poster

Poster: Continual Network Learning

Authors:

Nicola Di Cicco,

Chiara Grasselli,

Gianni Antichi,

Massimo TornatoreAuthors Info & Claims

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

Pages 1096 - 1098

https://doi.org/10.1145/3603269.3610855

Published: 01 September 2023 Publication History

Abstract

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions.

References

[1]

Soheil Abbasloo, Chen-Yu Yen, and H. Jonathan Chao. 2020. Classic Meets Modern: A Pragmatic Learning-Based Congestion Control for the Internet. In Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication (Virtual Event, USA) (SIGCOMM '20). Association for Computing Machinery, New York, NY, USA, 632--647.

Digital Library

[2]

AMD. 2019. Naples DSC-100 Distributed Services Card. https://pensando.io/assets/documents/Naples_100_ProductBrief-10-2019.pdf.

[3]

G. Apruzzese, P. Laskov, and J. Schneider. 2023. SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. In 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P). IEEE Computer Society, Los Alamitos, CA, USA, 592--614.

[4]

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, and Tom Rainforth. 2023. Prediction-Oriented Bayesian Active Learning. In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 206), Francisco Ruiz, Jennifer Dy, and Jan-Willem van de Meent (Eds.). PMLR, Valencia, Spain, 7331--7348. https://proceedings.mlr.press/v206/bickfordsmith23a.html

[5]

Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.

Digital Library

[6]

Pat Bosshart, Glen Gibb, Hun-Seok Kim, George Varghese, Nick McKeown, Martin Izzard, Fernando Mujica, and Mark Horowitz. 2013. Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN. SIGCOMM Comput. Commun. Rev. 43, 4 (aug 2013), 99--110.

Digital Library

[7]

CAIDA. 2019. The CAIDA UCSD Anonymized Internet Traces - 2018-2019. https://www.caida.org/catalog/datasets/passive_dataset

[8]

Brian Chang, Aditya Akella, Loris D'Antoni, and Kausik Subramanian. 2023. Learned Load Balancing. In Proceedings of the 24th International Conference on Distributed Computing and Networking (Kharagpur, India) (ICDCN '23). Association for Computing Machinery, New York, NY, USA, 177--187.

Digital Library

[9]

Bruno Coelho and Alberto Schaeffer-Filho. 2022. BACKORDERS: Using Random Forests to Detect DDoS Attacks in Programmable Data Planes. In Proceedings of the 5th International Workshop on P4 in Europe (Rome, Italy) (EuroP4 '22). Association for Computing Machinery, New York, NY, USA, 1--7.

Digital Library

[10]

Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. 2022. A Continual Learning Survey: Defying Forgetting in Classification Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7 (2022), 3366--3385.

[11]

Heitor M. Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, and Talel Abdessalem. 2017. Adaptive random forests for evolving data stream classification. Machine Learning 106, 9 (Oct 2017), 1469--1495.

Digital Library

[12]

Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011. Bayesian Active Learning for Classification and Preference Learning. arXiv:1112.5745 [stat.ML]

[13]

Intel. 2020. Tofino: P4-programmable Ethernet switch ASIC. https://www.intel.com/content/www/us/en/products/network-io/programmable-ethernet-switch/tofino-series/tofino.html.

[14]

Santosh Janardhan. 2021. Facebook Outage on October 4th 2021. https://engineering.fb.com/2021/10/04/networking-traffic/outage/.

[15]

James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences 114, 13 (2017), 3521--3526. arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.1611835114

[16]

Ankur Mallick, Kevin Hsieh, Behnaz Arzani, and Gauri Joshi. 2022. Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems. In Proceedings of Machine Learning and Systems, D. Marculescu, Y. Chi, and C. Wu (Eds.), Vol. 4. 77--94. https://proceedings.mlsys.org/paper_files/paper/2022/file/069a002768bcb31509d4901961f23b3c-Paper.pdf

[17]

Pavol Mulinka and Pedro Casas. 2018. Adaptive Network Security through Stream Machine Learning. In Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos (Budapest, Hungary) (SIGCOMM '18). Association for Computing Machinery, New York, NY, USA, 4--5.

