基于优化决策树的时延敏感流智能感知调度

王雪荣; 唐政治; 李银川; 齐美玉; 朱建波; 张亮

doi:10.11959/j.issn.1000-0801.2023095

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基于优化决策树的时延敏感流智能感知调度

研究与开发 | 更新时间：2024-06-05

- 基于优化决策树的时延敏感流智能感知调度
- Delay-sensitive traffic intellisense scheduling based on optimal decision tree
- 电信科学 2023年39卷第4期页码：120-132
- 作者机构：
  
  1. 中国电信股份有限公司研究院，广东广州 510660
  2. 北京华为数字技术有限公司，北京 100085
- 作者简介：
  
  [ "王雪荣（1997-），女，中国电信股份有限公司研究院工程师，主要研究方向为 IP 网络技术与下一代互联网" ]
  [ "唐政治（1991-），男，博士，北京华为数字技术有限公司高级工程师，主要研究方向为网络流量分析、网络人工智能技术" ]
  [ "李银川（1994-），男，博士，北京华为数字技术有限公司高级工程师，主要研究方向为人工智能技术" ]
  [ "齐美玉（1983-），女，博士，北京华为数字技术有限公司技术专家，主要研究方向为网络人工智能技术" ]
  [ "朱建波（1979-），男，北京华为数字技术有限公司技术专家，主要研究方向为骨干路由器架构设计" ]
  [ "张亮（1982- ），男，博士，北京华为数字技术有限公司技术专家，主要研究方向为智能故障分析、网络流量分析和网络优化" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023095
  中图分类号： TP393
- 网络出版日期：2023-04，
  
  纸质出版日期：2023-04-20
- 稿件说明：
移动端阅览
王雪荣, 唐政治, 李银川, 等. 基于优化决策树的时延敏感流智能感知调度[J]. 电信科学, 2023,39(4):120-132.

Xuerong WANG, Zhengzhi TANG, Yinchuan LI, et al. Delay-sensitive traffic intellisense scheduling based on optimal decision tree[J]. Telecommunications science, 2023, 39(4): 120-132.
王雪荣, 唐政治, 李银川, 等. 基于优化决策树的时延敏感流智能感知调度[J]. 电信科学, 2023,39(4):120-132. DOI： 10.11959/j.issn.1000-0801.2023095.

Xuerong WANG, Zhengzhi TANG, Yinchuan LI, et al. Delay-sensitive traffic intellisense scheduling based on optimal decision tree[J]. Telecommunications science, 2023, 39(4): 120-132. DOI： 10.11959/j.issn.1000-0801.2023095.

摘要

目前流量调度策略无法做到智能按需化，尤其对于网络突发故障造成的拥塞以及高价值业务的护航场景，无法按需保障时延敏感的业务体验。通过分析研究不同网络业务流量时延敏感性属性需求，探索挖掘不同网络业务流量的行为特征与其时延敏感性需求之间的内在关联关系。然后利用AI技术对这种内在的关联关系进行学习，构建其映射关系，实现了时延敏感流智能感知调度。同时，考虑AI模型的可解释性及可部署性实际问题，采用强化学习剪枝优化可解释性决策树模型，提高模型的鲁棒性同时使模型更轻量化，易于设备部署实现。通过真实网络流量实验，强化学习优化后的决策树模型在单次推理情形下感知正确率提高1.75%，推理速度提升约 30%；同时，实验也证明了使用局部微观统计特征多次推理有助于提高模型感知正确率。在所有实验中，强化学习优化的决策树模型规模缩小了60.0%～87.2%，并且Saras比Q-learning具有更好的优化表现。

Abstract

Currently

network traffic scheduling strategy cannot be intelligent and on-demand

especially in the congestion caused by sudden network failures and escort scenarios of high-value services.They cannot guarantee latency-sensitive service experience on demand.The delay-sensitive attribute requirements of different network traffic were analyzed and studied

and the internal correlation between the behavior characteristics of varying network traffic and its delay sensitivity requirements was explored.Then

AI technology was used to learn this inherent relationship and construct its mapping relationship

realizing a traffic scheduling technical solution based on the intelligent awareness of delay-sensitive traffic.At the same time

considering the practical issues of interpretability and deploy-ability of AI models

reinforcement learning (RL) technology was used to prune and optimize the interpretable decision tree model

which improved the robustness of the model and made model lighter and easier to implement in equipment deployment.Through experiments by the collected real network traffic

the decision tree model optimized by reinforcement learning could improve the awareness accuracy by 1.75% in a single inference case

and the inference performance was improved by about 30%.The experiment also proved that using micro-statistical features for multiple inferences could help improve the model accuracy; in all experiments

the scale of the decision tree model optimized by RL was reduced by about 60.0%～87.2%

and the Saras had better optimization performance than Q-learning.

