反映射:面向软件定义网络的通用流量统计采集与查询

Jiamin Liu, Peng Zhang, Huanzhao Wang, Chengchen Hu
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引用次数: 6

摘要

流量统计是许多网络测量任务的基础,如重磅攻击识别、流量矩阵估计、异常检测等。然而,像NetFlow和sFlow这样的传统技术由于数据包或流采样而只能提供粗粒度的统计数据。即使是软件定义网络(SDN)也提供了细粒度的流量统计收集,但现有的方法大多侧重于特定的应用,因此缺乏通用性。为此,我们提出了CounterMap,一个通用的流量统计收集和查询平台。CounterMap通过主动轮询交换机和被动监控流量超时来维护流量计数器的完整映射。为了高效地存储和查询,CounterMap将计数器存储在快速现成的内存数据存储中,并提供了一种通用的类似sql的查询语言。使用CounterMap语言,应用程序可以获得对现有流和历史流的可见性,而无需查询数据平面设备本身。我们展示了网络应用程序如何受益于CounterMap,具有更高的测量精度和更低的数据平面开销。
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CounterMap: Towards generic traffic statistics collection and query in Software Defined Network
Traffic statistics are fundamental for many network measurement tasks like heavy hitter identification, traffic matrix estimator, anomaly detection, etc. However, traditional techniques like NetFlow and sFlow only provide coarse-grained statistics due to packet or flow sampling. Even Software Defined Networking (SDN) offers fine-grained traffic statistics collection, most of existing methods focus on specific applications and thus lack generality. To this end, we propose CounterMap, a generic traffic statistics collection and query platform. CounterMap maintains a full map of flow counters by actively polling switches and passively monitoring flow timeouts. For efficient storage and query, CounterMap stores the counters in fast off-the-shelf in-memory data store, and offers a generic SQL-like query language. With the CounterMap language, applications can gain visibility into both existing and historical flows, without querying the dataplane devices themselves. We show how network applications benefit from CounterMap, with higher measurement accuracy and lower dataplane overhead.
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