A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.