基于压缩动量项的增量型ELM虚拟机能耗预测

在基于基础设施即服务（Infrastructure as a service，IaaS）的云服务模式下，精准的虚拟机能耗预测，对于在众多物理服务器之间进行虚拟机调度策略的制定具有十分重要的意义.针对基于传统的增量型极限学习机（Incremental extreme learning machine，I-ELM）的预测模型存在许多降低虚拟机能耗预测准确性和效率的冗余节点，在现有I-ELM模型中加入压缩动量项将网络训练误差反馈到隐含层的输出中使预测结果更逼近输出样本，能够减少I-ELM的冗余隐含层节点，从而加快I-ELM的网络收敛速度，提高I-ELM的泛化性能.

Virtual Machine Power Prediction Using Incremental Extreme Learning Machine Based on Compression Driving Amount

In cloud service models which is based on infrastructure as a service (IaaS), how to accurately predict power of virtual machine is very important for making scheduling strategy of virtual machines among many physical servers. However, the traditional incremental extreme learning machine (I-ELM) includes too many redundant hidden nodes, resulting in decreased efficiency and accuracy of virtual machine power prediction. Connecting compression driving amount to I-ELM, the paper builds the intelligent prediction model of I-ELM based on the compression driving amount (CDAI-ELM), and uses the model for predicting virtual machine power.