Load Prediction for Data Centers Based on Database Service

In the era of cloud computing, the over-occupancy of data center resources (CPU, memory, disk) and subsequent machine failure have resulted in great loss to users and enterprises. So it makes sense to anticipate the server workload in advance. Previous research on server workloads has focused on trend analysis and time series fitting. We propose an approach to forecast the workloads of servers based on machine learning. And our data comes from a database-based data center that is real, large-scale, and enterprise-class. We use the servers' historical monitoring data for our models to predict future workloads and hence provide the ability to automatically warn overload and reallocate resources. We calculate the failure detection rate and false alarm rate of our overload detection models, as well as put forward an evaluation based on the overload processing cost. Experimental results show that machine learning methods especially Random Forest can better predict the server load than traditional time series analysis method. We use the forecast results to propose some scheduling strategies to prevent server overload, achieve intelligent operation and maintenance, and failure prediction. Compared with the traditional time series analysis method, our method uses less data and lower dimensions, and yields more accurate predictions.