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Fast Detection of Heavy Hitters in Software Defined Networking Using an Adaptive and Learning Method

机译:使用自适应和学习方法快速检测软件定义网络中的重击者

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Heavy Hitters refer to the set of flows that represent a significantly large proportion of the link capacity or of the active traffic. Identifying Heavy Hitters is of particular importance in both network management and security applications. Traditional methods are focusing on sampling in the middle box and analyzing those packets using streaming algorithms. The paradigm of Software Defined Network (SDN) simplifies the work of flow counting. However, continuously monitoring the network will introduce overhead, which needs to be considered as a tradeoff between accurate measurement in real-time. In this paper, We propose a novel method that stamps each suspicious flow with a weight based on an online learning algorithm. The granularity of measurement is dynamically changed according to the importance of each flow. We take advantage of history flows to make the procedure of finding a heavy hitter faster so that applications can make decisions instantly. Using real-world data, we show that our online learning method can detect heavy hitters faster with less overhead and the same accuracy.
机译:重击者是指代表链路容量或活动流量的很大一部分流量。在网络管理和安全应用程序中,识别重击者尤其重要。传统方法集中在中间盒中进行采样,并使用流算法分析那些数据包。软件定义网络(SDN)的范例简化了流量计数的工作。但是,连续监视网络会引入开销,这需要在实时精确测量之间进行权衡。在本文中,我们提出了一种基于在线学习算法对每个可疑流进行加权标记的新颖方法。测量的粒度根据每个流的重要性而动态变化。我们利用历史记录流来更快地找到重击手,以便应用程序可以立即做出决策。使用实际数据,我们证明了我们的在线学习方法可以更快地检测出重击手,而开销却更少,并且准确性相同。

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