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北美退伍军人节(Veterans Day),12 日无更新
arXiv 2005.11542cs.NIcs.DS
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路由不经意测量分析

Routing Oblivious Measurement Analytics

Ran Ben Basat, Xiaoqi Chen, Gil Einziger, Shir Landau Feibish, Danny Raz, Minlan Yu

摘要:

各种网络监控任务通常需要网络范围的流量分析。这些测量通常通过在网络交换机处收集样本来执行,然后将样本发送到控制器进行聚合。然而,在不对穿过多个测量交换机的流或分组进行“过量计数”的情况下执行这样的分析是具有挑战性的。因此,现有的解决方案通常通过对路由或测量交换机放置进行假设来简化问题。我们引入了AROMA,这是一种测量基础设施,可以生成统一的数据包和流样本,而无论拓扑、工作负载和路由如何。因此,AROMA可以部署在许多环境中,也可以使用可编程PISA交换机在数据平面中工作。AROMA基础设施包括控制器算法,可近似各种基本测量任务,同时提供正式的精度保证。使用广泛的模拟现实世界的网络痕迹,我们表明,我们的算法是有竞争力的准确性相比,现有的最佳解决方案,尽管事实上,他们没有假设的底层网络或测量开关的位置。

英文摘要:

Network-wide traffic analytics are often needed for various network monitoring tasks. These measurements are often performed by collecting samples at network switches, which are then sent to the controller for aggregation. However, performing such analytics without ``overcounting'' flows or packets that traverse multiple measurement switches is challenging. Therefore, existing solutions often simplify the problem by making assumptions on the routing or measurement switch placement. We introduce AROMA, a measurement infrastructure that generates a uniform sample of packets and flows regardless of the topology, workload and routing. Therefore, AROMA can be deployed in many settings, and can also work in the data plane using programmable PISA switches. The AROMA infrastructure includes controller algorithms that approximate a variety of essential measurement tasks while providing formal accuracy guarantees. Using extensive simulations on real-world network traces, we show that our algorithms are competitively accurate compared to the best existing solutions despite the fact that they make no assumptions on the underlying network or the placement of measurement switches.

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。

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