| Autor: |
Guangxu Li, Junke Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Engineering Reports, Vol 6, Iss 5, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2577-8196 |
| DOI: |
10.1002/eng2.12777 |
| Popis: |
Abstract In mobile edge computing (MEC), accurately predicting and monitoring the energy consumption of edge servers is a key challenge in achieving green computing. The importance of solving this problem is that it can help optimize the energy usage in data centers and thus reduce the carbon emission of MEC. To this end, we propose an innovative entropy‐based power modeling framework called entropy weighted model (EWM). The EWM framework weights and combines classical prediction models by analyzing the major components of a server and selecting appropriate parameters. We validate the performance of EWM using real server power and performance counter data and compare it with other classical prediction models by Friedman test. The results show that EWM outperforms other classical prediction models in all test datasets. This result validates the significant advantages of our EWM framework in solving the critical problem of edge server power prediction, and provides an effective tool for achieving data center energy optimization and promoting green computing, resulting in a highly general and accurate prediction model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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