Cloud computing provides a new infrastructure for the research of complex system simulation (CSS). However, insufficient computing resource allocation results in lower performance of a CSS application. On the other hand, excessive computing resource allocation will lead to the increase of simulation communication overhead and simulation synchronous computing. Therefore, accurate computing resource prediction is important to achieve optimal scheduling for CSS applications in the cloud environment. In this paper, a computing resource prediction approach based on ensemble learning has been proposed, which includes a cloud computing resource prediction framework and an intelligent ensemble algorithm. The framework with three–level architecture (simulation as a service, cloud computing resource predictor, and cloud computing resource pool) can provide computing resources to deal with the demands of the simulation applications. The intelligent ensemble algorithm uses an Accuracy and Relative Error-based Pruning algorithm to ensure the effective ensemble of base models (support vector machine, decision tree, and k-nearest neighbor). To improve the performance of the intelligent ensemble algorithm, a Feature Capability-based forward search Feature Selection algorithm is introduced to reduce redundancy between features. The experiments are presented to demonstrate that the intelligent ensemble algorithm can achieve higher accuracy by 4%-20% when compared with existing resource prediction models such as Regressive Ensemble Approach for Prediction, Bayesian, Linear Regression, Random Forest, and Fuzzy Neural Network.

云计算为复杂系统仿真（CSS）的研究提供了新的基础架构。但是，计算资源分配不足会导致CSS应用程序的性能降低。另一方面，过多的计算资源分配将导致仿真通信开销和仿真同步计算的增加。因此，准确的计算资源预测对于在云环境中实现CSS应用程序的最佳调度很重要。本文提出了一种基于集成学习的计算资源预测方法，该方法包括云计算资源预测框架和智能集成算法。具有三级架构的框架（模拟即服务，云计算资源预测器，和云计算资源池）可以提供计算资源来满足模拟应用程序的需求。智能集成算法使用基于精度和相对误差的修剪算法来确保基本模型（支持向量机，决策树和k最近邻）的有效集成。为了提高智能集成算法的性能，引入了一种基于特征能力的前向搜索特征选择算法，以减少特征之间的冗余。实验表明，与现有的资源预测模型（例如，回归集成预测方法，贝叶斯，线性回归，随机森林和模糊神经网络）相比，智能集成算法可将精度提高4％-20％。