基于需求预测的PaaS平台资源分配方法

针对缺乏PaaS平台下资源需求的有效预测与优化分配的问题,提出一种资源需求预测模型和分配方法。首先,根据PaaS平台中应用对资源需求的周期性来对资源序列进行切分,并在短期预测的基础上结合应用的多周期性特征,利用多元回归算法建立综合的预测模型。然后,基于MapReduce架构设计实现了一个Master-Slave模式的PaaS平台资源分配系统。最后,结合当前任务请求和资源需求预测结果进行资源分配。实验结果表明,采用该资源需求预测模型和分配方法后,相比于自回归模型和指数平滑算法,平均绝对百分比误差分别下降8.71个百分点和2.07个百分点,均方根误差分别下降2.01个百分点和0.46个百分点。所提预测模型的预测结果不仅误差小,与真实值的拟合程度也较高,而且利用较小的时间开销就可以获得较高的准确度。此外,使用该预测模型的PaaS平台的资源请求的平均等待时间有了明显的下降。

PaaS platform resource allocation method based on demand forecasting

In view of the lack of effective resource demand forecasting and optimal allocation in Platform-as-a-Service (PaaS) platform, a resource demand forecasting model and an allocation method were proposed. Firstly, according to the periodicity of the application demand for resources in PaaS platform, the resource sequence was segmented. And on the basis of short-term prediction, combined with the multi-periodicity characteristics of the application, a comprehensive prediction model was established by using the multiple regression algorithm. Then, based on MapReduce architecture, a PaaS platform resource allocation system based on Master-Slave mode was designed and implemented. Finally, the resources were allocated based on current task request and resource demand prediction results. The experimental results show that, compared with autoregressive model and exponential smoothing algorithm, the proposed resource demand forecasting model and allocation method has the mean absolute percentage error drop of 8.71 percentage points and 2.07 percentage points respectively, root mean square error drop of 2.01 percentage points and 0.46 percentage points respectively. It can be seen that the prediction result of the prediction model has little error and its fitting degree with real value is high, while high accuracy costs little time. Besides, the average waiting time of PaaS platform with the proposed prediction model for resource requests decreases significantly.