Presentation | 2021-04-15 Estimation of server power consumption using machine learning Katsumi Fujita, Eriko Iwasa, Masashi Kaneko, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A server power consumption is one of the problem in a data center. There are virtualization and DVFS approaches to address this problem. Estimating power consumption of servers is necessary for these approaches. Previous works presented server power models for CPU and memory intensive workloads. In this paper, we focused on disk intensive workloads and built the server power model by machine learning. Our method used processor performance events as input data. This model is applicable to a server power estimation for disk intensive workload. Moreover we analyzed the feature importance and found that different server power models are needed depending on the type of workloads. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | server / power consumption / machine learning |
Paper # | NS2021-6 |
Date of Issue | 2021-04-08 (NS) |
Conference Information | |
Committee | NS |
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Conference Date | 2021/4/15(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Online |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Traffic, Network evaluation, Performance, Resource control and management, Traffic engineering, Network reliability and resilience, Network Intelligence and AI, etc. |
Chair | Akihiro Nakao(Univ. of Tokyo) |
Vice Chair | Tetsuya Oishi(NTT) |
Secretary | Tetsuya Oishi(NTT) |
Assistant | Shinya Kawano(NTT) |
Paper Information | |
Registration To | Technical Committee on Network Systems |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Estimation of server power consumption using machine learning |
Sub Title (in English) | In the case of disk intensive workload |
Keyword(1) | server |
Keyword(2) | power consumption |
Keyword(3) | machine learning |
1st Author's Name | Katsumi Fujita |
1st Author's Affiliation | NTT(NTT) |
2nd Author's Name | Eriko Iwasa |
2nd Author's Affiliation | NTT(NTT) |
3rd Author's Name | Masashi Kaneko |
3rd Author's Affiliation | NTT(NTT) |
Date | 2021-04-15 |
Paper # | NS2021-6 |
Volume (vol) | vol.121 |
Number (no) | NS-2 |
Page | pp.pp.31-36(NS), |
#Pages | 6 |
Date of Issue | 2021-04-08 (NS) |