本文選題：云計算 + 任務(wù)調(diào)度　； 參考：《廣西師范大學(xué)》2017年碩士論文

【摘要】：云計算使用虛擬化技術(shù)將各種計算、存儲、網(wǎng)絡(luò)寬帶等實體資源整合成一個共享的云服務(wù)資源池,再通過任務(wù)調(diào)度算法為用戶提交的任務(wù)分配資源。任務(wù)調(diào)度算法是云計算中最重要的技術(shù)之一,效率優(yōu)良的任務(wù)調(diào)度算法能夠保證云計算平臺穩(wěn)定高效地運行,可以縮短任務(wù)的完成時間,減少用戶使用云服務(wù)的成本,并且能夠保證云計算服務(wù)提供商的經(jīng)濟收益。云計算環(huán)境非常復(fù)雜,傳統(tǒng)的調(diào)度算法已經(jīng)無法滿足云計算任務(wù)調(diào)度的需求。新興的智能調(diào)度算法在一定程度上提升了任務(wù)調(diào)度的性能,但是還不夠成熟和穩(wěn)定,在收斂精度和穩(wěn)定性等方面還存在缺陷。本文的研究動機是通過分析云計算和任務(wù)調(diào)度算法的關(guān)鍵技術(shù)和特點,了解和掌握現(xiàn)有任務(wù)調(diào)度的模型和算法,然后設(shè)計出性能更加良好的調(diào)度算法來解決云計算任務(wù)調(diào)度面臨的問題。主要工作如下:(1)介紹云計算、云計算任務(wù)調(diào)度、云計算任務(wù)調(diào)度算法的基本理論之后,接著詳細分析粒子群算法和帝國競爭算法的基本原理、數(shù)學(xué)模型,分析這兩種算法的優(yōu)缺點,并對這兩種算法的發(fā)展和已有的改進進行總結(jié)。(2)通過對比分析粒子群和帝國競爭算法的特點,針對帝國競爭算法中殖民地無自主學(xué)習(xí)能力、不能記錄歷史最優(yōu)信息的缺點以及粒子群算法收斂過快的缺點,提出將粒子群和帝國競爭混合的算法,使具有生物啟發(fā)性的粒子群算法和具有社會啟發(fā)性的帝國競爭算法融合在一起,達到優(yōu)勢互補的效果;針對帝國算法中殖民地缺乏有效的控制機制調(diào)整移動距離和角度大小的缺點,融入粒子群算法的思想使殖民地具有粒子的特性之后,對慣性權(quán)重進行自適應(yīng)調(diào)整。(3)將粒子群和帝國競爭的混合算法應(yīng)用于云計算任務(wù)調(diào)度,設(shè)計編碼形式和適應(yīng)度函數(shù),然后在云計算仿真平臺Cloudsim上進行實驗,并將實驗結(jié)果和改進前的算法進行分析對比。實驗結(jié)果表明本文算法具有更加良好的性能。
[Abstract]:Cloud computing uses virtualization technology to integrate various computing, storage, network broadband and other physical resources into a shared pool of cloud services resources, and then assign resources to tasks submitted by users through task scheduling algorithms. Task scheduling algorithm is one of the most important technologies in cloud computing. The efficient task scheduling algorithm can ensure the cloud computing platform to run stably and efficiently, can shorten the completion time of tasks and reduce the cost of using cloud services. And can guarantee the economic benefit of cloud computing service provider. Cloud computing environment is very complex, the traditional scheduling algorithm can not meet the needs of cloud computing task scheduling. The new intelligent scheduling algorithm improves the performance of task scheduling to a certain extent, but it is not mature and stable, and there are still some defects in convergence accuracy and stability. The motivation of this paper is to understand and master the existing task scheduling models and algorithms by analyzing the key technologies and characteristics of cloud computing and task scheduling algorithms. Then we design a better scheduling algorithm to solve the problem of cloud computing task scheduling. The main work is as follows: (1) after introducing the basic theory of cloud computing and cloud computing task scheduling algorithm, the basic principle and mathematical model of particle swarm optimization algorithm and imperial competition algorithm are analyzed in detail. The advantages and disadvantages of these two algorithms are analyzed, and the development and improvement of these two algorithms are summarized. (2) by comparing and analyzing the characteristics of particle swarm optimization and imperial competition algorithm, the colony has no autonomous learning ability in imperial competition algorithm. Because the historical optimal information can not be recorded and the convergence of particle swarm optimization algorithm is too fast, a hybrid algorithm of particle swarm optimization and imperial competition is proposed. The particle swarm optimization algorithm with biological enlightenment and the imperial competition algorithm with social enlightenment are combined to achieve the effect of complementary advantages; In view of the lack of effective control mechanism to adjust the distance and angle of the colony in the Imperial algorithm, the particle swarm optimization (PSO) algorithm is used to make the colony have the characteristics of particles. The inertia weight is adjusted adaptively. (3) the hybrid algorithm of particle swarm optimization and imperial competition is applied to the task scheduling of cloud computing, the coding form and fitness function are designed, and then the experiment is carried out on Cloudsim, a cloud computing simulation platform. The experimental results are compared with the improved algorithm. Experimental results show that the proposed algorithm has better performance.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP3

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本文編號：2084259