容器作为物理资源的逻辑抽象,具有资源占用少、资源供给快等特点,适合工作负载突变的互联网应用模式,特别是面向微服务架构的新型服务范型.已有工作受限于物理机和虚拟化环境,或资源难以弹性供给或资源供给时效性较差,难以应对负载突变(flash-crowds)场景.针对此问题提出了一种服务质量(quality of service,QoS)敏感的、基于前馈的容器资源弹性供给方法,该方法采用排队论刻画工作负载、资源利用率和响应时间的关联关系,构建应用性能模型.其中,响应时间采用模糊自适应卡尔曼滤波进行预测(前馈控制器),预测结果违背QoS是触发资源弹性供给的依据.基于CloudStone基准的实验结果显示,前馈控制器具有快速收敛的特点,对响应时间的预测误差小于10%.在flash-crowds场景下,相对于已有方法可有效保障应用的QoS.%As a logical abstraction of physical resources,container-based virtualization has been adopted widely in cloud computing environment for elastic resource provisioning,which is lower overhead and potentially better performance.Nowadays,more and more enterprises seek to move large-scale Internet-based applications with micro-service architecture into the container-based infrastructure,and focus on efficient resource management.Unfortunately,many existing approaches were restricted by physical machine or virtual environment,the resources are hard to be elastically or timely provisioning.Therefore,Internet-based applications may suffer from frequent service-level agreement(SLA) violations under flash-crowd conditions.To address this limitation,this thesis proposes a quality of service(QoS) sensitive resource provisioning approach for containers in micro-service architecture based on the feed-forward control.We employ a performance model based on queuing theory.Firstly,we capture the relationship among workload,resource utilization and response time.Secondly,we predict the response time with fuzzy federal adaptive Kalman filtering based on the feed-forward control,and if the prediction result is against pre-defined QoS,elastic resource scheduling process is triggered.Experimental results based on CloudStone show that the feed-forward algorithm converges quickly.The prediction result of the response time has only maximum error of 10%,and is more effective and accurate compared with existing approaches.Furthermore,our approach can effectively protect resource provisioning for flash-crowds workload.
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