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针对供应虚拟机的IaaS(infrastructure as a service)云下虚拟机部署存在较高时延,导致弹性云服务效率低下的问题,提出一种基于ARIMA模型和季节指数的动态负载预测及资源估算的方法.该方法利用负载与虚拟机配置的关系,预测负载值,并估算虚拟机需求量,从而可提前部署虚拟机,提高IaaS云的服务效率.研究结合供应虚拟机的私有IaaS云环境,实现其下的弹性供应的资源决策模块.实验与算法分析表明,该方法能够准确决策虚拟机资源量,保证虚拟机资源预留,有效改善了IaaS云的弹性效率.
Aiming at the problem of high latency of virtual machine deployment in IaaS (Infrastructure as a service) supplying virtual machines and inefficiency of elastic cloud services, a dynamic load forecasting and resource estimation method based on ARIMA model and seasonal index is proposed This method uses the relationship between load and virtual machine configuration to predict the load value and estimate the demand of virtual machine so that the virtual machine can be deployed in advance to improve the service efficiency of IaaS cloud.Combining with the private IaaS cloud environment that provides virtual machine, The experimental results show that this method can accurately estimate the amount of virtual machine resources and ensure the reservation of virtual machine resources, which effectively improves the flexibility of IaaS cloud.