Sketchovsky: Enabling Ensembles of Sketches on Programmable Switches
Sketchovsky: Enabling Ensembles of Sketches on Programmable Switches
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发表时间：
2023
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通讯作者：
Hun Namkung;Zaoxing Liu;Daehyeok Kim;Vyas Sekar;P. Steenkiste
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作者：
Hun Namkung;Zaoxing Liu;Daehyeok Kim;Vyas Sekar;P. Steenkiste
网络运营商需要在可编程交换机上运行各种测量任务以支持管理决策（例如，流量工程或异常检测）。虽然先前的工作已经表明了运行单个草图实例的可行性，但它们在很大程度上忽略了为测量任务的集合运行草图实例的集合的问题。因此，现有的努力不能有效地支持草图实例的一般集合。在这项工作中，我们提出了Sketchovsky，一种新的跨草图优化和组成框架的设计和实现。我们确定了五个新的交叉草图优化构建块，以减少关键的交换机硬件资源。我们设计了有效的算法来选择和应用这些构建块的任意合奏。为了简化开发人员的工作，Sketchovsky自动生成要输入到硬件编译器的组合代码。我们的评估表明，Sketchovsky使集成多达18个草图实例成为可行的，并可以减少高达45%的关键硬件资源。
Network operators need to run diverse measurement tasks on programmable switches to support management decisions (e.g., traffic engineering or anomaly detection). While prior work has shown the viability of running a single sketch instance, they largely ignore the problem of running an ensemble of sketch instances for a collection of measurement tasks. As such, existing efforts fall short of efficiently supporting a general ensemble of sketch instances. In this work, we present the design and implementation of Sketchovsky, a novel cross-sketch optimization and composition framework. We identify five new cross -sketch optimization building blocks to reduce critical switch hardware resources. We design efficient heuristics to select and apply these building blocks for arbitrary ensembles. To simplify developer effort, Sketchovsky automatically generates the composed code to be input to the hardware compiler. Our evaluation shows that Sketchovsky makes ensembles with up to 18 sketch instances become feasible and can reduce up to 45% of the critical hardware resources.