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.
输入通信量的行为特性对于 IP 骨干网节点的性能和设计有重要的影响。本文从 IP 骨干网络节点输入通信量角度出发,提出了一个基于 Pareto 和指数分布的混合通信量模型,其中数据包到达间隔为 Pareto 分布.包大小为指数分布。该模型克服了传统网络通信量模型中没有显式考虑数据包大小的缺点,从数据包级对网络通信量进行了精确的描述,从而能够更好地进行路由器结构设计和性能分析。仿真实验验证了我们模型的有效性。
针对传感器网络在空间、海洋等三维场景下的应用,基于划分空间为球壳并取球壳交集定位的思想,提出了对传感器结点进行三维定位的非距离定位算法APIS(approximate point in sphere),研究了该算法的原理和实现方法,并对该算法在VC环境中进行了仿真实验,并对其结果进行了分析.实验表明,在100×100×100单位的三维空间中,随机放置55个锚结点,就能对98%的结点进行定位,其平均相对误差仅为60%.因此,APIS算法能有效地实现三维环境中的传感器结点定位.
