Name
Abstract | ||
|---|---|---|
Data centers play an important role on worldwide electrical energy consumption. Understanding their power dissipation is a key aspect to achieve energy efficiency. Some application specific models were proposed, while other generic ones lack accuracy. The contributions of this paper are threefold. First we expose the importance of modelling alternating to direct current conversion losses. Second, a weakness of CPU proportional models is evidenced. Finally, a methodology to estimate the power consumed by applications with machine learning techniques is proposed. Since the results of such techniques are deeply data dependent, a study on devices' power profiles was executed to generate a small set of synthetic benchmarks able to emulate generic applications' behaviour. Our approach is then compared with two other models, showing that the percentage error of energy estimation of an application can be less than 1 %. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00450-014-0264-x | Computer Science - Research and Development |
Keywords | Field | DocType |
Power estimation, Generic model, Data centers, Machine learning, Neural networks | Direct current,Application specific,Simulation,Computer science,Efficient energy use,Dissipation,Data dependent,Real-time computing,Artificial neural network,Small set,Computer engineering,Estimator | Journal |
Volume | Issue | ISSN |
30 | 2 | 1865-2042 |
Citations | PageRank | References |
5 | 0.48 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leandro Fontoura Cupertino | 1 | 7 | 0.84 |
| Georges Da Costa | 2 | 345 | 32.75 |
| Jean-Marc Pierson | 3 | 623 | 59.06 |