Research on traffic identification and scheduling based on optical interconnection architecture in data center

Bingli GUO; Ning ZHAO; Zhiwen ZHU; Fan NING; Shanguo HUANG

doi:10.11959/j.issn.1000-436x.2018161

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Research on traffic identification and scheduling based on optical interconnection architecture in data center

Optical Transmission System and Optical Network Optimization | 更新时间：2024-06-05

- Research on traffic identification and scheduling based on optical interconnection architecture in data center
- Journal on Communications Vol. 39, Issue 9, Pages: 122-128(2018)
- 作者机构：
  
  1. 北京邮电大学信息光子学与光通信研究院，北京 100876
  2. 北京邮电大学信息与通信学院，北京 100876
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2018161
  CLC： TP393
- Online First：2018-09，
  
  Published：25 September 2018
- 稿件说明：
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Bingli GUO, Ning ZHAO, Zhiwen ZHU, et al. Research on traffic identification and scheduling based on optical interconnection architecture in data center[J]. Journal on Communications, 2018, 39(9): 122-128.
DOI：

Bingli GUO, Ning ZHAO, Zhiwen ZHU, et al. Research on traffic identification and scheduling based on optical interconnection architecture in data center[J]. Journal on Communications, 2018, 39(9): 122-128. DOI： 10.11959/j.issn.1000-436x.2018161.

摘要

为了解决数据中心链路拥塞问题，依据流量分布与类型的特点，提出了基于光互联架构的流量识别和调度方案，即HCFD（host-controller flow detection），旨在识别出对网络性能影响较大的大象流。利用SDN控制器下发转发策略，对网络中的流量进行合理调度。 HCFD首先在主机端利用Linux内核协议的Netfilter框架实现将超过阈值的数据流进行标记，然后在控制器端利用决策树分类模型再对标记流进行分类，最后利用光电混合网络的优势，实现深度融合的流量适配和切换机制。HCFD方案整合了已有方法的优势进行大象流识别，同时保证了识别的实时性、准确性以及流信息的全面性。实验与仿真结果显示，在此方案场景下，能有效缓解网络拥塞情况，充分利用网络带宽，减少数据端到端时延，降低分组丢失率。

Abstract

In order to solve the data center link congestion problem

based on the characteristics of the flow distribution and flow types

a flow identification and scheduling scheme based on optical interconnect structure

named HCFD (host-controller flow detection)

was proposed to identify the elephant flow which has a large impact on the network performance

and use the SDN controller to make forward strategy

and schedule the network traffic reasonably.The implementation of the scheme was to use the Netfilter framework in Linux kernel protocol on the host side to mark the flow that exceeds the threshold amount.Then

the classification model was used in the controller side to classify the marked flow.Finally

the appropriate forwarding strategy was developed based on the above results.With the advantage of the photoelectric network

mechanisms of flow depth fusion and switching could be realized.The scheme which integrates the advantage of the existing research results

was expected to identify elephant flow more accurately and comprehensively.It can effectively alleviate the network congestion

make full use of network bandwidth

reduce end-to-end delay and packet loss rate.

关键词

Keywords

references

CISCO . Cisco global cloud index:forecast and methodology,2015-2020 [R ] . SanJose:Cisco Public , 2016 .

SRIKANTH K , SUDIPTA S , ALBERT G , et al . The nature of data center traffic:measurements ＆ analysis [C ] // The 9th ACM SIGCOMM Conference on Internet Measurement (IMC '09) . 2009 : 202 - 208 .

GREENBERG A , HAMILTON J R , JAIN N , et al . VL2:a scalable and flexible data center network [J ] . Communications of the ACM , 2009 , 54 ( 4 ): 95 - 104 .

CURTIS A R , KIM W , YALAGANDULA P . Mahout:low-overhead datacenter traffic management using end-host-based elephant detection [C ] // IEEE INFOCOM . 2011 : 1629 - 1637 .

严军荣 , 叶景畅 , 潘鹏 . 一种大象流两级识别方法 [J ] . 电信科学 , 2017 , 33 ( 03 ): 36 - 43 .

YAN J R , YE J C , PAN P . A two-level method for elephant flow identification [J ] . Telecommunications Science , 2017 , 33 ( 3 ): 36 - 43 .

CHAO S C , LIN K C J , CHEN M S . Flow classification for software-defined data centers using stream mining [C ] // IEEE Transactions on Services Computing .

HUANG Y H , SHIH W Y , HUANG J L . A classification-based elephant flow detection method using application round on SDN environments [C ] // 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS) . 2017 : 231 - 234 .

WANG B , SU J . A survey of elephant flow detection in SDN [C ] // International Symposium on Digital Forensic and Security . 2018 : 1 - 6 .

蔡岳平 , 樊欣唯 , 王昌平 . 光电混合数据中心网络负载均衡流量调度机制 [J ] . 计算机应用与软件 , 2017 , 34 ( 08 ): 145 - 150+166 .

CAI Y P , FAN X W , WANG C P . Load balance traffic scheduling mechanism in an optical-electrical hybrid data center network [J ] . Computer Applications and Software , 2017 , 34 ( 8 ): 145 - 150+166 .

RAN B B , EINZIGER G , FRIEDMAN R , et al . Optimal elephant flow detection [J ] . IEEE INFOCOM , 2017 : 1 - 9 .

罗军舟 , 金嘉晖 , 宋爱波 , 等 . 云计算:体系架构与关键技术 [J ] . 通信学报 , 2011 , 32 ( 7 ): 3 - 21 .

LUO J Z , JIN J H , SONG A B , et al . Cloud computing:architecture and key technologies [J ] . Journal on Communications , 2011 , 32 ( 7 ): 3 - 21 .

GANG D , ZHENG H G , HONG W . Characteristics research on modern data center network[ [J ] . Journal of Computer Research and Development , 2014 , 51 ( 2 ): 395 - 407 .

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