pHeavy: Predicting Heavy Flows in the Programmable Data Plane
Xiaoquan Zhang, Lin Cui, Fung Po Tso, Weijia Jia · IEEE Transactions on Network and Service Management · 2021
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 presentpHeavy, 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,pHeavyincludes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implementedpHeavyin both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino). Evaluation results demonstrate thatpHeavyhas 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,pHeavycan predict heavy flows at line rate on the P4 hardware switch.