發(fā)布時(shí)間：2018-08-11 09:21

【摘要】：數(shù)據(jù)中心作為云計(jì)算平臺(tái)的基礎(chǔ)依托設(shè)施,正在云計(jì)算技術(shù)的推動(dòng)下,以前所未有的規(guī)模擴(kuò)張,然而,數(shù)據(jù)中心的高能耗、資源利用率低、對(duì)環(huán)境的污染等問(wèn)題,一直以來(lái)極大的制約著數(shù)據(jù)中心的發(fā)展。虛擬化技術(shù)的應(yīng)用,為解決上述問(wèn)題提供了一個(gè)很好的途徑,現(xiàn)今大多數(shù)降低數(shù)據(jù)中心能耗的研究,都是依托在虛擬化技術(shù)之上,虛擬化的服務(wù)器集群在節(jié)能方面有很多優(yōu)勢(shì),通過(guò)對(duì)計(jì)算資源的有效管理和調(diào)度,動(dòng)態(tài)調(diào)節(jié)服務(wù)器的狀態(tài),可以有效減小數(shù)據(jù)中心的能耗。本課題源于國(guó)家自然基金項(xiàng)目,希望通過(guò)現(xiàn)有的相對(duì)成熟的虛擬化技術(shù)構(gòu)建一種新型的數(shù)據(jù)中心功耗管理系統(tǒng),在保證服務(wù)質(zhì)量的前提下,通過(guò)資源整合和動(dòng)態(tài)調(diào)度,來(lái)降低數(shù)據(jù)中心的整體能耗。根據(jù)這一目標(biāo),本文在開(kāi)源云資源管理軟件OpenNEbula的基礎(chǔ)上,提出了CREMS云資源管理系統(tǒng)。 本論文主要的研究和創(chuàng)新包括以下幾點(diǎn): 1)構(gòu)建一種針對(duì)數(shù)據(jù)中心的新型資源管理系統(tǒng),該系統(tǒng)將數(shù)據(jù)中心的所有物理機(jī)和虛擬機(jī)進(jìn)行統(tǒng)一管理,系統(tǒng)中物理機(jī)和虛擬機(jī)的CPU利用率、內(nèi)存利用率狀態(tài)以及應(yīng)用程序的服務(wù)質(zhì)量等信息能夠被及時(shí)獲取,便于管理和監(jiān)控虛擬化集群。 2)提高數(shù)據(jù)中心資源分配的動(dòng)態(tài)性和合理性,本文提出的資源管理系統(tǒng)能夠收集到所有虛擬機(jī)和物理機(jī)的狀態(tài)信息,對(duì)整個(gè)數(shù)據(jù)中心的資源進(jìn)行調(diào)節(jié),在局部根據(jù)各個(gè)虛擬機(jī)的服務(wù)優(yōu)先級(jí)和性能指標(biāo)的不同,動(dòng)態(tài)調(diào)節(jié)虛擬機(jī)獲得相應(yīng)的資源,在保證服務(wù)質(zhì)量的前提下優(yōu)化局部物理資源分配,從整體上通過(guò)負(fù)載整合和動(dòng)態(tài)開(kāi)關(guān)閉物理機(jī),來(lái)提高資源利用率,降低整體功耗。 3)以網(wǎng)頁(yè)或者數(shù)據(jù)庫(kù)這些應(yīng)用服務(wù)器中的響應(yīng)時(shí)間作為描述應(yīng)用性能的指標(biāo),據(jù)此結(jié)合CREMS系統(tǒng)監(jiān)視到的動(dòng)態(tài)變化的虛擬機(jī)的資源利用率,CREMS做出合理的資源需求預(yù)測(cè),,根據(jù)預(yù)測(cè)負(fù)載來(lái)調(diào)整虛擬機(jī)的資源分配,通過(guò)虛擬機(jī)遷移、掛起等操作,實(shí)現(xiàn)調(diào)節(jié)資源分配和節(jié)能的目標(biāo)。 4)研究調(diào)度算法和策略,根據(jù)采集到的虛擬機(jī)和物理機(jī)的數(shù)據(jù)信息,在系統(tǒng)中設(shè)計(jì)高效的調(diào)度算法,動(dòng)態(tài)的調(diào)度每個(gè)虛擬機(jī)資源的分配,以及調(diào)整物理機(jī)的功耗狀態(tài),使得資源分配更加合理有效,保證系統(tǒng)運(yùn)行的穩(wěn)定性和持久性。 本論文首先驗(yàn)證了CPU利用率和功耗之間的關(guān)系,結(jié)果顯示服務(wù)器能源的絕大部分是被CPU消耗掉,然后在搭建的試驗(yàn)平臺(tái)上運(yùn)行基于OpenNEbula的數(shù)據(jù)中心資源管理系統(tǒng)CREMS,通過(guò)動(dòng)態(tài)調(diào)節(jié)負(fù)載,對(duì)CREMS系統(tǒng)的預(yù)測(cè)和調(diào)度功能進(jìn)行測(cè)試,實(shí)驗(yàn)結(jié)果表明,該系統(tǒng)能夠在保證服務(wù)質(zhì)量的前提下,減少大約12%的整體功耗。
[Abstract]:Data center, as the basic infrastructure of cloud computing platform, is expanding on an unprecedented scale driven by cloud computing technology. However, the data center has many problems, such as high energy consumption, low utilization of resources, pollution to the environment, etc. The development of data center has been greatly restricted. The application of virtualization technology provides a good way to solve the above problems. Nowadays, most of the research on reducing energy consumption of data center is based on virtualization technology. The virtualized server cluster has many advantages in energy saving. Through the efficient management and scheduling of computing resources and dynamically adjusting the state of the server, the energy consumption of the data center can be effectively reduced. This topic is originated from the National Natural Fund project, hoping to construct a new data center power management system through the existing relatively mature virtualization technology, under the premise of ensuring the quality of service, through the integration of resources and dynamic scheduling. To reduce the overall energy consumption of the data center. According to this goal, this paper puts forward the CREMS cloud resource management system based on the open source cloud resource management software OpenNEbula. The main research and innovations of this paper are as follows: 1) A new resource management system for data center is constructed, which manages all physical computers and virtual machines in the data center. Information such as CPU utilization, memory utilization status and application quality of service of the physical machine and virtual machine in the system can be obtained in time. It is easy to manage and monitor virtualized cluster. 2) improve the dynamic and rationality of resource allocation in data center. The resource management system proposed in this paper can collect the state information of all virtual machines and physical machines. Adjust the resources of the whole data center, dynamically adjust the virtual machine to obtain the corresponding resources according to the different service priority and performance index of each virtual machine, and optimize the local physical resource allocation under the premise of guaranteeing the quality of service. On the whole, through load integration and dynamic turn on and off the physical machine, to improve the resource utilization, reduce the overall power consumption. 3) take the response time in the application server such as web page or database as the index to describe the application performance. According to this, the resource utilization ratio of dynamic virtual machine monitored by CREMS system is combined with CREMS to make reasonable resource demand prediction. According to the forecast load, the resource allocation of virtual machine is adjusted, and the virtual machine is migrated, suspended and so on. To achieve the goals of regulating resource allocation and energy saving. 4) the scheduling algorithm and strategy are studied. According to the collected data of virtual machine and physical machine, an efficient scheduling algorithm is designed in the system. The resource allocation of each virtual machine is dynamically scheduled and the power state of the physical machine is adjusted to make the resource allocation more reasonable and effective to ensure the stability and persistence of the system. This paper first verifies the relationship between CPU utilization and power consumption. The results show that most of the server energy is consumed by CPU. Then run the data center resource management system based on OpenNEbula on the test platform, and test the prediction and scheduling function of the CREMS system by dynamically adjusting the load. The experimental results show that, The system can reduce the overall power consumption by about 12% under the premise of guaranteeing the quality of service.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP391.9;TP308

