云计算环境下基于主动预测的节点部署模型研究

Research on Resource Deployment Model Based on Active Prediction in Cloud Computing

随着云计算的不断普及,越来越多的用户选择将自身的业务迁移至云计算系统。用户的使用习惯与社会日常的运行规律也伴随着大量用户涌入云计算系统,如每早8点集中地向云计算系统申请资源节点,这给系统带来了一种可预期的资源冲击。针对上述问题,提出了一种基于主动预测模式的资源部署模型。该模型首先根据预测模块中Holt-Winters季节指数平滑模型的算法周期长度来预测下一个时间周期的任务请求量,通过设计的主动预测算法判断是否应对当前的任务请求量做出响应并得出其具体数量、位置等参数指标,以实现对用户使用规律的主动应对。使用CloudSim进行仿真实验,系统地评判模型的性能。实验结果表明,AF-HW模型在应对可预测的海量并发任务请求时可有效地提升单点及整体的响应速率,使用户得到更好的体验。

With the growing popularity of cloud computing,more and more users choose to migrate their business to the cloud computing system. Users＇ usage habits and social routine working laws swarm into the cloud computing sys- tem along with the influx of large numbers of users, such as applying intensively to cloud computer system for resource nodes as early as 8：00, which leads into a kind of predictable resources conflict. In view of the problems above, a re- source deployment model based on active prediction was proposed. Firstly, the task request amounts of next cycle are predicted according to the algorithm cycle length of Holt-Winters seasonal exponential smoothing model in prediction model, and determination of whether to make response to the current task request amounts or not and the specific amount, location and other parameter indicators should be made according to the active prediction algorithm designed to achieve active response capabilities to users＇ usage patterns. The simulation experiment was conducted using CloudSim, and the performance of model proposed was judged systematically. Experimental results show that AF-HW model can effectively enhance the single-point and overall response rate when responding to predictable massive and concurrent task requests,so that users can get a better experience.