基于状态空间的最优网络流采样交通监控方法
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
AI总结：
本文提出基于状态空间的优化交通监控方法，通过利用网络流的空间和时间关系，在资源受限条件下实现最优的流量采样策略，提升网络流量估计的准确性。
摘要：
IP网络的鲁棒性和完整性要求高效的流量监控和分析工具，这些工具能够随着流量体积和网络规模的增加而扩展。我们解决了在资源受限条件下对计算机网络进行大规模流量监控的最优问题。我们提出了一种随机优化框架，通过利用流量的空间（跨网络链路）和时间关系来进行流量测量。具体而言，给定网络拓扑，网络流的状态空间表征以及每个监控站的采样约束，我们寻求一种最优的分组采样策略，以获得网络中所有流量的最佳流量体积估计。最优的采样设计是凸优化问题的结果；然后，使用卡尔曼滤波来为每个网络流生成一系列流量估计。我们通过实际的Internet2数据评估了我们的算法。
英文摘要：
The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the problem of optimal large-scale flow monitoring of computer networks under resource constraints. We propose a stochastic optimization framework where traffic measurements are done by exploiting the spatial (across network links) and temporal relationship of traffic flows. Specifically, given the network topology, the state-space characterization of network flows and sampling constraints at each monitoring station, we seek an optimal packet sampling strategy that yields the best traffic volume estimation for all flows of the network. The optimal sampling design is the result of a concave minimization problem; then, Kalman filtering is employed to yield a sequence of traffic estimates for each network flow. We evaluate our algorithm using real-world Internet2 data.
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