Adaptive Resource Prediction in the Cloud Using Linear Stacking Model
Shasha Liao, Hongjie Zhang, Guansheng Shu, Jing Li · 2017
Resource demands prediction has become a promising tool to facilitate automatic scaling of resource management, which makes it feasible to reduce the cost and improve resource utilization in the cloud. Most current prediction methods of resource demands are based on a single model. However, the resource load patterns are usually diversity and variability in the cloud, it is hard to get the satisfactory prediction performance through a single model. To solve this problem, LSRP is proposed in this paper which is a novel ensemble approach for resource demands prediction. The method contains two important learning phases. First, we use a set of sub-models to predict the resource demands respectively based on the historic workloads. Second, we regard the results of sub-models as inputs to a second-level model. This second-level model is a linear model which is trained to combine the sub-models. Extensive experiments on production data set demonstrate that the proposed model outperforms the sub-models and other ensemble models in accuracy and adaptation.