基于ARIMA模型的虚拟资源动态调度方法  

Dynamic Scheduling Method of Virtual Resources Based on ARIMA Model

作　　者：杨冬菊 邓崇彬 

机构地区：[1]大规模流数据集成与分析技术北京市重点实验室,北京100144 [2]北方工业大学云计算研究中心,北京100144

出　　处：《计算机科学》2017年第10期14-18,44,共6页Computer Science

基　　金：北京市自然科学基金重点项目:面向大规模流数据处理的数据空间理论和关键技术(4131001)资助

摘　　要：将应用部署到云端已经成为业界越来越普遍的做法,高并发、大流量已经成为多数云应用的一大特征。如何应对不断增长的高并发和用户流量的激增、合理利用资源、保障应用的稳定运行是云资源管理需要解决的重要问题。针对基于监控数据进行资源调整的方式容易引发资源调整滞后的问题,提出了一种基于ARIMA预测模型进行资源调整的虚拟资源动态调度方法。该方法能够根据预测的请求量,结合当前资源的负载能力来计算所需的资源规模,从而进行虚拟机资源的配置或释放。实验结果表明,所采用的预测模型能够较好地拟合实验的场景,通过使用基于预测模型的资源调度算法能够及时、有效地保证云服务质量。Deploying applications to the cloud has become an increasingly common practice in the industry,high concurrency and high traffic have become major features of most cloud applications.How to deal with the rising of the high concurrency and the surge of user traffic,to use resources reasonably,and to ensure the stable operation of the application,are important issues to solve for the cloud resource management.Considering the adjustment of resources based on monitoring data is easy to trigger the delay of resource scheduling,a dynamic scheduling method for resource adjustment based on ARIMA prediction model was proposed in this paper.The method can calculate the required resource size according to the demand of the forecast and the load capacity of the current resources scale,thus configurating or releasing the virtual machine resources.The experimental results show that the prediction model can fit the scene well.By using the predictive model,the resource scheduling algorithm can effectively guarantee the quality of cloud services in a timely and effective manner.

关 键 词：云应用 流量激增 服务质量 预测模型 资源动态调度 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]