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pHeavy: Predicting heavy flows in the programmable data plane

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journal contribution
posted on 11.08.2021, 15:34 by Xiaoquan Zhang, Lin Cui, Fung Po TsoFung Po Tso, Weijia Jia
Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we present pHeavy, a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane, pHeavy includes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implemented pHeavy in both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino).Evaluation results demonstrate that pHeavy has achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly, pHeavy can predict heavy flows at line rate on the P4 hardware switch.


Chinese National Research Fund (NSFC) No. 61772235 and 61872239

Natural Science Foundation of Guangdong Province No. 2020A1515010771

Science and Technology Program of Guangzhou No. 202002030372

SYNC: Synergistic Network Policy Management for Cloud Data Centres

Engineering and Physical Sciences Research Council

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FRuIT: The Federated RaspberryPi Micro-Infrastructure Testbed

Engineering and Physical Sciences Research Council

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InnovateUK grant 106199- 47198

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 AI-DS Research Hub



  • Science


  • Computer Science

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IEEE Transactions on Network and Service Management




AM (Accepted Manuscript)

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Dr Posco Tso. Deposit date: 30 June 2021