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A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers
Author(s) -
Duggan M.,
Shaw R.,
Duggan J.,
Howley E.,
Barrett E.
Publication year - 2019
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2635
Subject(s) - cloud computing , computer science , data center , scheduling (production processes) , artificial neural network , bandwidth (computing) , distributed computing , real time computing , data mining , operations research , artificial intelligence , mathematical optimization , computer network , engineering , operating system , mathematics
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.