CAJ | 학술논문

云计算对计算能力的需求，促进了大规模数据中心的飞速发展。与此同时，云计算数据中心产生了巨大的能耗。由于云计算的弹性服务和可扩展性等特性，云计算数据中心的硬件规模近年来极度膨胀，这使得过去分散的能耗问题变成了集中的能耗问题。因此，深入研究云计算数据中心的节能问题具有重要意义。为此，针对云计算数据中心的能耗问题，提出了一种精确度高的能耗模型来预测云计算数据中心单台服务器的能耗状况。精确的能量模型是很多能耗感知资源调度方法的研究基础，在大多数现有的云计算能耗研究中，多采用线性模型来描述能耗和资源利用率之间的关系。然而随着云计算数据中心服务器体系结构的变化，能耗和资源使用率的关系已经难以用简单的线性函数来描述。因此，从处理器性能计数器和系统使用情况入手，结合多元线性回归和非线性回归的数学方法，分析总结了不同参数和方法对服务器能耗建模的影响，提出了适合云计算数据中心基础架构的服务器能耗模型。实验结果表明，该能耗模型在只监控系统使用率的情况下，在系统稳定后，能耗预测精度可达到95%以上。
운계산대계산능력적수구，촉진료대규모수거중심적비속발전。여차동시，운계산수거중심산생료거대적능모。유우운계산적탄성복무화가확전성등특성，운계산수거중심적경건규모근년래겁도팽창，저사득과거분산적능모문제변성료집중적능모문제。인차，심입연구운계산수거중심적절능문제구유중요의의。위차，침대운계산수거중심적능모문제，제출료일충정학도고적능모모형래예측운계산수거중심단태복무기적능모상황。정학적능량모형시흔다능모감지자원조도방법적연구기출，재대다수현유적운계산능모연구중，다채용선성모형래묘술능모화자원이용솔지간적관계。연이수착운계산수거중심복무기체계결구적변화，능모화자원사용솔적관계이경난이용간단적선성함수래묘술。인차，종처리기성능계수기화계통사용정황입수，결합다원선성회귀화비선성회귀적수학방법，분석총결료불동삼수화방법대복무기능모건모적영향，제출료괄합운계산수거중심기출가구적복무기능모모형。실험결과표명，해능모모형재지감공계통사용솔적정황하，재계통은정후，능모예측정도가체도95%이상。
Energy efficiency of cloud data centers has received significant attention recently as data centers often consume significant resources in operation. Most of the existing energy-saving algorithms focus on resource consolidation for energy efficiency. Accurate energy consumption model is the basis for these algorithms. This paper proposes an accurate energy model to predict energy consumption of single machine. In most of the existing cloud computing energy studies, linear models are used to describe the relationship between energy consumption and resource utilizations. However, with the changes in computer architecture, the relationship between energy and resource utilizations may not be linear. In fact, this paper explored a variety of regression analysis methods to estimate the energy consumption accurately while using low computational overhead. Initially, multiple linear regression models are used, but they often do not produce good enough results. Afterwards, this paper chooses three non-linear models and finally settled with the polynomial regression with Lasso as it produces the best estimation. Experimental results show that in adoption of energy model presented in this paper, the prediction accuracy can reach more than 95%.