A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers
Matthew B. Duggan, Rabindra Nath Shaw, James R. Duggan, Enda Howley, Enda Barrett · Software Practice and Experience · 2018
Summary 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.