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An Elman Neural Network-based Prediction Model for the Power Consumption of Servers
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jiang, Xi Xue, Chuan Yin, Shengnan Han, Guangjie |
| Copyright Year | 2017 |
| Abstract | The growing number of the servers across the world has spurred the development of cloud computing. However, the appetite of the servers for power consumption is extraordinary because of the round-the-clock operation. This paper models the running process of the servers to be a nonlinear and time-variation system with uncertainties. An Elman neural network is established to find out the major factors that influence the power consumption of the server in different working conditions. Using the recommended input vector that consists of the major factors, the proposed Elman neural network correlate different kinds of performance data generated in the running of the server and transforms the data into energy information. The simulation results demonstrate that after training with the recommended input vector, the Elman neural network can provide the prediction results that has a high reliability. |
| Starting Page | 246 |
| Ending Page | 256 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.csroc.org.tw/journal/JOC28_6/1991-1599-28.6-22.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
