本文選題：云數(shù)據(jù)中心 + 資源利用率�。� 參考：《河南科技大學》2014年碩士論文

【摘要】：近年來，隨著云計算技術的發(fā)展，越來越多的資源集中在數(shù)據(jù)中心，給數(shù)據(jù)中心能耗的高效管理帶來了挑戰(zhàn)。數(shù)據(jù)中心的高能耗不僅造成電能的浪費、系統(tǒng)運行的不穩(wěn)定，同時也對環(huán)境造成不良影響。因此，在利用云數(shù)據(jù)中心資源的同時，也應該考慮數(shù)據(jù)中心的高能耗問題。虛擬化作為云計算的基礎，在數(shù)據(jù)中心資源管理方面發(fā)揮著重要作用。如何利用虛擬化技術提高數(shù)據(jù)中心的資源利用率，降低能耗，讓云數(shù)據(jù)中心更加節(jié)能成為近年來一個研究的熱點。 論文從能耗測量、能耗建模等方面對云數(shù)據(jù)中心的能耗問題進行了系統(tǒng)分析和研究，針對虛擬機能耗測量難的問題，提出了云數(shù)據(jù)中心中虛擬機能耗的監(jiān)控和測量方法，建立了虛擬機系統(tǒng)以及虛擬機遷移時的能耗模型。常用虛擬機遷移方法使用啟發(fā)式算法分配虛擬機，其求解結果易陷入局部最優(yōu)解，論文在研究遺傳算法的基礎上，給出了一種基于遺傳算法的虛擬機遷移算法(Migratingalgorithm based on Genetic Algorithm，MGA)。該算法利用遺傳進化的全局搜索原理實現(xiàn)虛擬機到目標節(jié)點的映射，并通過將虛擬機和目標節(jié)點的資源利用率作為輸入因子引入計算過程，對遺傳算法中的目標函數(shù)進行改進，在滿足服務級別協(xié)議的條件下，使目標節(jié)點的使用個數(shù)及遷移次數(shù)最少，從而實現(xiàn)數(shù)據(jù)中心的節(jié)能。 在Cloudsim仿真平臺中對基于遺傳算法的虛擬機遷移算法進行了仿真，，并使用能耗模型對基于遺傳算法的虛擬機遷移算法實現(xiàn)過程中的能耗進行了測量和分析。該算法與單閾值方法(ST)和雙閾值方法(DT)相比，不僅提高了算法的搜索速度，并且減少了遷移次數(shù)和物理節(jié)點的使用數(shù)量，提高了數(shù)據(jù)中心的資源利用率，降低了能耗。
[Abstract]:In recent years, with the development of cloud computing technology, more and more resources are concentrated in the data center, which brings challenges to the efficient management of data center energy consumption. The high energy consumption of the data center not only causes the waste of electric energy and the unstable operation of the system, but also has a bad effect on the environment. Therefore, the high energy consumption of the data center should be taken into account when using the cloud data center resources. As the foundation of cloud computing, virtualization plays an important role in data center resource management. How to use virtualization technology to improve resource utilization, reduce energy consumption and make cloud data center more energy efficient has become a hot topic in recent years. In this paper, the energy consumption of cloud data center is systematically analyzed and studied from the aspects of energy consumption measurement and energy modeling. Aiming at the difficulty of virtual machine energy consumption measurement, the monitoring and measuring method of virtual machine energy consumption in cloud data center is put forward. The virtual machine system and the energy consumption model of virtual machine migration are established. Heuristic algorithm is used to allocate virtual machine in common virtual machine migration method, and the solution result is easy to fall into local optimal solution. Based on the research of genetic algorithm, a migration algorithm of virtual machine based on Genetic algorithm based on genetic algorithm is presented in this paper. The algorithm uses the global search principle of genetic evolution to realize the mapping of virtual machine to target node. By introducing the resource utilization ratio of virtual machine and target node into the calculation process, the objective function in genetic algorithm is improved. Under the condition of satisfying the service level agreement, the number of target nodes and the number of migrations are minimized, so that the energy saving of the data center can be realized. The virtual machine migration algorithm based on genetic algorithm is simulated in Cloudsim simulation platform, and the energy consumption of virtual machine migration algorithm based on genetic algorithm is measured and analyzed by energy consumption model. Compared with single threshold method (STT) and double threshold method (DTT), this algorithm not only improves the search speed, but also reduces the number of migrations and the number of physical nodes, improves the resource utilization of the data center and reduces the energy consumption.
【學位授予單位】：河南科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP302;TP308

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本文編號：1785235