description from incomplete data when this data is possible to fulfill up to the required level. Also, we provide methods for detecting and resolving an ambiguity in the data and methods for improving incorrect initial data. The tests and evaluations provided in the article demonstrate the applicability and effectiveness of the build methods for discovering  various heterogeneous real-life networks. Additionally,  we show the advantages of the provided methods over the previous analogs: our methods are able to derive up to 99% data on link layer connectivity in polynomial time; able to provide a correct solution from an ambiguous data.
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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