本文選題：云數(shù)據(jù)中心 + 能耗��； 參考：《電子科技大學(xué)》2016年碩士論文

【摘要】：隨著云計(jì)算的應(yīng)用和發(fā)展,數(shù)據(jù)中心規(guī)模的擴(kuò)大,隨之而來(lái)的是數(shù)據(jù)中心的能耗、資源利用率等問(wèn)題日益突出。因此,設(shè)計(jì)高效的資源分配策略,提高數(shù)據(jù)中心的資源利用率,降低數(shù)據(jù)中心的能耗成為一個(gè)研究的熱點(diǎn)。而能耗作為資源調(diào)度的指標(biāo),首先會(huì)受到服務(wù)器性能的影響,其次也會(huì)受到用戶應(yīng)用需求的影響。在現(xiàn)代數(shù)據(jù)中心中,虛擬化技術(shù)被廣泛應(yīng)用,但因?yàn)樘摂M機(jī)資源分配被證明是NP難問(wèn)題,又因?yàn)楦鞣N應(yīng)用業(yè)務(wù)需求不一樣以及物理環(huán)境的多樣性都給能耗計(jì)算研究帶來(lái)了挑戰(zhàn),尤其在全球能源緊缺和溫室效應(yīng)逐年增強(qiáng)的背景下,節(jié)能調(diào)度成為值得深入研究的課題。本論文從現(xiàn)有的資源監(jiān)控系統(tǒng)和能耗模型兩方面對(duì)能耗問(wèn)題進(jìn)行研究和分析,通過(guò)在每個(gè)服務(wù)器上放置一個(gè)節(jié)點(diǎn)代理來(lái)獲取其資源使用情況。然后根據(jù)工程實(shí)驗(yàn)得出服務(wù)器在不同CPU利用率下的功耗值,計(jì)算出能耗模型的參數(shù)。根據(jù)現(xiàn)有的理論研究了虛擬機(jī)遷移準(zhǔn)則和觸發(fā)機(jī)制以及待遷移服務(wù)器的虛擬機(jī)選擇策略。然后比較三種選擇策略機(jī)制,哪一種更能適應(yīng)實(shí)驗(yàn)室這種小規(guī)模的云數(shù)據(jù)中心,得出了最少選擇策略相比較而言最能降低其能耗。本論文針對(duì)多個(gè)虛擬機(jī)的放置問(wèn)題,建立了以負(fù)載均衡為目標(biāo)的優(yōu)化函數(shù),該優(yōu)化函數(shù)的最優(yōu)解將使得所有物理服務(wù)器的平均利用率保持在一個(gè)期望的最優(yōu)值附近,同時(shí)最優(yōu)解也描述了最優(yōu)的虛擬機(jī)放置策略。為了求解該優(yōu)化模型,設(shè)計(jì)了相應(yīng)的遺傳算法,染色體采用二進(jìn)制編碼形式,并采用了隨機(jī)選擇、兩點(diǎn)交叉、精英原則等方法來(lái)實(shí)現(xiàn)遺傳算法。然后和工程項(xiàng)目中經(jīng)常用到的裝箱算法和隨機(jī)放置算法得到的放置序列求得適應(yīng)度函數(shù)值進(jìn)行比較,結(jié)果證明遺傳算法求解的最優(yōu)放置策略可以使得服務(wù)器CPU利用率最接近于期望的最優(yōu)值,從而達(dá)到最好的負(fù)載均衡效果,負(fù)載均衡同時(shí)也意味著可以有效減少所需服務(wù)器的數(shù)量,從而達(dá)到降低能耗的作用。最后研究了云計(jì)算系統(tǒng)下的節(jié)能高效調(diào)度機(jī)制,建立了云計(jì)算系統(tǒng)的性能評(píng)估模型和能耗評(píng)估模型,并進(jìn)一步提出了基于利潤(rùn)函數(shù)的性能-能耗聯(lián)合優(yōu)化函數(shù),然后設(shè)計(jì)了遺傳算法求解最優(yōu)的請(qǐng)求分發(fā)策略和資源分配策略,該最優(yōu)解意味著一種性能和能耗均衡的調(diào)度策略,將比單指標(biāo)優(yōu)化更加全面合理。通過(guò)實(shí)驗(yàn)分析詳細(xì)描述了系統(tǒng)理論評(píng)估模型的分析方法、節(jié)能高效調(diào)度機(jī)制的運(yùn)行過(guò)程以及在系統(tǒng)利潤(rùn)上取得的優(yōu)化效果。
[Abstract]:With the application and development of cloud computing, the expansion of data center scale, followed by data center energy consumption, resource utilization and other issues become increasingly prominent. Therefore, designing efficient resource allocation strategy, improving resource utilization of data center and reducing energy consumption of data center has become a hot research topic. As an indicator of resource scheduling, energy consumption is affected by the performance of the server first, and then by the application requirements of the user. In modern data centers, virtualization technology is widely used, but because the allocation of virtual machine resources is proved to be NP-hard problem, and because of the different business requirements of various applications and the diversity of physical environment, it brings challenges to the research of energy consumption computing. Especially in the background of global energy shortage and Greenhouse Effect increasing year by year, energy saving scheduling becomes a subject worthy of further study. In this paper, the problem of energy consumption is studied and analyzed from the two aspects of resource monitoring system and energy consumption model, and the resource usage is obtained by placing a node agent on each server. Then, according to the engineering experiment, the power consumption of the server under different CPU utilization is obtained, and the parameters of the energy consumption model are calculated. According to the existing theories, the migration criteria and trigger mechanism of virtual machine and the virtual machine selection strategy of server to be migrated are studied. Then compared with the three selection strategy mechanisms which is more suitable for the small scale cloud data center such as the laboratory the least choice strategy is the most effective to reduce the energy consumption compared with the least choice strategy. In this paper, an optimization function aiming at load balancing is established for the placement of multiple virtual machines. The optimal solution of the optimization function will keep the average utilization of all physical servers near a desired optimal value. At the same time, the optimal solution also describes the optimal virtual machine placement strategy. In order to solve the optimization model, the corresponding genetic algorithm is designed, the chromosome is coded in binary form, and the methods of random selection, two-point crossover and elitist principle are used to realize the genetic algorithm. Then compared with the packing algorithm often used in engineering projects and the placement sequence obtained by the random placement algorithm, the fitness function values are obtained. The results show that the optimal placement strategy solved by the genetic algorithm can make the server CPU utilization close to the expected optimal value, so as to achieve the best load balancing effect. Load balancing also means reducing the number of servers needed to reduce energy consumption. Finally, the energy-saving and efficient scheduling mechanism in cloud computing system is studied, the performance evaluation model and energy consumption evaluation model of cloud computing system are established, and the joint optimization function of performance-energy consumption based on profit function is proposed. Then genetic algorithm is designed to solve the optimal request distribution strategy and resource allocation strategy. The optimal solution means a scheduling strategy with balanced performance and energy consumption, which is more comprehensive and reasonable than the single index optimization. Through the experimental analysis, the analysis method of the system theory evaluation model, the running process of the energy saving and efficient scheduling mechanism and the optimization effect of the system profit are described in detail.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18;TP308

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本文編號(hào)：1902671