伺服器利用組件進行節能之評估 – 以Z公司為例

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姓名

吳宇媜(Yu-Zhen Wu) 查詢紙本館藏

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工業管理研究所在職專班

論文名稱

伺服器利用組件進行節能之評估 – 以Z公司為例

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摘要(中)

全球科技的迅速發展使得伺服器需求大增，這也導致了電力需求的劇增，進而引發了巨大的能源消耗。伺服器產業作為高能耗行業之一，其能源消耗對全球能源壓力造成了顯著負擔，也常常出現各大公司的數據中心因電力不足跳電而導致伺服器癱瘓使得用戶無法使用等危機。本研究旨在探討伺服器如何利用組件進行節能評估，以降低伺服器功耗進而減少產業造成的能源消耗及研發成本。通過對公司 Z 作為案例研究，本文詳細分析了如何利用組件在節能方面達到影響效益，通過對不同組件的評估和比較，本研究提出了一套簡單但有效的節能策略，可以幫助企業在保證伺服器運行效能的同時，最大程度地降低系統功耗值以達到節省能源消耗。該研究結果對於提高
伺服器效能使用、降低功耗值具有一定的實踐價值。研究結果顯示，通過採用組件進行節能評估，不僅能夠有效降低伺服器專案開發時的成本，同時也對全球能源危機做出了些微貢獻，有效地減輕全球能源壓力、緩解能源危機。

摘要(英)

The rapid development of global technology has greatly increased the demand for servers, which has also led to a dramatic increase in power demand, which in turn has led to huge energy consumption. As one of the high-energy consumption industries, the server industry′s energy consumption has placed a significant burden on global energy pressure. Crises such as Data Center outages of major companies often occur due to insufficient power, rendering servers paralyzed thereby affecting users. This study aims to explore how servers can conduct energy-saving assessments using configurations to reduce consumption and research and development costs in the server industry. Through a case study of Company Z, this paper analysis of how to achieve significant benefits in energy savings using configurations. By evaluating and comparing different configurations, this study proposes a set of simple but effective energy-saving strategies that can help enterprises reduce system power consumption to the greatest extent while ensuring server operating performance to save energy consumption.The findings indicate that by using configurations for energy-saving assessments, it is not only possible to effectively reduce the development costs of server projects but also make a slight contribution to global energy crisis mitigation, effectively alleviating global energy pressure.

關鍵字(中)

★ 伺服器節能
★ 數據中心
★ 組件
★ 功耗值
★ 能源消耗
★ 降低開發成本

關鍵字(英)

★ Server energy saving
★ Data Center
★ Configurations
★ Power consumption
★ Energy consumption
★ Reduce development cost

論文目次

中文摘要 ..................................................................................................................................... i
Abstract ....................................................................................................................................... ii
誌謝 ........................................................................................................................................... iii
目錄 ........................................................................................................................................... iv
圖目錄 ....................................................................................................................................... vi
表目錄 ....................................................................................................................................... ix
第一章緒論 .............................................................................................................................. 1
1.1 研究背景 ...................................................................................................................... 1
1.2 研究動機 ...................................................................................................................... 1
1.3 研究目的 ..................................................................................................................... 2
1.4 研究架構 ..................................................................................................................... 3
第二章文獻探討 ...................................................................................................................... 4
2.1 伺服器產業及數據中心的簡介 .................................................................................. 4
2.1.1 伺服器的應用 ................................................................................................... 4
2.1.2 數據中心及用電簡介 ....................................................................................... 4
2.2 全球缺電危機與節能規範 ......................................................................................... 5
2.2.1 全球能源不足危機 ........................................................................................... 5
2.2.2 台灣政府節能政策 ........................................................................................... 7
2.2.3 全球針對伺服器節能規範 - EU ..................................................................... 8
2.2.4 全球針對伺服器節能規範 - USA ................................................................... 9
2.2.5 目前台灣公司應用於伺服器的節能方案 ..................................................... 10
2.3 隨機森林 (Random Forest) ...................................................................................... 14
2.3.1 隨機森林訓練過程及優點 ............................................................................ 14
2.3.2 監督式學習 (Supervised Learning) ............................................................... 15
2.3.3 集成學習 (Ensemble Learning) ..................................................................... 15
2.3.4 過度擬合 (Overfitting) .................................................................................. 16
第三章研究方法與流程 ........................................................................................................ 17
3.1 研究流程 ................................................................................................................... 17
3.2 組件研究分類 ............................................................................................................ 17
3.3 組件試驗流程 ............................................................................................................ 20
3.4 資料庫建立 ................................................................................................................ 21
3.5 模型建立 .................................................................................................................... 27
3.5.1 搜尋模型的建立 ............................................................................................. 27
3.5.2 預測模型的建立 ............................................................................................. 30
3.6 模擬客戶需求 ............................................................................................................ 32
第四章研究結果與分析 ........................................................................................................ 39 4.1 搜尋模型結果分析 ................................................................................................... 39
4.1.1 A 客戶需求之結果分析 .............................................................................. 39
4.1.2 B 客戶需求之結果分析 .............................................................................. 40
4.2 預測模型結果分析 ................................................................................................... 44
第五章結論與建議 ................................................................................................................ 48
5.1 結論 ........................................................................................................................... 48
5.2 研究限制 ................................................................................................................... 50
5.3 建議 ........................................................................................................................... 50
中文參考文獻 .......................................................................................................................... 51
英文參考文獻 .......................................................................................................................... 52
附錄一 ...................................................................................................................................... 54
附錄二 ...................................................................................................................................... 56

