基于EMPC-BCGRU的云虚拟机CPU负载分析预测

Analysis and Prediction of Cloud VM CPU Load Based on EMPC-BCGRU

云平台资源预测对于云资源管理和节能具有非常重要的意义。云虚拟机技术是云平台为了充分利用物理资源而实施的一种虚拟化手段,但是有效的云虚拟机负载预测仍具有挑战性,因为云虚拟机负载具有周期性和非周期性的变化模式以及突变的负载峰值,云虚拟机负载受到用户随机提交作业的影响。为了准确分析云虚拟机负载的变化模式,提升云虚拟机CPU负载预测性能,提出了一种基于分解-预测的云虚拟机负载预测方法。通过经验模态分解和主成分分析的云虚拟机负载模式分解,得到不同尺度的特征波动序列;预测模型的卷积层能够充分提取分解后的特征,并通过双向门控循环神经网络双向学习序列的前向和后向依赖关系,提高了预测模型学习云虚拟机负载变化模式的能力。最后,在真实云环境微软Azure产生的2019 VM数据集上进行单步和多步预测实验,验证了所提预测方法的有效性。

Cloud platform resource prediction is of great significance for resource management and energy saving.Cloud VM technology is a virtualization method implemented by the cloud to make full use of physical resources,but effective cloud VM load prediction is still challenging,because the cloud VM load has periodic and aperiodic change patterns and sudden load peaks,and the cloud VM load is affected by the random submission of jobs by users.In order to accurately analyze the change mode of VM load and improve the performance of VM CPU load prediction,a cloud VM load prediction method based on decomposition-prediction is proposed.Through EMD and PCA of cloud VM load mode decomposition,the characteristic fluctuation sequences of different time scales are obtained.The convolution layer of the prediction model can fully extract the decomposed features,and learn the forward and backward dependencies of the sequence through the bidirectional gated cyclic neural network,which improves the ability of the prediction model to learn the load change mode of the VM.Finally,single-step and multi-step prediction experiments are performed on the 2019 VM data sets generated by Microsoft Azure in the real cloud environment,which verifies the effectiveness of the prediction method.