ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (6): 1307-1317.doi: 10.7544/issn1000-1239.2021.20201087

Special Issue: 2021云网融合专题

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Algorithm of Mixed Traffic Scheduling Among Data Centers Based on Prediction

Wang Ran1,2, Zhang Yuchao1, Wang Wendong1,2, Xu Ke3, Cui Laizhong4   

  1. 1(School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876);2(State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications), Beijing 100876);3(Department of Computer Science and Technology, Tsinghua University, Beijing 100084);4(College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060)
  • Online:2021-06-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China ( 2019YFB1802603), the National Natural Science Foundation of China for Young Scientists (61802024), the Fundamental Research Funds for the Central Universities (2020RC36), the National Natural Science Foundation of China (62072047), the National Natural Science Foundation of China for Distinguished Young Scholars (61825204), and the Beijing Outstanding Young Scientist Program (BJJWZYJH01201910003011).

Abstract: To handle the problem of low link utilization resulting from mixing online and offline traffic in one data center transmission network and separating them with a fix way in the same link, we propose a solution of offline traffic scheduling based on online traffic prediction. It firstly predicts online traffic needed to be guaranteed preferentially in link using an algorithm calling Sliding-k that combines EWMA and Bayesian changepoint detection algorithm. This customized algorithm can make prediction sensitive to a sudden change of network environment and reduce unnecessary re-adjustments when network environment is steady at the same time. Therefore, it can exactly meet the prediction demand under different network environments. After computing the remaining space for offline traffic according to online traffic prediction result and implementing dynamic bandwidth allocation, it uses an algorithm called SEDF that can consider both traffic deadline and size to schedule offline traffic. Experimental results reflect that Sliding-k can meet the prediction needs both when network mutation occurs and when network has no change and can simultaneously improve the accuracy of traditional EWMA algorithm. The combination of Sliding-k and SEDF can improve the utilization of data center links, so as to make full use of link resources.

Key words: data center, traffic engineering (TE), prediction algorithm, scheduling, exponentially weighted moving average (EWMA)

CLC Number: