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Virtual indexing based methods for estimating node connection degrees

机译：基于虚拟索引的估计节点连接度的方法

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摘要

It is difficult to accurately measure node connection degrees for a high speed network, since there is a massive amount of traffic to be processed. In this paper, we present a new virtual indexing method for estimating node connection degrees for high speed links. It is based on the virtual connection degree sketch {VCDS) where a compact sketch of network traffic is built by generating multiple virtual bitmaps for each network node. Each virtual bitmap consists of a fixed number of bits selected randomly from a shared bit array by a new method for recording the traffic flows of the corresponding node. The shared bit array is efficiently utilized by all nodes since every bit is shared by the virtual bitmaps of multiple nodes. To reduce the "noise" contaminated in a node's virtual bitmaps due to sharing, we propose a new method to generate the "filtered" bitmap used to estimate node connection degree. Furthermore, we apply VCDS to detect super nodes often associated with traffic anomalies. Since VCDS need a large amount of extra memory to store node addresses, we also propose a new data structure, the reversible virtual connection degree sketch, which identifies super node addresses analytically without the need of extra memory space but at a small increase in estimation error. Furthermore we combine the VCDS and RVCDS based methods with a uniform flow sampling technique to reduce memory complexities. Experiments are performed based on the actual network traffic and testing results show that the new methods are more memory efficient and more accurate than existing methods.
机译：由于要处理大量流量，因此很难为高速网络准确测量节点连接度。在本文中，我们提出了一种新的虚拟索引方法，用于估计高速链路的节点连接度。它基于虚拟连接度草图（VCDS），其中通过为每个网络节点生成多个虚拟位图来构建网络流量的紧凑草图。每个虚拟位图由固定数量的位数组成，该位数是通过一种用于记录相应节点的业务流的新方法从共享位数组中随机选择的。由于每个位都由多个节点的虚拟位图共享，因此共享位数组可被所有节点有效利用。为了减少由于共享而在节点的虚拟位图中污染的“噪声”，我们提出了一种新的方法来生成用于估计节点连接度的“已过滤”位图。此外，我们应用VCDS来检测经常与交通异常相关的超级节点。由于VCDS需要大量额外的内存来存储节点地址，因此我们还提出了一种新的数据结构，即可逆虚拟连接度草图，该结构可解析地标识超级节点地址，而无需额外的内存空间，但是估计误差会有所增加。此外，我们将基于VCDS和RVCDS的方法与统一的流量采样技术相结合，以减少内存的复杂性。根据实际网络流量进行实验，测试结果表明，新方法比现有方法具有更高的内存效率和准确性。

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  • 来源
    《Computer networks》 |2012年第12期|p.2773-2787|共15页
  • 作者

    Pinghui Wang; Xiaohong Guan; Don Towsley; Jing Tao;

  • 作者单位

    MOE Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China;

    MOE Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China,Department of Automation and NL1ST Lab, Tsinghua University, Beijing 100083, China;

    Department of Computer Science, University of Massachusetts, Amherst, MA 01003, United States;

    MOE Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data streaming; traffic monitoring; super host detection;

    机译：数据流;交通监控;超级主机检测;

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