KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Transmission Delay Estimation-based Forwarding Strategy for Load Distribution in Software-Defined Network

SDN 환경에서 효율적 Flow 전송을 위한 전송 지연 평가 기반 부하 분산 기법 연구

  • 김도현 (경희대학교 컴퓨터공학과) ;
  • 홍충선 (경희대학교 컴퓨터공학과)
  • Received : 2016.10.07
  • Accepted : 2017.01.18
  • Published : 2017.05.15
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Abstract

In a centralized control structure, the software defined network controller manages all openflow enabled switched in a data plane and controls the telecommunication between all hosts. In addition, the network manager can easily deploy the network function to the application layer with a software defined network controller. For this reason, many methods for network management using a software defined network concept have been proposed. The main policies for network management are related to traffic Quality of Service and resource management. In order to provide Quality of Service and load distribution for network users, we propose an efficient routing method using a naive bayesian algorithm and transmission delay estimation module. In this method, the forwarding path is decided by flow class and estimated transmission delay result in the software defined network controller. With this method, the load on the network node can be distributed to improve overall network performance. The network user also gets better dynamic Quality of Service.

Software-Defined Network의 등장은 하드웨어적인 네트워크 기능들을 소프트웨어적인 형태의 모듈로 Controller에 보다 유연하게 적용시키도록 함으로써 전통적인 네트워크의 구조를 변화시키고 있다. 이러한 환경 속에서 최근 네트워크 트래픽에 대한 Quality of Service 및 자원관리와 같은 다양한 관점에서의 네트워크 관리정책에 대한 연구개발이 진행되고 있고, 이러한 관리정책을 뒷받침 할 수 있는 네트워크 모니터링에 대한 기법들 또한 제시되어 왔다. 이에 본 논문에서는 기계 학습 기법인 Naive Bayesian Classification을 통하여 Flow를 분류한 후, 전송 지연 측정 모듈을 통하여 효율적인 전송경로를 선정하는 기법을 제안한다. 이는 다양한 대역폭을 갖는 여러 경로들로 이루어진 네트워크상에서 효율적인 경로 분배 역할을 할 수 있고, 부하를 분산시킴으로써 보다 원활한 네트워크 환경 및 서비스 품질을 제공할 수 있다.

Keywords

Acknowledgement

Supported by : 한국연구재단, 정보통신기술진흥센터

References

  1. Christian Strizke, Claudia Priesterjahn, Pedro A. Aranda Gutierrez, "Towards a Method for endto- end SDN App Development," 4th European Workshop on Software Defined Networks, pp. 107-108, 2015.
  2. Jun Zhang, Yang Xiang, Yu Wang, Wanlei Zhou, Yong Xiang, Yong Guan, "Network Traffic Classification Using Correlation Information," IEEE Transactions on Parallel and Distributed System, Vol. 24, No. 1, pp. 104-117, Jan. 2013. https://doi.org/10.1109/TPDS.2012.98
  3. Lu He, Chen Xu, Yan Luo, "vTC: Machine Learning Based Traffic Classification as a Virtual Network Function," 2016 ACM International Workshop on Security in Software-Defined Networks & Network Function Virtualization, pp. 53-56, 9-11 Mar. 2015.
  4. Davide Adami, Lisa Donatini, Stefano Giordano, Michele Pagano, "A Network Control Application enabling Software Defined Quality of Service," IEEE International Conference on Communications(ICC) 2015, pp. 6074-6079, 8-12 Jun. 2015.
  5. Naman Grover, Nitin Agarwal, Kotaro Kataoka, "liteFlow: Lightweight and Distributed Flow Monitoring Platform for SDN," 2015 1st IEEE Conference on Network Softwarization(Netsoft), pp. 1-9, 13-17, Apr. 2015.
  6. Do Hyeon Kim, Hyo Sung Kang, Nam Ho Kim, Jin Won Lee, Choong Seon Hong, "QoS Technique Using Probabilistic Packet Classification in Software- Defined Network Environment," Proc. of the 42th KIISE Winter Conference, pp. 943-945, 17-19, Dec. 2015. (in Korean)
  7. Taejune Park, Seungsoo Lee and Seoungwon Shin, "A Reflectornet Based on Software Defined Network," The Journal of Korea Information and Communications Society, Vol. 39B, No. 06, pp. 397-405, 2014. (in Korean) https://doi.org/10.7840/kics.2014.39B.6.397
  8. Federico Montesino Pouzols, Diego R. Lopez, Angel Barriga Barros, Mining & Control of Network Traffic by Computational Intelligence, Springer, pp. 87-145, 2011.