With the advent of large-scale and high-speed IPv6 network technology, an effective multi-point traffic sampling is becoming a necessity. A distributed multi-point traffic sampling method that provides an accurate and...With the advent of large-scale and high-speed IPv6 network technology, an effective multi-point traffic sampling is becoming a necessity. A distributed multi-point traffic sampling method that provides an accurate and efficient solution to measure IPv6 traffic is proposed. The proposed method is to sample IPv6 traffic based on the analysis of bit randomness of each byte in the packet header. It offers a way to consistently select the same subset of packets at each measurement point, which satisfies the requirement of the distributed multi-point measurement. Finally, using real IPv6 traffic traces, the conclusion that the sampled traffic data have a good uniformity that satisfies the requirement of sampling randomness and can correctly reflect the packet size distribution of full packet trace is proved.展开更多
在高速网络环境中,超级传播者被界定为那些具有大量连接的主机或设备。准确的超级传播者检测在网络监控、安全分析及流量管理等多种应用中起着至关重要的作用。基于Sketch的可逆算法因具有卓越的内存效率与从内部结构中恢复超级传播者I...在高速网络环境中,超级传播者被界定为那些具有大量连接的主机或设备。准确的超级传播者检测在网络监控、安全分析及流量管理等多种应用中起着至关重要的作用。基于Sketch的可逆算法因具有卓越的内存效率与从内部结构中恢复超级传播者ID的能力,受到了广泛关注。根据应用需要,通常将同一台主机或设备发出或接收的所有数据包抽象为一条流。在高速网络中,流的分布通常高度偏斜,仅有少部分流为大流,绝大多数是小流。然而,现有研究的内存结构设计无法高效地适应高度偏斜的流分布,使得内存资源利用率较为低下。为此,设计了一种基于自适应采样的超级传播者检测算法AS-SSD(Adaptive Sampling Based Super Spreader Detection),该算法通过一种基于寄存器共享的自适应采样策略,弥补了上述不足。AS-SSD首先将到达的流元素映射到一个寄存器数组中,使得小流仅使用少量寄存器,而越大的流使用越多的寄存器,从而适应偏斜的流分布;接着,将所有流的元素映射到一个寄存器数组中,使得小流仅使用少量寄存器,大流使用更多的寄存器,从而适应偏斜的流分布;然后,利用自适应采样策略动态调整不同规模流的元素采样概率,在保证精度的前提下,减少大流对寄存器的占用,进一步提升内存资源的利用效率。实验评估显示,AS-SSD在维持高吞吐量的同时,在超级传播者检测任务中展现出了更高的检测准确度,相比目前最先进的算法,最高可以将F1值提高0.609以上。展开更多
基金This project was supported by the National Natural Science Foundation of China (60572147,60132030)
文摘With the advent of large-scale and high-speed IPv6 network technology, an effective multi-point traffic sampling is becoming a necessity. A distributed multi-point traffic sampling method that provides an accurate and efficient solution to measure IPv6 traffic is proposed. The proposed method is to sample IPv6 traffic based on the analysis of bit randomness of each byte in the packet header. It offers a way to consistently select the same subset of packets at each measurement point, which satisfies the requirement of the distributed multi-point measurement. Finally, using real IPv6 traffic traces, the conclusion that the sampled traffic data have a good uniformity that satisfies the requirement of sampling randomness and can correctly reflect the packet size distribution of full packet trace is proved.
文摘在高速网络环境中,超级传播者被界定为那些具有大量连接的主机或设备。准确的超级传播者检测在网络监控、安全分析及流量管理等多种应用中起着至关重要的作用。基于Sketch的可逆算法因具有卓越的内存效率与从内部结构中恢复超级传播者ID的能力,受到了广泛关注。根据应用需要,通常将同一台主机或设备发出或接收的所有数据包抽象为一条流。在高速网络中,流的分布通常高度偏斜,仅有少部分流为大流,绝大多数是小流。然而,现有研究的内存结构设计无法高效地适应高度偏斜的流分布,使得内存资源利用率较为低下。为此,设计了一种基于自适应采样的超级传播者检测算法AS-SSD(Adaptive Sampling Based Super Spreader Detection),该算法通过一种基于寄存器共享的自适应采样策略,弥补了上述不足。AS-SSD首先将到达的流元素映射到一个寄存器数组中,使得小流仅使用少量寄存器,而越大的流使用越多的寄存器,从而适应偏斜的流分布;接着,将所有流的元素映射到一个寄存器数组中,使得小流仅使用少量寄存器,大流使用更多的寄存器,从而适应偏斜的流分布;然后,利用自适应采样策略动态调整不同规模流的元素采样概率,在保证精度的前提下,减少大流对寄存器的占用,进一步提升内存资源的利用效率。实验评估显示,AS-SSD在维持高吞吐量的同时,在超级传播者检测任务中展现出了更高的检测准确度,相比目前最先进的算法,最高可以将F1值提高0.609以上。
