pheavy-zhang-2021.pdf (3.13 MB)
Download filepHeavy: Predicting heavy flows in the programmable data plane
journal contribution
posted on 2021-08-11, 15:34 authored by Xiaoquan Zhang, Lin Cui, Fung Po TsoFung Po Tso, Weijia JiaSince 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
Find out more...FRuIT: The Federated RaspberryPi Micro-Infrastructure Testbed
Engineering and Physical Sciences Research Council
Find out more...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
History
School
- Science
Department
- Computer Science
Published in
IEEE Transactions on Network and Service ManagementVolume
18Issue
4Pages
4353-4364Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2021-06-24Publication date
2021-07-05Copyright date
2021eISSN
1932-4537Publisher version
Language
- en