Short-Term Load Forecasting of Virtual Machines Based on Improved Neural Network

Cloud computing allows applications to share resources on physical machines in an independent manner so as to effectively increase the utilization of physical machines. To enable cloud service users to obtain high-quality services, it is significant for cloud service providers to reduce the time required to allocate and deploy virtual machines, as well as to provide time windows for the deployment of physical servers. To ensure demand is met, one effective implementation method is to predict the workload of virtual machines in the future, as this contributes to the efficient deployment of a data center's requests from virtual machines to dynamically changing physical servers. However, due to the complexity of virtual machine requests, virtual machine workload prediction remains a significant challenge at present; on the one hand, it is difficult for standard recurrent neural networks to capture long-term dependencies because of the disappearance of gradients, while on the other hand, the long short-term memory method cannot handle irregular intervals. Accordingly, a new virtual machine workload prediction method that is capable of managing irregular time intervals is proposed in this paper. The proposed method combines the amount and time intervals of historical virtual machine work requests in order to accurately predict the virtual machine's future workload using a fixed length of time as a unit. Experiments show that the proposed model can generate more accurate prediction results than the long short-term memory method.