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.
| Original language | English (Ireland) |
|---|---|
| Pages (from-to) | 617-639 |
| Number of pages | 22 |
| Journal | Software: Practice and Experience |
| Volume | 49 |
| Issue number | 4 |
| Publication status | Published - 1 Jan 2019 |
Keywords
- CPU
- cloud computing
- network bandwidth
- neural network
- prediction algorithms
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Duggan, Martin and Shaw, Rachael and Duggan, Jim and Howley, Enda and Barrett, Enda
- Duggan, M. and Shaw, R. and Duggan, J. and Howley, E. and Barrett, E.
Fingerprint
Dive into the research topics of 'A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver