A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers. (16th September 2018)

• Record Type: Journal Article
• Authors: Duggan, M.; Shaw, R.; Duggan, J.; Howley, E.; Barrett, E.
• Other Names: Somani Gaurav guestEditor.; Zhao Xinghui guestEditor.; Srirama Satish Narayana guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 49:Number 4(2019)
• Page Start: 617
• Page End: 639
• Publication Date: 2018-09-16
• Subjects: cloud computing -- CPU -- network bandwidth -- neural network -- prediction algorithms; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2635
• Languages: English
• ISSNs: 0038-0644

Abstract

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.