Online Ensemble Learning Approach for Server Workload Prediction in Large Datacenters

Growing scale of server infrastructure in large datacenters has led to an increased need for effective server workload prediction mechanisms. Two main challenges faced in server workload prediction task are lack of large-scale training data and changes in the underlying distribution of server workloads in events like change in dominant applications of servers or change in allocation of servers, etc. In this work, we propose an online server workload prediction approach based on ensemble learning which addresses these issues. We evaluate the proposed approach using real dataset of an enterprise data center and a synthetic dataset. Experimental results reveal that the proposed approach achieves accuracy of 87.8% on real dataset and 88.8% on synthetic dataset.