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Application-awareness in SDN

Published: 27 August 2013 Publication History

Abstract

We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.

References

[1]
HP and Microsoft Demo OpenFlow-Lync Applications-optimized Network. http://tinyurl.com/avjjg8o.
[2]
H. Kim et al. Internet Traffic Classification Demystified: Myths, Caveats, and the Best Practices. In ACM CoNEXT, 2008.
[3]
N. Williams et al. Real Time Traffic Classification and Prioritisation on a Home Router using DIFFUSE. In CAIA Technical Report 120412A, 2011.

Cited By

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  • (2024)Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine LearningNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575394(1-9)Online publication date: 6-May-2024
  • (2024)Machine Learning Techniques for Secure Edge SDNSecure Edge and Fog Computing Enabled AI for IoT and Smart Cities10.1007/978-3-031-51097-7_14(175-193)Online publication date: 20-Mar-2024
  • (2023)Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230189(1-10)Online publication date: Jul-2023
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  1. Application-awareness in SDN

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    Published In

    cover image ACM SIGCOMM Computer Communication Review
    ACM SIGCOMM Computer Communication Review  Volume 43, Issue 4
    October 2013
    595 pages
    ISSN:0146-4833
    DOI:10.1145/2534169
    Issue’s Table of Contents
    • cover image ACM Conferences
      SIGCOMM '13: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
      August 2013
      580 pages
      ISBN:9781450320566
      DOI:10.1145/2486001
    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.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 August 2013
    Published in SIGCOMM-CCR Volume 43, Issue 4

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    Author Tags

    1. application awareness
    2. software-defined networking (sdn)

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    Cited By

    View all
    • (2024)Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine LearningNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575394(1-9)Online publication date: 6-May-2024
    • (2024)Machine Learning Techniques for Secure Edge SDNSecure Edge and Fog Computing Enabled AI for IoT and Smart Cities10.1007/978-3-031-51097-7_14(175-193)Online publication date: 20-Mar-2024
    • (2023)Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230189(1-10)Online publication date: Jul-2023
    • (2022)Recent Advancements in Intrusion Detection in Software Defined Network SecurityInternational Journal of Scientific Research in Science and Technology10.32628/IJSRST22913(35-42)Online publication date: 1-Jan-2022
    • (2022)Collaborative Flow-Identification Mechanism for Software-Defined Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2021.30998229:5(3457-3464)Online publication date: 1-Mar-2022
    • (2021)NetworkAPI: An In-Band Signalling Application-Aware Traffic Engineering Using SRv6 and IP AnycastIEICE Transactions on Information and Systems10.1587/transinf.2020NTP0005E104.D:5(617-627)Online publication date: 1-May-2021
    • (2021)Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning ApproachIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2019.29026619:3(1529-1541)Online publication date: 1-Jul-2021
    • (2021)On Traffic Classification in Enterprise Wireless Networks2021 National Conference on Communications (NCC)10.1109/NCC52529.2021.9530062(1-6)Online publication date: 27-Jul-2021
    • (2021)IPv6 Flow-Label based Application Aware Routing in SDNsIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFOCOMWKSHPS51825.2021.9484442(1-6)Online publication date: 10-May-2021
    • (2021)A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning ApproachTowards new e-Infrastructure and e-Services for Developing Countries10.1007/978-3-030-70572-5_12(185-202)Online publication date: 4-Mar-2021
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