posted on 2021-08-11, 15:34authored byXiaoquan 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.
Funding
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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