research-article

PacketScope: Monitoring the Packet Lifecycle Inside a Switch

Authors:

Jennifer RexfordAuthors Info & Claims

SOSR '20: Proceedings of the Symposium on SDN Research

Pages 76 - 82

https://doi.org/10.1145/3373360.3380838

Published: 04 March 2020 Publication History

Abstract

As modern switches become increasingly more powerful, flexible, and programmable, network operators have an ever greater need to monitor their behavior. Many existing systems provide the ability to observe and analyze traffic that arrives at switches, but do not provide visibility into the experience of packets within the switch. To fill this gap, we present PacketScope, a network telemetry system that lets us peek inside network switches to ask a suite of useful queries about how switches modify, drop, delay, and forward packets. PacketScope gives network operators an intuitive and powerful Spark-like dataflow language to express these queries. To minimize the overhead of PacketScope on switch metadata, our compiler uses a "tag little, compute early" strategy that tags packets with metadata as they move through the switch pipeline, and computes query results as early as possible to free up pipeline resources for later processing. PacketScope also combines information from the ingress and egress pipelines to answer aggregate queries about packets dropped due to a full queue.

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Cited By

Chen XZhang WSun XLiu HZhang JHuang QZhang DZhou HLiu XWu C(2024)Resource-Efficient and Timely Packet Header Vector (PHV) Encoding on Programmable SwitchesIEEE/ACM Transactions on Networking10.1109/TNET.2024.341353032:5(4191-4206)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3413530
Huang CZhang TWang LXiao YYang CTian CWang XZhang DYan BAbdelmoniem ADou WChen G(2024)MpScope: Enabling multi-pipeline monitoring inside a switchComputer Networks10.1016/j.comnet.2024.110764254(110764)Online publication date: Dec-2024
https://doi.org/10.1016/j.comnet.2024.110764
Duan YLi CBai GChen GZhou FChen JGao ZZhang C(2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
https://doi.org/10.1186/s13677-023-00492-w
Show More Cited By

Index Terms

PacketScope: Monitoring the Packet Lifecycle Inside a Switch
1. Networks
  1. Network services
    1. In-network processing

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cover image ACM Conferences

SOSR '20: Proceedings of the Symposium on SDN Research

March 2020

151 pages

ISBN:9781450371018

DOI:10.1145/3373360

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2020

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SOSR '20: Symposium on SDN Research

March 3, 2020

CA, San Jose, USA

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Overall Acceptance Rate 7 of 43 submissions, 16%

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Cited By

Chen XZhang WSun XLiu HZhang JHuang QZhang DZhou HLiu XWu C(2024)Resource-Efficient and Timely Packet Header Vector (PHV) Encoding on Programmable SwitchesIEEE/ACM Transactions on Networking10.1109/TNET.2024.341353032:5(4191-4206)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3413530
Huang CZhang TWang LXiao YYang CTian CWang XZhang DYan BAbdelmoniem ADou WChen G(2024)MpScope: Enabling multi-pipeline monitoring inside a switchComputer Networks10.1016/j.comnet.2024.110764254(110764)Online publication date: Dec-2024
https://doi.org/10.1016/j.comnet.2024.110764
Duan YLi CBai GChen GZhou FChen JGao ZZhang C(2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
https://doi.org/10.1186/s13677-023-00492-w
Wang BChen HChen PHe ZYu G(2023)MARS: Fault Localization in Programmable Networking Systems with Low-cost In-Band Network TelemetryProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605622(347-357)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605622
Yang KWu YMiao RYang TLiu ZXu ZQiu RZhao YLv HJi ZXie GSchulzrinne HKohler EMaltz DMisra V(2023)ChameleMon: Shifting Measurement Attention as Network State ChangesProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604850(881-903)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3604850
Langlet JBen Basat ROliaro GMitzenmacher MYu MAntichi GSchulzrinne HKohler EMaltz DMisra V(2023)Direct Telemetry AccessProceedings of the ACM SIGCOMM 2023 Conference10.1145/3603269.3604827(832-849)Online publication date: 10-Sep-2023
https://dl.acm.org/doi/10.1145/3603269.3604827
Chen XLiu HXiao QZhang JHuang QZhang DLiu XWu C(2023)Melody: Toward Resource-Efficient Packet Header Vector Encoding on Programmable SwitchesIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229056(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10229056
Kianpisheh STaleb T(2023)A Survey on In-Network Computing: Programmable Data Plane and Technology Specific ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.321323725:1(701-761)Online publication date: Sep-2024
https://doi.org/10.1109/COMST.2022.3213237
Haddadou KPujolle G(2023)OpenFlow, P4, Opflex and I2RSCloud and Edge Networking10.1002/9781394257461.ch7(113-126)Online publication date: 6-Dec-2023
https://doi.org/10.1002/9781394257461.ch7
Feibish SLiu ZIvkin NChen XBraverman VRexford J(2022)Flow-level loss detection with Δ-sketchesProceedings of the Symposium on SDN Research10.1145/3563647.3563653(25-32)Online publication date: 19-Oct-2022
https://dl.acm.org/doi/10.1145/3563647.3563653
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