本文選題：溫度模型 + 溫度預測��； 參考：《華中科技大學》2012年碩士論文

【摘要】：對存儲系統(tǒng)能耗的優(yōu)化研究不僅是日益增長的數(shù)據(jù)量的客觀需求，也是對綠色存儲、節(jié)能減排號召的響應。在不考慮能耗的情況下單方面提高系統(tǒng)的性能，會導致電能的浪費，然而離開性能，單純來降低系統(tǒng)能耗也顯得意義甚微，因此，對存儲系統(tǒng)的優(yōu)化需要同時考慮性能與功耗兩個因素，在節(jié)省能耗的基礎(chǔ)上盡可能獲得滿足計算需求的性能。大量的實驗數(shù)據(jù)表明，溫度是影響存儲系統(tǒng)性能的一個重要的影響因素，同時，溫度也是能耗研究的一個不可忽視的條件，因此站在溫度的角度來對存儲系統(tǒng)的性能與功耗優(yōu)化進行研究有一定的研究價值。 針對存儲系統(tǒng)溫度過高會導致系統(tǒng)的性能下降，能耗上升這一問題，提出了一種溫度預測的方法，，該方法可以準確的預測存儲系統(tǒng)的溫度變化從而有效的控制系統(tǒng)溫度，防止溫度過高。首先從存儲系統(tǒng)的結(jié)構(gòu)出發(fā)，設計并實現(xiàn)了一種系統(tǒng)溫度與能耗數(shù)據(jù)的采集方法，有效采集存儲系統(tǒng)的實時溫度與能耗信息；其次，利用能量守恒定律結(jié)合熱力學公式對系統(tǒng)的溫度變化進行建模，同時采用遞歸最小二次方算法估算出系統(tǒng)溫度模型中的參數(shù)，通過將采集到的溫度歷史數(shù)據(jù)帶入模型，結(jié)合自適應調(diào)整對存儲系統(tǒng)的溫度變化進行預測；最后根據(jù)系統(tǒng)工作效率將系統(tǒng)的溫度分為三個溫度區(qū)域，針對這三個區(qū)域分別提出相應的系統(tǒng)溫度控制方法，結(jié)合負載轉(zhuǎn)移以及關(guān)盤操作來有效降低系統(tǒng)溫度。 對系統(tǒng)進行三個方面測試，首先對預測模型的準確性進行測試，結(jié)果顯示溫度預測模型可以準確預測出存儲系統(tǒng)的溫度變化趨勢；其次對比分析了有溫度控制和無溫度控制時系統(tǒng)溫度的變化曲線，發(fā)現(xiàn)溫度控制可以有效的將系統(tǒng)溫度控制在最優(yōu)溫度范圍內(nèi)；最后對系統(tǒng)功耗優(yōu)化進行測試，測試表明溫度控制對系統(tǒng)功耗的降低效果符合預期。
[Abstract]:The research on energy consumption optimization of storage system is not only the objective demand of increasing data volume, but also the response to the call of green storage, energy saving and emission reduction. Without considering the energy consumption, the unilateral improvement of the system performance will lead to the waste of electric energy. However, it is of little significance to reduce the system energy consumption simply by leaving the performance. Both performance and power consumption should be taken into account in the optimization of storage system. On the basis of saving energy consumption, the performance of computing requirements should be obtained as much as possible. A large number of experimental data show that temperature is an important factor affecting the performance of storage system, and temperature is also a condition that can not be ignored in the study of energy consumption. Therefore, it is valuable to study the performance and power optimization of storage system from the point of view of temperature. In view of the problem that excessive temperature of storage system will lead to the deterioration of system performance and increase of energy consumption, a temperature prediction method is proposed, which can accurately predict the temperature change of storage system and effectively control system temperature. Prevent excessive temperature. First of all, from the structure of the storage system, a data acquisition method of system temperature and energy consumption is designed and implemented, which effectively collects the real-time temperature and energy consumption information of the storage system. The temperature change of the system is modeled by energy conservation law combined with thermodynamic formula. At the same time, the parameters of the system temperature model are estimated by using the recursive least square algorithm, and the collected temperature historical data are brought into the model. Finally, the temperature of the system is divided into three temperature regions according to the working efficiency of the system, and the corresponding system temperature control method is put forward for the three regions. Combined with load transfer and diskette operation to effectively reduce system temperature. The system is tested in three aspects. Firstly, the accuracy of the prediction model is tested. The results show that the temperature prediction model can accurately predict the temperature trend of the storage system. Secondly, the temperature variation curves of the system with and without temperature control are compared and analyzed. It is found that temperature control can effectively control the system temperature within the optimal temperature range. Finally, the power consumption optimization of the system is tested. The test results show that the temperature control can reduce the power consumption of the system.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP333

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