An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model

Cloud computing provides different kind of services for users and provides with the help of internet. The Infrastructure as a service is a service model that provides virtual computing resources such as, networking, hardware, and storage services as needed for users. However, cloud-hosting initialization takes several minutes delay in the hardware resource allocation process. To resolve this issue, we need to predict the future amount of computing. In this paper, we propose a convolutional neural network and long short-term memory model for predicting multivariate workload which are the central processing unit, memory, and network usage. Firstly, the input data are analyzed by the vector auto regression method which filters the linear interdependencies among the multivariate data. Then, the residual data are computed and entered into the convolutional neural network layer which extracts complex features of each of the virtual machine usage components after the long short-term memory neural network, which is suitable for modeling temporal information of irregular trends in time series components in the proposed hybrid model latest activation function scaled polynomial constant unit used. The proposed model is compared with other predictive models. Based on the result, the proposed model shows the accuracy rate enhanced by approximately 3.8% to 10.9% and the error percentage rate also reduces by approximately 7% to 8.5% as compared to the other different models. It means the proposed technique improves central processing unit, memory, disk, and network usage in the network taking less amount of time due to the good predication approach compare to other models. In future research, we implement the proposed technique for VM energy section as well data predication system in cloud data center.

Automated summary

The proposed hybrid model improves the accuracy rate by approximately 3.8% to 10.9% and reduces the error percentage rate by approximately 7% to 8.5% compared to other models. This technique enhances central processing unit, memory, disk, and network usage in the network, providing better prediction results than other models.