參考文獻

[1] Deloitte Taiwan :〈2022 年趨勢解析《審計篇》新世代資料中心 DCOS 標準—下篇 : 再生能源運用於 IDC 多層次安全維護的綠色方案〉，2022 年 1 月，取自: https://www2.deloitte.com/tw/tc/pages/audit/articles/2022-outlook-audit2.html。
[2] 科技報橘 :〈面對淨零永續，「未來資料中心」的發展趨勢是什麼？〉，2022 年 7 月 28 日，取自: https://www.easpnet.com/news-20220728/。
[3] TrendForce 集邦科技 :〈全球資料中心產業淨零碳排關鍵布局與發展趨勢〉，2023年 8 月 23 日，取自: https://www.ctee.com.tw/news/20230823700971-431304。
[4] Michael Clegg :〈降低企業營運成本與碳足跡最佳化伺服器方案實現目標〉，2023年 10 月 12 日，取自:https://www.eettaiwan.com/20231012nt71-how-to-optimize-servers-to-lower-operating-costs-and-reduce-carbon-footprint/。
[5] 國家發展委員會 :〈臺灣 2050 淨零排放路徑及策略總說明〉，2022 年 3 月 20 日，取自: https://ws.ndc.gov.tw/Download.ashx?u=LzAwMS9hZG1pbmlzdHJhdG9yLzEwL3JlbGZpbG
UvMC8xNTA0MC82Yzg4MWJlZC04ZDBlLTRhZmEtOGY4ZC02NTI5ZTE1MjViMTQuc
GRm&n=6Ie654GjMjA1MOa3qOmbtuaOkuaUvui3r%2bW%2bkeWPiuetlueVpee4veiqquaY
jl%2fnsKHloLEucGRm&icon=.pdf。
[6] 經濟部能源局 : 〈民國 111 年電力消費量及各部門占比〉，2023 年 4 月版，取自https://www.moeaea.gov.tw/ECW/populace/content/wHandHotLink.ashx?no=34。
[7] 台灣電力公司 : 〈近十年來台電發購電量及結構〉，2023 年，取自: https://www.taipower.com.tw/tc/Chart.aspx?mid=194。
[8] ARTESYN 官網 : 〈CSU800AP Datasheet〉，2024 年，取自 :
https://www.advancedenergy.com/getmedia/a3b59e87-3984-4239-bdd9-03acb8099fff/ENG-
CSU800AP-600-235-01-03-16-24.pdf。
[9] Murata 官網 : 〈D1U86T-W-800-12-HB4C Datasheet〉，2018 年，取自: https://www.murata.com.cn/products/productdata/8807027245086/d1u86t-w-800-12-hxxc.pdf?1583754811000。
[10] Ahmed, Elbeltagi., Chaitanya, B., Pande., Manish, Kumar., Abebe, Debele, Tolche., Sudhir, Kumar, Singh., Akshaya, Mahesh, Kumar., Dinesh, Kumar, Vishwakarma. "Prediction of meteorological drought and standardized precipitation index based on the random forest
(RF), random tree (RT), and Gaussian process regression (GPR) models." Environmental Science and Pollution Research, vol. 30, 2023.
[11] Breiman, Leo. "Random forests." Machine learning, vol. 45.1, 2001, pages 5-32.
[12] Brown R., C. Webber and J.G. Koomey. “Status and future directions of the Energy Star program” Energy, vol. 27, no. 5, May 2002, pages 505-520.
[13] Buyya R., Beloglaz A. and Abwajy J. (2010). Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open
Challenges. Parallel and Distributed Processing Techniques and Applications (PDPTA), pages 6-20.
[14] Delmotte V. M., Zhai P., Pörtner H. O., Roberts D., Skea J., Shukla P. R., Pirani A., Okia
W. M., Péan C., Pidcock R., Connors S., Matthews J. B. R., Chen Y., Zhou X., Gomis M. I., Lonnoy E., Maycock T., Tignor M., Waterfield T. (2018). Global warming of 1.5°C. IPCC.
[15] European Commission. “COMMISSION REGULATION (EU) No 801/2013” European Commission, EU, 2013.
[16] European Commission. “COMMISSION REGULATION (EC) No 278/2009” European Commission, EU, 2009.
[17] European Commission. “COMMISSION REGULATION (EU) 2019/424” European Commission, EU, 2019.
[18] Ishwaran, Hemant, and Udaya B. Kogalur. "Random forests for survival, regression, and classification (RF-SRC)." R package, vol. 2.6, 2007.
[19] Jacksonm J. M., Koomey J. G., Nordman B. and Blazek M. “Data center power requirements: measurements from Silicon Valley” Energy, vol. 28, no. 8, pages 837-850.
[20] Lefurgy C., Rajamani K., Rawson F., Felter W., Kistler M. and Keller T. W. “Energy management for commercial servers” Computer, vol. 36, no. 12, 2003, pages 39-48.
[21] Liaw, Andy, and Matthew Wiener. "Classification and regression by randomForest." R news, vol. 2.3, 2002, pages 18-22.
[22] Massimo, Aria., Corrado, Cuccurullo., Agostino, Gnasso. "A comparison among interpretative proposals for Random Forests." Elsevier BV, vol. 6, 2021.
[23] Osisanwo, FY, Akinsola, J., E., T., Oludele, Awodele., Hinmikaiye, J., O., Olakanmi, O., Akinjobi, J.“"Supervised Machine Learning Algorithms: Classification and Comparison."
International Journal of Computer Trends and Technology, vol. 48, 2017, pages 128-138.
[24] Ward David O., Christopher D. Clark, Kimberly L. Jensen, Steven T. Yen and Clifford S. Russell. “Factors influencing willingness-to-pay for the ENERGY STAR® label” Energy Policy, vol. 39, no. 3, March 2011, pages 1450-1458.

指導教授

葉英傑

審核日期

2024-7-11

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