关键词

Keywords

references

张彤 , 冯佳琦 , 马延滢 , 等 . 时间敏感网络流量调度综述 [J ] . 计算机研究与发展 , 2022 , 59 ( 4 ): 747 - 764 .

ZHANG T , FENG J Q , MA Y Y , et al . Survey on traffic scheduling in time-sensitive networking [J ] . Journal of Computer Research and Development , 2022 , 59 ( 4 ): 747 - 764 .

李文信 , 齐恒 , 徐仁海 , 等 . 数据中心网络流量调度的研究进展与趋势 [J ] . 计算机学报 , 2020 , 43 ( 4 ): 600 - 617 .

LI W X , QI H , XU R H , et al . Data center network flow scheduling progress and trends [J ] . Chinese Journal of Computers , 2020 , 43 ( 4 ): 600 - 617 .

AURELI D , CIANFRANI A , DIAMANTI A , et al . Going beyond DiffServ in IP traffic classification [C ] // Proceedings of NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium . New York:ACM Press , 2020 : 1 - 6 .

ALIZADEH M , YANG S , SHARIF M , et al . pFabric:minimal near-optimal datacenter transport [J ] . ACM SIGCOMM Computer Communication Review , 2013 , 43 ( 4 ): 435 - 446 .

BAI W , CHEN L , CHEN K , et al . PIAS:practical information-agnostic flow scheduling for commodity data centers [C ] // Proceedings of IEEE/ACM Transactions on Networking . Piscataway:IEEE Press , 2017 : 1954 - 1967 .

CHEN L , LINGYS J , CHEN K , et al . AuTO:scaling deep reinforcement learning for datacenter-scale automatic traffic optimization [C ] // Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication . New York:ACM Press , 2018 : 191 - 205 .

Intelligent buffer management on Cisco Nexus 9000 series switches white paper [EB ] . 2017 .

Application-aware routing [EB ] . 2022 .

WU M K , HUANG H , SUN Y , et al . Active Keeper:an accurate and efficient algorithm for finding top-k elephant flows [J ] . IEEE Communications Letters , 2021 , 25 ( 8 ): 2545 - 2549 .

XIE K , TIAN J Z , WANG X , et al . Efficiently inferring top-k elephant flows based on discrete tensor completion [C ] // Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2019 : 2170 - 2178 .

LOU K H , YANG Y J , WANG C C . An elephant flow detection method based on machine learning [C ] // Proceedings of 2019 International Conference on Smart Computing and Communication . Cham:Springer , 2019 : 212 - 220 .

AL-SAADI M , KHAN A , KELEFOURAS V , et al . Unsupervised machine learning-based elephant and mice flow identification [C ] // Proceedings of the 2021 Computing Conference . Cham:Springer , 2021 : 357 - 370 .

ZUO P , SHU Y A . An elephant flows scheduling method based on feedforward neural network [C ] // Proceedings of 2021 World Conference on Computing and Communication Technologies . Piscataway:IEEE Press , 2021 : 16 - 20 .

Configuration guidelines for DiffServ service classes [EB ] . 2006 .

WANG W , ZHU M , ZENG X W , et al . Malware traffic classification using convolutional neural network for representation learning [C ] // Proceedings of 2017 International Conference on Information Networking . Piscataway:IEEE Press , 2017 : 712 - 717 .

MOORE A W , ZUEV D . Discriminators for use in flow-based classification [R ] . 2005 .

SUTHAHARAN S . Decision tree learning [M ] // Machine learning models and algorithms for big data classification . Boston : Springer , 2016 : 237 - 269 .

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