The Power Model of Data Center Server Based on Temporal Convolutional Network
To solve the problem of real-time server energy consumption estimation,a data center server energy consumption prediction method based on a temporal convolutional network is proposed.First,for the different workloads handled by the server,it is divided into four categories,namely central processing unit-intensive workload,memory-intensive workload,input/output intensive workload,and hybrid workload.Then,the method calculates the importance of characteristic parameters under four different workloads through a random forest algorithm and then selects representative parameters greater than the threshold as the input of the model.Finally,the temporal convolutional network is used to build the energy consumption prediction model of the data center server.The experimental results show that compared with other models,the average relative error of the proposed model is reduced by 2.18％～5.29％,which has certain advantages in the accuracy of energy consumption prediction.
data centergreen computingenergy consumption modelenergy consumption prediction
NETL
NSTL
万方数据
