钓鱼在泊松流：专注于鲸鱼，忽略了小鱼
Fishing in Poisson streams: focusing on the whales, ignoring the minnows
摘要：
本文描述了一种低复杂度的方法来重建平均数据包到达率和瞬时数据包计数在路由器在通信网络中，在每个流中的数据包的到达遵循泊松过程。假设该泊松过程的速率向量是稀疏的或近似稀疏的，目标是使用少量计数器来维护过程样本路径的压缩摘要，使得在任何时候都可以重建每个流中的分组总数和底层速率向量。我们表明，这些任务可以有效地完成，准确地使用压缩感知与扩展图。特别地，压缩计数是通过不平衡扩展器的邻接矩阵的基础计数过程的线性变换。这样的矩阵是二进制和稀疏的，这允许在新分组到达时有效地递增。我们描述，分析和比较两种方法，可用于估计当前的总数据包计数向量和到达率的基本向量。
英文摘要：
This paper describes a low-complexity approach for reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arrivals of packets in each flow follow a Poisson process. Assuming that the rate vector of this Poisson process is sparse or approximately sparse, the goal is to maintain a compressed summary of the process sample paths using a small number of counters, such that at any time it is possible to reconstruct both the total number of packets in each flow and the underlying rate vector. We show that these tasks can be accomplished efficiently and accurately using compressed sensing with expander graphs. In particular, the compressive counts are a linear transformation of the underlying counting process by the adjacency matrix of an unbalanced expander. Such a matrix is binary and sparse, which allows for efficient incrementing when new packets arrive. We describe, analyze, and compare two methods that can be used to estimate both the current vector of total packet counts and the underlying vector of arrival rates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
