【摘要】：我們生活在一個(gè)數(shù)據(jù)信息的時(shí)代,人們的日常生活已經(jīng)離不開(kāi)數(shù)據(jù)和信息了,并且隨著時(shí)間的推移,數(shù)據(jù)開(kāi)始呈現(xiàn)出爆炸式增長(zhǎng)的趨勢(shì)。在數(shù)據(jù)急速增長(zhǎng)的背后,完成對(duì)這些數(shù)據(jù)存儲(chǔ)及加工的重任就交給了數(shù)據(jù)中心,數(shù)據(jù)中心為了應(yīng)對(duì)數(shù)據(jù)大規(guī)模增長(zhǎng)的趨勢(shì),它內(nèi)部的IT設(shè)備及其它輔助設(shè)備的規(guī)模也會(huì)逐漸擴(kuò)充,同時(shí)也會(huì)建造新的數(shù)據(jù)中心來(lái)保障新的需求。據(jù)估計(jì),數(shù)據(jù)中心一年的電能消耗占全球總電能消耗的1.5%左右,相當(dāng)于26個(gè)核電站一年的發(fā)電量,并且這個(gè)數(shù)字在未來(lái)還會(huì)增長(zhǎng)。如果不能及時(shí)對(duì)數(shù)據(jù)中心的能耗加以管理、未采取合適的措施降低數(shù)據(jù)中心的能耗,那么就可能會(huì)出現(xiàn)能源緊缺,數(shù)據(jù)中心也不能及時(shí)的完成用戶請(qǐng)求,這些都會(huì)影響到人們的日常生活的各個(gè)方面。 為了將數(shù)據(jù)中心變成“綠色”數(shù)據(jù)中心,我們需要研究并發(fā)現(xiàn)數(shù)據(jù)中心能耗的因素,通過(guò)合理的改善這些耗能因素降低數(shù)據(jù)中心的整體能耗。這些因素可能是環(huán)境因素,也可能是設(shè)備因素。本文針對(duì)數(shù)據(jù)中心能耗數(shù)據(jù)的分析開(kāi)展了相關(guān)的研究工作。主要的工作包括： 1)通過(guò)數(shù)據(jù)挖掘聚類算法對(duì)數(shù)據(jù)中心內(nèi)部設(shè)備的能耗進(jìn)行聚類。由于同一種類型的設(shè)備能耗較為接近而不同設(shè)備類型的能耗差異較大,所以可以通過(guò)聚類結(jié)果發(fā)現(xiàn)某些異常耗能的設(shè)備,對(duì)這些設(shè)備加以改造改善其能耗。 2)通過(guò)數(shù)據(jù)挖掘分類和預(yù)測(cè)算法對(duì)數(shù)據(jù)中心的歷史數(shù)據(jù)進(jìn)行分類并對(duì)未來(lái)進(jìn)行預(yù)測(cè)。這里提出了基于數(shù)據(jù)中心的歷史數(shù)據(jù)的分析對(duì)未來(lái)一段時(shí)間內(nèi)的能耗或業(yè)務(wù)請(qǐng)求量等的預(yù)測(cè),可以通過(guò)預(yù)測(cè)的結(jié)果,控制數(shù)據(jù)中心內(nèi)部一些設(shè)備的狀態(tài)(如開(kāi)啟或關(guān)閉),通過(guò)這種手段控制數(shù)據(jù)中心的能耗。 3)建設(shè)開(kāi)發(fā)能耗分析系統(tǒng),使得算法可以運(yùn)行在系統(tǒng)之上,并得以實(shí)際的應(yīng)用。使用人員通過(guò)系統(tǒng)的使用,方便他的操作,系統(tǒng)也增強(qiáng)了人機(jī)的交互性。目前,系統(tǒng)的一期已經(jīng)建設(shè)完畢,系統(tǒng)的功能包括統(tǒng)計(jì)分析,聚類分析、分類和預(yù)測(cè)分析三大模塊。每個(gè)模塊可以應(yīng)用于不同的場(chǎng)景對(duì)能耗數(shù)據(jù)進(jìn)行分析。
[Abstract]:We live in an era of data and information, people's daily life has become inseparable from data and information, and with the passage of time, the data began to show an explosive growth trend. Behind the rapid growth of data, the task of storing and processing these data is entrusted to the data center, which in order to cope with the trend of large-scale data growth, Its internal IT equipment and other ancillary equipment will also grow in size, and new data centers will be built to support new needs. It is estimated that data centers consume about 1.5 percent of the world's electricity a year, the equivalent of 26 nuclear power plants a year, and that number will grow in the future. If energy consumption in the data center is not managed in a timely manner and appropriate measures are not taken to reduce the energy consumption in the data center, there may be energy shortages and the data center will not be able to complete user requests in a timely manner. These will affect every aspect of people's daily life. In order to turn the data center into a "green" data center, we need to study and identify the energy consumption factors in the data center and reduce the overall energy consumption of the data center by reasonably improving these energy consumption factors. These factors may be environmental factors or equipment factors. In this paper, the energy consumption data analysis of the data center carried out related research work. The main work includes: 1) clustering energy consumption of internal equipments in data center through data mining clustering algorithm. Because the energy consumption of the same type of equipment is close and the energy consumption of different equipment types is quite different, we can find out some equipment with abnormal energy consumption by clustering results, and improve the energy consumption of these devices. 2) classify the historical data of data center and forecast the future by data mining classification and prediction algorithm. Here is the analysis of the historical data based on the data center, which can control the status of some devices (such as on or off) in the data center by forecasting the energy consumption or the quantity of business requests, etc., in the coming period, and the result of the prediction can be used to control the status of some devices in the data center. Control the energy consumption of the data center in this way. 3) build and develop the energy consumption analysis system, so that the algorithm can run on the system, and can be applied in practice. User through the use of the system, convenient for his operation, the system also enhanced the human-computer interaction. At present, the first phase of the system has been completed. The functions of the system include statistical analysis, cluster analysis, classification and prediction analysis. Each module can be applied to different scenarios to analyze energy consumption data.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP311.13;TP308

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