基于预测的数据中心间混合流量调度算法  

Algorithm of Mixed Traffic Scheduling Among Data Centers Based on Prediction

作　　者：王然 张宇超 王文东[1,2] 徐恪[3] 崔来中[4] Wang Ran;Zhang Yuchao;Wang Wendong;Xu Ke;Cui Laizhong(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876;State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications),Beijing 100876;Department of Computer Science and Technology,Tsinghua University,Beijing 100084;College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,Guangdong 518060)

机构地区：[1]北京邮电大学计算机学院(国家示范性软件学院),北京100876 [2]网络与交换技术国家重点实验室(北京邮电大学),北京100876 [3]清华大学计算机科学与技术系,北京100084 [4]深圳大学计算机与软件学院,广东深圳518060

出　　处：《计算机研究与发展》2021年第6期1307-1317,共11页Journal of Computer Research and Development

基　　金：国家重点研发计划项目(2019YFB1802603);国家自然科学基金青年科学基金项目(61802024);中央高校基本科研业务费专项资金(2020RC36);国家自然科学基金项目(62072047);国家杰出青年科学基金项目(61825204);北京高校卓越青年科学家计划项目(BJJWZYJH01201910003011)。

摘　　要：为解决在线流量和离线流量共用一个数据中心传输网络,且2种类型的流量在链路中的分配模式固定不变而导致的链路利用率低的问题,提出了一种基于在线流量预测的离线流量调度方式.首先使用结合了EWMA方法和贝叶斯拐点检测算法的Sliding-k算法对链路中需要优先保障的在线流量进行预测,使预测既能在网络环境突然变化时灵敏响应,又能在网络平稳时减少不必要的重调整.根据预测结果计算出离线流量的可用剩余空间,实现动态的带宽分配之后,使用能够同时考虑流量截止时间和流量大小2个维度的SEDF算法对离线流量进行调度.实验结果表明:Sliding-k能够同时满足网络突变和网络无变化情况下的预测需求,并且能够提高传统EWMA方法的准确率,它和SEDF的结合能够提高数据中心链路的利用率.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.

关 键 词：数据中心 流量工程 预测算法 调度 指数加权移动平均 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]