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Telemetry systems are widely used to collect data from distributed endpoints, analyze data in conjunction to gain valuable insights, and store data for historical analytics. These systems consist of four stages (Figure 1): collection, transmission, analysis, and storage. Collectors at the endpoint collect various types of data, which is then transmitted to a central server for analysis. This data is used for multiple downstream tasks, such as dashboard monitoring and anomaly detection. Finally, this data is stored in long-term storage to aid retrospective analytics and debugging. 

While sketch-based network telemetry is attractive, realizing its potential benefits has been elusive in practice. Existing sketch so- lutions offer low-level interfaces and impose high effort on op- erators to satisfy telemetry intents with required accuracies. Ex- tending these approaches to reduce effort results in inefficient deployments with poor accuracy-resource tradeoffs. We present SketchPlan, an abstraction layer for sketch-based telemetry to re- duce effort and achieve high efficiency. SketchPlan takes an en- semble view across telemetry intents and sketches, instead of ex- isting approaches that consider each intent-sketch pair in isola- tion. We show that SketchPlan improves accuracy-resource trade- offs by up-to 12x and up-to 60x vs. baselines, in single-node and network-wide settings. SketchPlan is open-sourced at: https: //github.com/milindsrivastava1997/SketchPlan 

While sketch-based network telemetry is attractive, realizing its potential benefits has been elusive in practice. Existing sketch solutions offer low-level interfaces and impose high effort on operators to satisfy telemetry intents with required accuracies. Extending these approaches to reduce effort results in inefficient deployments with poor accuracy-resource tradeoffs. We present SketchPlan, an abstraction layer for sketch-based telemetry to reduce effort and achieve high efficiency. SketchPlan’s takes an ensemble view across telemetry intents and sketches, instead of existing approaches that consider each intent-sketch pair in isolation. We show that SketchPlan improves accuracy-resource tradeoffs by up-to 12x and up-to 60x vs. baselines, in single-node and network-wide settings. 

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.