A Map-Reduce-Based Relation Inference Algorithm for Autonomous System

Ting Lv, Donghong Qin, Lina Ge, Song Wen
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Abstract

The business relationships between Autonomous systems (ASs) are crucial to understanding the internet structure, performance, evolution, and so on. However, the business relationships between ASs are often confidential and can only be captured by inference algorithms. This paper proposes a new algorithm to infer the ASs relationship based on the transmission capacity. A new metric for ASs node transmission capability based on path behavior is defined. Meanwhile, a big data processing technique based on Map-Reduce to deduce the ASs relationship. Besides, to analyze the accuracy of the algorithm, we compare its accuracy performance with those of existing algorithms. Through simulation experimental, the proposed scheme achieves substantial performance improvements in term of consistency and effectiveness. Also, the complexity of the proposed scheme is found to be reasonably low.
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基于映射约简的自治系统关系推理算法
自治系统(as)之间的业务关系对于理解互联网结构、性能、演变等至关重要。然而,as之间的业务关系通常是保密的,只能通过推理算法捕获。本文提出了一种基于传输容量推断自治系统关系的新算法。定义了一种新的基于路径行为的as节点传输能力度量。同时,采用基于Map-Reduce的大数据处理技术对as关系进行推断。此外,为了分析该算法的精度,我们将其与现有算法的精度性能进行了比较。通过仿真实验,该方案在一致性和有效性方面取得了较大的性能提升。此外,发现所建议的方案的复杂性相当低。
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