Integrated deep learning method for workload and resource prediction in cloud systems
Document Type
Article
Publication Date
2-1-2021
Abstract
Cloud computing providers face several challenges in precisely forecasting large-scale workload and resource time series. Such prediction can help them to achieve intelligent resource allocation for guaranteeing that users’ performance needs are strictly met with no waste of computing, network and storage resources. This work applies a logarithmic operation to reduce the standard deviation before smoothing workload and resource sequences. Then, noise interference and extreme points are removed via a powerful filter. A Min–Max scaler is adopted to standardize the data. An integrated method of deep learning for prediction of time series is designed. It incorporates network models including both bi-directional and grid long short-term memory network to achieve high-quality prediction of workload and resource time series. The experimental comparison demonstrates that the prediction accuracy of the proposed method is better than several widely adopted approaches by using datasets of Google cluster trace.
Identifier
85098779545 (Scopus)
Publication Title
Neurocomputing
External Full Text Location
https://doi.org/10.1016/j.neucom.2020.11.011
e-ISSN
18728286
ISSN
09252312
First Page
35
Last Page
48
Volume
424
Grant
61802015
Fund Ref
Alexander von Humboldt-Stiftung
Recommended Citation
Bi, Jing; Li, Shuang; Yuan, Haitao; and Zhou, Meng Chu, "Integrated deep learning method for workload and resource prediction in cloud systems" (2021). Faculty Publications. 4351.
https://digitalcommons.njit.edu/fac_pubs/4351
