Name
Abstract | ||
|---|---|---|
Leveraging power consumption models in software systems can achieve easy deployment of low-cost, high-availability power monitoring in cloud datacenters that are usually large-scale, heterogeneous and frequently scaling up. However, traditional regression-based power consumption models generally have two drawbacks. First, their mathematical forms are usually fixed and determined a priori. This may... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCC.2019.2922379 | IEEE Transactions on Cloud Computing |
Keywords | DocType | Volume |
Servers,Power demand,Cloud computing,Monitoring,Computational modeling,Artificial neural networks | Journal | 9 |
Issue | ISSN | Citations |
4 | 2168-7161 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentai Wu | 1 | 34 | 3.77 |
| Weiwei Lin | 2 | 147 | 13.95 |
| Ligang He | 3 | 542 | 56.73 |
| Guangxin Wu | 4 | 2 | 0.72 |
| Ching-Hsien Hsu | 5 | 1121 | 125.53 |