Dynamic Scheduling of Approximate Telemetry Queries


Chris Misa, Walt O'Connor, Ramakrishnan Durairajan, and Reza Rejaie, University of Oregon; Walter Willinger, NIKSUN, Inc.


Network telemetry systems provide critical visibility into the state of networks. While significant progress has been made by leveraging programmable switch hardware to scale these systems to high and time-varying traffic workloads, less attention has been paid towards efficiently utilizing limited hardware resources in the face of dynamics such as the composition of traffic as well as the number and types of queries running at a given point in time. Both these dynamics have implications on resource requirements and query accuracy.

In this paper, we argue that this dynamics problem motivates reframing telemetry systems as resource schedulers---a significant departure from state-of-the-art. More concretely, rather than statically partition queries across hardware and software platforms, telemetry systems ought to decide on their own and at runtime when and for how long to execute the set of active queries on the data plane. To this end, we propose an efficient approximation and scheduling algorithm that exposes accuracy and latency tradeoffs with respect to query execution to reduce hardware resource usage. We evaluate our algorithm by building DynATOS, a hardware prototype built around a reconfigurable approach to ASIC programming. We show that our approach is more robust than state-of-the-art methods to traffic dynamics and can execute dynamic workloads comprised of multiple concurrent and sequential queries of varied complexities on a single switch while meeting per-query accuracy and latency goals.

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King Abdullah University of Science and Technology (KAUST)

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@inproceedings {276986,
author = {Chris Misa and Walt O{\textquoteright}Connor and Ramakrishnan Durairajan and Reza Rejaie and Walter Willinger},
title = {Dynamic Scheduling of Approximate Telemetry Queries},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {701--717},
url = {https://www.usenix.org/conference/nsdi22/presentation/misa},
publisher = {USENIX Association},
month = apr,

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