Digital Library

[18]

Shivam Patel, Rigden Atsatsang, Kenneth M. Tichauer, Michael H. L. S. Wang, James B. Kowalkowski, and Nik Sultana. 2022. In-Network Fractional Calculations Using P4 for Scientific Computing Workloads. In Proceedings of the 5th International Workshop on P4 in Europe (Rome, Italy) (EuroP4 '22). Association for Computing Machinery, New York, NY, USA, 33--38.

Digital Library

[19]

Yu Qiao, Chengxiang Li, Shuzheng Hao, Jun Wu, and Liang Zhang. 2022. Deep or Statistical: An Empirical Study of Traffic Predictions on Multiple Time Scales. In Proceedings of the SIGCOMM '22 Poster and Demo Sessions (Amsterdam, Netherlands) (SIGCOMM '22). Association for Computing Machinery, New York, NY, USA, 10--12.

Digital Library

[20]

S. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert. 2017. iCaRL: Incremental Classifier and Representation Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 5533--5542.

[21]

Davide Sanvito, Giuseppe Siracusano, and Roberto Bifulco. 2018. Can the Network Be the AI Accelerator?. In Proceedings of the 2018 Morning Workshop on In-Network Computing (Budapest, Hungary) (NetCompute '18). Association for Computing Machinery, New York, NY, USA, 20--25.

Digital Library

[22]

Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, and Jordan T. Ash. 2023. Streaming Active Learning with Deep Neural Networks. arXiv:2303.02535 [cs.LG]

[23]

Burr Settles. 2012. Active Learning. Springer Cham, Cham, Switzerland.

[24]

Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani. 2019. Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST). IEEE, Chennai, India, 1--8.

[25]

Ravid Shwartz-Ziv and Amitai Armon. 2021. Tabular Data: Deep Learning is Not All You Need. In 8th ICML Workshop on Automated Machine Learning (AutoML). https://openreview.net/forum?id=vdgtepS1pV

[26]

Giuseppe Siracusano and Roberto Bifulco. 2018. In-network Neural Networks. arXiv:1801.05731 [cs.DC]

[27]

Radostin Stoyanov and Noa Zilberman. 2020. MTPSA: Multi-Tenant Programmable Switches. In Proceedings of the 3rd P4 Workshop in Europe (Barcelona, Spain) (EuroP4'20). Association for Computing Machinery, New York, NY, USA, 43--48.

Digital Library

[28]

Tushar Swamy, Alexander Rucker, Muhammad Shahbaz, Ishan Gaur, and Kunle Olukotun. 2022. Taurus: A Data Plane Architecture for per-Packet ML. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (Lausanne, Switzerland) (ASP-LOS '22). Association for Computing Machinery, New York, NY, USA, 1099--1114.

Digital Library

[29]

Tushar Swamy, Annus Zulfiqar, Luigi Nardi, Muhammad Shahbaz, and Kunle Olukotun. 2023. Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 (Vancouver, BC, Canada) (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 329--342.

Digital Library

[30]

Tao Wang, Xiangrui Yang, Gianni Antichi, Anirudh Sivaraman, and Aurojit Panda. 2022. Isolation Mechanisms for High-Speed Packet-Processing Pipelines. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA, 1289--1305. https://www.usenix.org/conference/nsdi22/presentation/wang-tao

[31]

Matthias Wichtlhuber, Eric Strehle, Daniel Kopp, Lars Prepens, Stefan Stegmueller, Alina Rubina, Christoph Dietzel, and Oliver Hohlfeld. 2022. IXP Scrubber: Learning from Blackholing Traffic for ML-Driven DDoS Detection at Scale. In Proceedings of the ACM SIGCOMM 2022 Conference (Amsterdam, Netherlands) (SIGCOMM '22). Association for Computing Machinery, New York, NY, USA, 707--722.

