【摘要】：隨著計(jì)算機(jī)技術(shù)的革新與互聯(lián)網(wǎng)的飛速發(fā)展，云計(jì)算應(yīng)運(yùn)而生。云計(jì)算是一種新興的商業(yè)計(jì)算模式，它利用成熟的虛擬化技術(shù)將大量的基礎(chǔ)設(shè)施資源集中起來(lái)，實(shí)現(xiàn)了數(shù)據(jù)中心資源的按需服務(wù)。在云計(jì)算中，由于資源具有動(dòng)態(tài)性、異構(gòu)性、大規(guī)模性等特點(diǎn)，如何根據(jù)云計(jì)算的實(shí)際特點(diǎn)制定合適的資源分配策略是目前急需解決的問(wèn)題。智能優(yōu)化算法由于其高度并行、自組織、自適應(yīng)等特性，已經(jīng)被廣泛用于解決云計(jì)算的資源分配問(wèn)題，本文通過(guò)研究云計(jì)算下的資源分配問(wèn)題，對(duì)現(xiàn)有的資源分配算法存在的問(wèn)題進(jìn)行了分析，主要進(jìn)行了以下方面的研究工作： ①提出一種粒子群結(jié)合遺傳算法（PSO-GA）的云計(jì)算資源分配算法。傳統(tǒng)的的粒子群算法、遺傳算法在云計(jì)算資源分配過(guò)程中均容易陷入早熟收斂的缺陷，不能很好解決云計(jì)算下的資源分配。針對(duì)這一問(wèn)題，提出PSO-GA資源分配算法，該算法在遺傳算法的基礎(chǔ)上通過(guò)引入種群分割、種群覆蓋的概念，并且將粒子群算法中的變異算子應(yīng)用到PSO-GA算法的變異過(guò)程中。實(shí)驗(yàn)表明，PSO-GA算法能夠有效解決單一的遺傳算法和粒子群算法的早熟收斂的缺陷，提高最優(yōu)解收斂速度和算法執(zhí)行效率。 ②提出一種改進(jìn)型人工魚(yú)群算法（IAFA）的云計(jì)算資源分配算法。在云計(jì)算資源分配過(guò)程中，在種群規(guī)模較大的情況下，PSO-GA算法收斂速度較慢，不能快速得到全局最優(yōu)解。為了解決這一問(wèn)題，本文提出一種改進(jìn)型人工魚(yú)群算法（IAFA），在原來(lái)行為的基礎(chǔ)上淘汰了隨機(jī)行為，增加了跳躍行為，促使了陷入局部最優(yōu)的人工魚(yú)跳出局部極值繼續(xù)搜索全局最優(yōu)；引入生存周期和生存指數(shù)的概念，，節(jié)約了儲(chǔ)存空間，提高了算法的效率。實(shí)驗(yàn)表明，IAFA算法能夠在種群規(guī)模較大的情況下快速收斂并得到全局最優(yōu)解。 ③擴(kuò)展了云計(jì)算仿真模擬平臺(tái)CloudSim，對(duì)上文提出的算法進(jìn)行仿真模擬。本文分析和研究了CloudSim的資源分配機(jī)制，對(duì)CloudSim平臺(tái)進(jìn)行重編譯，在CloudSim上實(shí)現(xiàn)了PSO-GA、IAFA等算法的仿真程序，并對(duì)算法進(jìn)行了模擬驗(yàn)證和對(duì)比分析，實(shí)驗(yàn)證明了上述兩種改進(jìn)算法的有效性。
[Abstract]:With the innovation of computer technology and the rapid development of the Internet, cloud computing emerges as the times require. Cloud computing is a new business computing model, which uses mature virtualization technology to centralize a large number of infrastructure resources and realize on-demand service of data center resources. In cloud computing, due to the dynamic, heterogeneous, large-scale characteristics of resources, how to formulate appropriate resource allocation strategy according to the actual characteristics of cloud computing is an urgent problem to be solved at present. Intelligent optimization algorithm has been widely used to solve the resource allocation problem of cloud computing because of its highly parallel, self-organizing, adaptive and other characteristics. This paper studies the resource allocation problem in cloud computing. The existing problems of resource allocation algorithms are analyzed, and the main work is as follows: (1) A cloud computing resource allocation algorithm based on particle swarm optimization (PSO-GA) is proposed. Traditional particle swarm optimization (PSO) and genetic algorithm (GA) are prone to fall into premature convergence in the process of resource allocation in cloud computing, and can not solve the problem of resource allocation in cloud computing. In order to solve this problem, the PSO-GA resource allocation algorithm is proposed. Based on the genetic algorithm, the concept of population segmentation and population coverage is introduced, and the mutation operator in particle swarm optimization is applied to the mutation process of PSO-GA algorithm. Experiments show that PSO-GA algorithm can effectively solve the shortcomings of premature convergence of single genetic algorithm and particle swarm optimization algorithm, improve the convergence rate of the optimal solution and the efficiency of the algorithm. In this paper, an improved artificial fish swarm algorithm (IAFA) for cloud computing resource allocation is proposed. In the process of resource allocation in cloud computing, when the population size is large, the convergence rate of PSO-GA algorithm is slow, and the global optimal solution can not be obtained quickly. In order to solve this problem, an improved artificial fish swarm algorithm (IAFA),) is proposed in this paper, which eliminates random behavior and increases jump behavior on the basis of the original behavior. The artificial fish trapped in the local optimum jump out of the local extremum and continue to search for the global optimal; By introducing the concepts of life cycle and survival index, the storage space is saved and the efficiency of the algorithm is improved. The experimental results show that the IAFA algorithm can converge rapidly and obtain the global optimal solution when the population size is large. 3 extend the cloud computing simulation platform CloudSim, to simulate the algorithm proposed above. This paper analyzes and studies the resource allocation mechanism of CloudSim, recompiles the CloudSim platform, implements the simulation program of PSO-GA,IAFA and other algorithms on CloudSim, and makes simulation verification and comparative analysis of the algorithms. Experimental results show the effectiveness of the two improved algorithms.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;TP393.09

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