本文關(guān)鍵詞：數(shù)據(jù)中心能效管理多目標(biāo)優(yōu)化策略研究　出處：《吉林財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 蜂群算法 模擬退火 DVFS 梯度下降 虛擬機(jī)整合

【摘要】：在大數(shù)據(jù)的時(shí)代背景下,隨著云計(jì)算技術(shù)在全世界的快速普及和發(fā)展,云數(shù)據(jù)中心的基礎(chǔ)設(shè)施和相關(guān)配套設(shè)施的數(shù)量也在高速地增長(zhǎng)。數(shù)據(jù)中心大量的計(jì)算密集型和數(shù)據(jù)密集型操作需要快速有效地響應(yīng),以保證數(shù)據(jù)中心的正常運(yùn)轉(zhuǎn)。海量服務(wù)器間的協(xié)同配合會(huì)產(chǎn)生大量的能源消耗,同時(shí),數(shù)據(jù)中心對(duì)于能源的利用率還待提高,這樣就對(duì)云數(shù)據(jù)中心運(yùn)營(yíng)成本造成巨大的浪費(fèi)。因此,云數(shù)據(jù)中心的能耗問(wèn)題亟待解決。當(dāng)前,云數(shù)據(jù)中心的能耗問(wèn)題得到了國(guó)內(nèi)外學(xué)者的廣泛關(guān)注,主要的解決策略分為硬件節(jié)能和軟件節(jié)能策略兩個(gè)方面,在軟件節(jié)能方面,其中的虛擬化技術(shù)已經(jīng)被證實(shí)是解決云數(shù)據(jù)中心能耗問(wèn)題的有效途徑,也是本文的關(guān)注重點(diǎn)。本文主要聚焦于虛擬機(jī)選擇和虛擬機(jī)分配過(guò)程。實(shí)時(shí)虛擬機(jī)(VM)整合是提高綠色數(shù)據(jù)中心能效管理水平的有效方法。目前,綠色數(shù)據(jù)中心的能耗評(píng)估模型是以CPU占用率為主要的影響因素。然而,由于GPU的密集處理產(chǎn)生巨大的能耗,原有的能耗評(píng)估模型并不適合于數(shù)據(jù)密集型計(jì)算。在本文中,我們提出了基于CPU和GPU利用率的一種新的能效管理評(píng)估模型,并提出兩種實(shí)時(shí)動(dòng)態(tài)遷移虛擬機(jī)的策略:一個(gè)應(yīng)用于虛擬機(jī)選擇,另一個(gè)應(yīng)用于虛擬機(jī)分配。一些研究人員已經(jīng)分別基于VM選擇策略或VM分配政策提出了自己的解決方案。然而,將虛擬機(jī)選擇和虛擬機(jī)分配這兩個(gè)策略集成在一起,將會(huì)得到一個(gè)更為高效的實(shí)時(shí)動(dòng)態(tài)遷移的虛擬機(jī)整合策略�；诖�,一個(gè)快速的基于人工蜂群算法(ABC)的實(shí)時(shí)VM整合策略被提出,并結(jié)合適合數(shù)據(jù)密集型計(jì)算的能耗評(píng)估模型共同組成DataABC策略。DataABC采用了人工蜂群算法的思想,從而得到一個(gè)快速并且具有全局優(yōu)化特點(diǎn)的虛擬機(jī)遷移策略。與其他經(jīng)典的虛擬機(jī)整合策略相比,DataABC的總能耗下降明顯。在虛擬機(jī)分配過(guò)程中,傳統(tǒng)的分配策略存在著分配速度難以滿足數(shù)據(jù)密集型作業(yè)要求的特點(diǎn),以及容易陷入局部最優(yōu)等現(xiàn)象。因此,為了滿足數(shù)據(jù)密集型作業(yè)對(duì)于響應(yīng)速度的需要,本文引入梯度下降算法,加快人工蜂群算法搜尋局部最優(yōu)解的速度,同時(shí)引入模擬退火算法,加強(qiáng)人工蜂群算法搜尋全局近似最優(yōu)解的能力,使空閑節(jié)點(diǎn)關(guān)閉或者休眠來(lái)達(dá)到節(jié)能的目的,從而減少了能源消耗,提高了資源使用效率,減少了數(shù)據(jù)中心的運(yùn)營(yíng)成本。研究者提出了多種節(jié)能策略,例如開/關(guān)策略,虛擬機(jī)整合策略,DVFS策略等,但是,每種策略都有其實(shí)現(xiàn)條件和自身特點(diǎn),將多種節(jié)能策略集成,將更好的實(shí)現(xiàn)數(shù)據(jù)中心節(jié)能目標(biāo),實(shí)現(xiàn)數(shù)據(jù)中心的可持續(xù)發(fā)展。
[Abstract]:Under the background of big data era, with the rapid spread and development of cloud computing technology in the world. The number of cloud data centers infrastructure and related supporting facilities is also growing at a high speed. A large number of computation-intensive and data-intensive operations in data centers need to respond quickly and effectively. In order to ensure the normal operation of the data center. The cooperation between the massive servers will produce a large amount of energy consumption, at the same time, the data center for energy utilization still needs to be improved. Therefore, the energy consumption of cloud data center needs to be solved urgently. At present, the energy consumption of cloud data center has been widely concerned by scholars at home and abroad. The main solutions are hardware energy saving and software energy saving. In software energy saving, the virtualization technology has been proved to be an effective way to solve the problem of cloud data center energy consumption. This paper focuses on the selection and allocation of virtual machines. The integration of real-time virtual machines (VMs) is an effective way to improve the energy efficiency management level of green data centers. The energy consumption assessment model of green data center is based on the CPU occupancy rate. However, because of the intensive processing of GPU, the energy consumption is huge. The original energy consumption evaluation model is not suitable for data-intensive computing. In this paper, we propose a new energy efficiency management evaluation model based on CPU and GPU utilization. Two strategies for real-time dynamic migration of virtual machines are proposed: one is applied to virtual machine selection. Another application is virtual machine allocation. Some researchers have proposed their own solutions based on VM selection strategy or VM allocation policy respectively. Integrating the two strategies of virtual machine selection and virtual machine allocation will result in a more efficient virtual machine integration strategy for real-time dynamic migration. A fast real-time VM integration strategy based on artificial bee colony algorithm (ABC) is proposed. Combined with the energy consumption evaluation model suitable for data-intensive computing, DataABC strategy. DataABC adopts the idea of artificial bee colony algorithm. Thus, a fast and globally optimized virtual machine migration strategy is obtained. Compared with other classical virtual machine integration strategies, the total energy consumption of DataABC is significantly reduced, and in the process of virtual machine allocation. The traditional allocation strategy has the characteristics that the allocation speed is difficult to meet the requirements of data-intensive jobs, and it is easy to fall into the local optimum. Therefore, in order to meet the needs of the response speed of data-intensive jobs. In this paper, gradient descent algorithm is introduced to speed up the search speed of artificial bee colony algorithm, and simulated annealing algorithm is introduced to enhance the ability of artificial bee colony algorithm to search global approximate optimal solution. The idle nodes are closed or dormant to achieve the purpose of energy saving, thus reducing energy consumption, improving the efficiency of resource use, and reducing the operating costs of the data center. Researchers have proposed a variety of energy-saving strategies. For example, on / off strategy, virtual machine integration strategy, DVFS strategy, etc., however, each strategy has its own implementation conditions and its own characteristics, the integration of a variety of energy-saving strategies will better achieve the data center energy-saving goals. To realize the sustainable development of data center.
【學(xué)位授予單位】：吉林財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP308

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