pHeavy: Predicting Heavy Flows in the Programmable Data Plane

Author:

Zhang XiaoquanORCID,Cui LinORCID,Tso Fung PoORCID,Jia WeijiaORCID

Funder

Chinese National Research Fund

Natural Science Foundation of Guangdong

Science and Technology Program of Guangzhou

The UK Engineering and Physical Sciences Research Council

InnovateUK

Guangdong Key Lab of AI and Multi-modal Data Processing; BNU-UIC Institute of Artificial Intelligence and Future Networks funded by Beijing Normal University (Zhuhai) and AIDS Research Hub

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Multi-task Aware Resource Efficient Traffic Classification via in-Network Inference;Proceedings of the 2024 SIGCOMM Workshop on Networks for AI Computing;2024-08-04

2. A Machine Learning-Based Toolbox for P4 Programmable Data-Planes;IEEE Transactions on Network and Service Management;2024-08

3. Enabling locality-sensitive machine learning towards low predictive overhead in flow classification;Computer Networks;2024-08

4. AdaFlow: Efficient In-Network Traffic Classification using Programmable Switches;2024 IFIP Networking Conference (IFIP Networking);2024-06-03

5. Ultra-Low Latency User-Plane Cyberattack Detection in SDN-based Smart Grids;The 15th ACM International Conference on Future and Sustainable Energy Systems;2024-05-31

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