【共引文獻(xiàn)】

相關(guān)期刊論文 前3條

1 葉可江;吳朝暉;姜曉紅;何欽銘;;虛擬化云計(jì)算平臺(tái)的能耗管理[J];計(jì)算機(jī)學(xué)報(bào);2012年06期

2 韓兵;趙政文;張曉;;基于負(fù)載的能耗預(yù)測(cè)與溫度監(jiān)控系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[J];計(jì)算機(jī)與現(xiàn)代化;2011年09期

3 Xiaolong Xu;Jiaxing Wu;Geng Yang;Ruchuan Wang;;Low-power task scheduling algorithm for large-scale cloud data centers[J];Journal of Systems Engineering and Electronics;2013年05期

相關(guān)碩士學(xué)位論文 前4條

1 高逢騫;基于彈性虛擬機(jī)池的數(shù)據(jù)中心能耗管理框架優(yōu)化[D];上海交通大學(xué);2011年

2 潘鈺;云計(jì)算平臺(tái)中的能耗管理方法[D];南京郵電大學(xué);2013年

3 伍開(kāi)文;低能耗存儲(chǔ)系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];華中科技大學(xué);2012年

4 馬艾田;基于云計(jì)算的有限元分析仿真系統(tǒng)研究與實(shí)現(xiàn)[D];北京工業(yè)大學(xué);2013年

，

本文編號(hào)：2176574