基于压缩动量项的增量型ELM虚拟机能耗预测

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.