PARALLEL DATA LAB 

PDL Abstract

Raising the Level of Abstraction for Sketch-Based Network Telemetry with SketchPlan

IMC ’24, November 4–6, 2024, Madrid, Spain.

Milind Srivastava, Shao-Tse Hung*, Hun Namkung, Kate Ching-Ju Lin*, Zaoxing Liu^, Vyas Sekar

Carnegie Mellon University
* National Yang-Ming Chiao Tung University Taipei, Taiwan
^ University of Maryland

http://www.pdl.cmu.edu/

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 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. SketchPlan is open-sourced at: https://github.com/milindsrivastava1997/SketchPlan.

FULL PAPER: pdf