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    • 43. 发明授权
    • System and method for spatially consistent sampling of flow records at constrained, content-dependent rates
    • 以受限制的,依赖内容的速率对流记录进行空间一致采样的系统和方法
    • US08064359B2
    • 2011-11-22
    • US12343007
    • 2008-12-23
    • Nicholas DuffieldLee M. BreslauCheng EeAlexandre GerberCarsten LundSubhabrata Sen
    • Nicholas DuffieldLee M. BreslauCheng EeAlexandre GerberCarsten LundSubhabrata Sen
    • H04L12/26
    • H04L43/026H04L43/024Y02D50/30
    • Disclosed herein are systems, computer-implemented methods, and computer-readable media for sampling network traffic. The method includes receiving a desired quantity of flow record to sample, receiving a plurality of network flow record each summarizing a network flow of packets, calculating a hash for each flow record of based on one or more invariant part of a respective flow, generating a quasi-random number from the calculated hash for each respective flow record, generating a priority from the calculated hash for each respective flow record, and sampling exactly the desired quantity of flow records, selecting flow records having a highest priority first. In one aspect, the method further partitions the plurality of flow records into groups based on flow origin and destination, generates an individual priority for each partitioned group, and separately samples exactly the desired quantity of flow records from each partitioned group, selecting flows having a highest individual priority first.
    • 本文公开了系统,计算机实现的方法和用于对网络业务进行采样的计算机可读介质。 该方法包括接收所需数量的流记录到采样中,接收多个网络流记录,每个汇总分组的网络流,基于相应流的一个或多个不变部分计算每个流记录的散列, 从每个相应流记录的计算散列中产生准随机数,从每个相应流记录的计算散列生成优先级,并精确地采样所需数量的流记录,首先选择具有最高优先级的流记录。 在一个方面,该方法还基于流源和目的地进一步将多个流记录划分为组,为每个分区组生成一个单独的优先级,并且从每个分区组中分别精确地采集所需数量的流记录,选择具有 最高个人优先。
    • 44. 发明授权
    • Methods and apparatus to bound network traffic estimation error for multistage measurement sampling and aggregation
    • 用于多级测量采样和聚合的网络流量估计误差的方法和装置
    • US07990982B2
    • 2011-08-02
    • US12335074
    • 2008-12-15
    • Nicholas DuffieldCarsten LundMikkel ThorupEdith Cohen
    • Nicholas DuffieldCarsten LundMikkel ThorupEdith Cohen
    • H04L12/28H04L12/56
    • H04L43/16H04L41/0681H04L41/12H04L43/02
    • Methods and apparatus to bound network traffic estimation error for multistage measurement sampling and aggregation are disclosed. An example method disclosed herein comprises determining a hierarchical sampling topology representative of multiple data sampling and aggregation stages, the hierarchical sampling topology comprising a plurality of nodes connected by a plurality of edges, each node corresponding to at least one of a data source and a data aggregation operation, and each edge corresponding to a data sampling operation characterized by a generalized sampling threshold, selecting a first generalized sampling threshold from a set of generalized sampling thresholds associated with a respective set of edges originating at a respective set of descendent nodes of a target node undergoing network traffic estimation, and transforming a measured sample of network traffic into a confidence interval for a network traffic estimate associated with the target node using the first generalized sampling threshold and an error parameter.
    • 公开了多级测量采样和聚合的绑定网络流量估计误差的方法和装置。 本文公开的示例性方法包括确定表示多个数据采样和聚合阶段的分层采样拓扑,所述分层采样拓扑包括由多个边缘连接的多个节点,每个节点对应于数据源和数据中的至少一个 并且每个边缘对应于由广义采样阈值表征的数据采样操作,从与源于目标的相应的一组后代节点的相应的一组边缘相关联的一组广义采样阈值中选择第一广义采样阈值 节点进行网络流量估计,并且使用第一广义采样阈值和误差参数将网络流量的测量样本变换为与目标节点相关联的网络流量估计的置信区间。
    • 47. 发明申请
    • SYSTEM AND METHOD FOR SAMPLING NETWORK TRAFFIC
    • 用于采集网络交通的系统和方法
    • US20100161791A1
    • 2010-06-24
    • US12342957
    • 2008-12-23
    • Nicholas DuffieldLee M. BreslauCheng EeAlexandre GerberCarsten LundSubhabrata Sen
    • Nicholas DuffieldLee M. BreslauCheng EeAlexandre GerberCarsten LundSubhabrata Sen
    • G06F15/173
    • H04L43/04H04L43/022H04L43/026H04L43/062
    • Disclosed herein are systems, computer-implemented methods, and computer-readable media for sampling network traffic. The method includes receiving a plurality of flow records, calculating a hash for each flow record based on one or more invariant part of a respective flow, generating a quasi-random number from the calculated hash for each respective flow record, and sampling flow records having a quasi-random number below a probability P. Invariant parts of flow records include destination IP address, source IP address, TCP/UDP port numbers, TCP flags, and network protocol. A plurality of routers can uniformly calculate hashes for flow records. Each router in a plurality of routers can generate a same quasi-random number for each respective flow record and uses different values for probability P. The probability P can depend on a flow size. The method can divide the quasi-random number by a maximum possible hash value.
    • 本文公开了系统,计算机实现的方法和用于对网络业务进行采样的计算机可读介质。 该方法包括:接收多个流记录,基于相应流的一个或多个不变部分计算每个流记录的散列,从针对每个相应流记录的计算出的散列生成准随机数,以及对具有 低于概率P的准随机数。流记录的不变部分包括目的地IP地址,源IP地址,TCP / UDP端口号,TCP标志和网络协议。 多个路由器可以统一计算流记录的哈希值。 多个路由器中的每个路由器可以为每个相应的流记录生成相同的准随机数,并对概率P使用不同的值。概率P可以取决于流量大小。 该方法可以将准随机数除以最大可能的哈希值。