Earticle

초록

영어

In order to improve the performance of data transmission in SDN, this paper proposes a load balance solution scheme by taking advantage of the global network view of SDN. We collect 4 load features from each transmission path. These features are bandwidth utilization ratio, packet loss rate, transmission latency and transmission hops. By using this 4 load features, BP Artificial Neural Network model is trained to predict the integrated load for different path and to choose one with least load as the data-flow transmission path. The contrast experiment results show that load balancing strategy proposed in this paper can select more rational transmission path for data-flow and achieve 19.3% network latency decrease at most.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Load Balance System Design
 4. Path Features Extraction
 5. Load Balance Approach Based on Artificial Neural Network
 6. Experimental Results and Evaluation
  6.1 Experimental Environment
  6.2 Experimental Results Analyze
 7. Conclusion
 References

키워드

저자

• Cui Chen-xiao [ School of Computer, Beijing Information Science and Technology University, Beijing 100101, China ]
• Xu Ya-bin [ School of Computer, Beijing Information Science and Technology University, Beijing 100101, China, Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing 100101, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605