pHeavy: Predicting Heavy Flows in the Programmable Data Plane

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...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management Vol. 18; no. 4; pp. 4353 - 4364
Main Authors: Zhang, Xiaoquan, Cui, Lin, Tso, Fung Po, Jia, Weijia
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communications traffic
Computational modeling
Decision making
decision tree
Decision trees
Hardware
Heavy flow
Machine learning
Machine learning algorithms
Predictive models
Programmable controllers
programmable data plane
Software
Software-defined networking
Switches
Training
ISSN:1932-4537, 1932-4537
Online Access:Get full text
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Summary: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.
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ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2021.3094514