Digital Library

[32]

Jiarong Xing, Kuo-Feng Hsu, Matty Kadosh, Alan Lo, Yonatan Piasetzky, Arvind Krishnamurthy, and Ang Chen. 2022. Runtime Programmable Switches. In 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22). USENIX Association, Renton, WA, 651--665. https://www.usenix.org/conference/nsdi22/presentation/xing

[33]

Zhaoqi Xiong and Noa Zilberman. 2019. Do Switches Dream of Machine Learning? Toward In-Network Classification. In Proceedings of the 18th ACM Workshop on Hot Topics in Networks (Princeton, NJ, USA) (HotNets '19). Association for Computing Machinery, New York, NY, USA, 25--33.

Digital Library

[34]

Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman. 2022. IIsy: Practical In-Network Classification. arXiv:2205.08243 [cs.NI]

[35]

Peng Zheng, Theophilus Benson, and Chengchen Hu. 2018. P4Visor: Lightweight Virtualization and Composition Primitives for Building and Testing Modular Programs. In Proceedings of the 14th International Conference on Emerging Networking EXperiments and Technologies (Heraklion, Greece) (CoNEXT '18). Association for Computing Machinery, New York, NY, USA, 98--111.

Digital Library

Cited By

Wang RZhang JZhang QZhang BGu ZAttarpour AJi YTornatore MSekar VYu MSeneviratne AVeitch D(2024)Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI TasksProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673744(60-62)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673744
Azorin RMonterubbiano ACastellano GGallo MPontarelli SRossi D(2024)Taming the Elephants: Affordable Flow Length Prediction in the Data PlaneProceedings of the ACM on Networking10.1145/36494732:CoNEXT1(1-24)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3649473

Index Terms

Poster: Continual Network Learning
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Active learning settings
      2. Online learning settings
2. Networks
  1. Network services
    1. In-network processing
    2. Programmable networks

Recommendations

Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Highlights
- State of the art on continual / lifelong learning and its implications for robotics.
Abstract
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective change through time, or where all the training data and objective criteria are never available at once. The evolution ...
Avalanche RL: A Continual Reinforcement Learning Library
Image Analysis and Processing – ICIAP 2022
Abstract
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for ...
Towards continual reinforcement learning through evolutionary meta-learning
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

In continual learning, an agent is exposed to a changing environment, requiring it to adapt during execution time. While traditional reinforcement learning (RL) methods have shown impressive results in various domains, there has been less progress in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

September 2023

1217 pages

ISBN:9798400702365

DOI:10.1145/3603269

Chairs:
Henning Schulzrinne,
Vishal Misra,
Program Chairs:
Eddie Kohler,
David Maltz

Copyright © 2023 Owner/Author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2023

Check for updates

Author Tags

Qualifiers

Poster

Conference

ACM SIGCOMM '23

Sponsor:

SIGCOMM

ACM SIGCOMM '23: ACM SIGCOMM 2023 Conference

September 10, 2023

NY, New York, USA

Acceptance Rates

Overall Acceptance Rate 462 of 3,389 submissions, 14%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
340
Total Downloads

Downloads (Last 12 months)167
Downloads (Last 6 weeks)12

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang RZhang JZhang QZhang BGu ZAttarpour AJi YTornatore MSekar VYu MSeneviratne AVeitch D(2024)Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI TasksProceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos10.1145/3672202.3673744(60-62)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672202.3673744
Azorin RMonterubbiano ACastellano GGallo MPontarelli SRossi D(2024)Taming the Elephants: Affordable Flow Length Prediction in the Data PlaneProceedings of the ACM on Networking10.1145/36494732:CoNEXT1(1-24)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3649473

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents