【摘要】：云計(jì)算作為一種新型商業(yè)模式，推動(dòng)了工業(yè)生產(chǎn)等各方面的發(fā)展。云計(jì)算以一種透明的方式給用戶提供各種服務(wù)，用戶不需要了解云計(jì)算平臺的技術(shù)實(shí)現(xiàn)便可以根據(jù)自己的需求獲取多元化的服務(wù)。如何將云計(jì)算中的虛擬資源有效的分配給各類用戶是一個(gè)關(guān)鍵技術(shù)問題，一個(gè)好的資源分配策略可以在滿足用戶需求的同時(shí)，提高系統(tǒng)的運(yùn)行效率，因此，研究云計(jì)算環(huán)境下的任務(wù)調(diào)度策略具有重要的現(xiàn)實(shí)理論意義。 本文深入的剖析了云計(jì)算的關(guān)鍵技術(shù)，，重點(diǎn)的研究了云計(jì)算中的任務(wù)匹配算法，針對現(xiàn)有調(diào)度算法中存在的一些缺陷，本文提出了一種優(yōu)化的蟻群模型來解決不同任務(wù)模型的調(diào)度問題。本文首先用蟻群算法來解決獨(dú)立任務(wù)系統(tǒng)的調(diào)度問題，然后用蟻群搜尋DAG（有向無環(huán)圖）任務(wù)調(diào)度問題的優(yōu)化解。以下是本文的主要工作： （1）在獨(dú)立任務(wù)調(diào)度系統(tǒng)中，任務(wù)之間彼此無關(guān)聯(lián)，將任務(wù)分配給虛擬機(jī)的過程可以看作一個(gè)多目標(biāo)優(yōu)化問題，蟻群能夠基于一種正反饋的機(jī)制不斷迭代來全局搜索問題的優(yōu)化解。針對蟻群的這些特性本文提出了一種用于解決任務(wù)分配的蟻群模型，通過構(gòu)建智能的“人工蟻群”，使得蟻群能夠快速的收斂，將任務(wù)分配到合理的虛擬機(jī)。本文在一個(gè)高性能的云計(jì)算仿真平臺CloudSim上進(jìn)行相關(guān)實(shí)驗(yàn)，對其中的云計(jì)算任務(wù)調(diào)度模塊進(jìn)行了擴(kuò)展，并將蟻群算法與FCFS（先來先服務(wù)）和貪心調(diào)度策略進(jìn)行比較。 （2）在實(shí)際的情況下，任務(wù)之間會(huì)存在一些關(guān)聯(lián)，本文用DAG工作流的模型來描述這個(gè)復(fù)雜的任務(wù)調(diào)度系統(tǒng)，并對現(xiàn)有的一些DAG調(diào)度算法進(jìn)行了研究。為了解決此類任務(wù)的調(diào)度問題，本文先提出了一種基于優(yōu)先級調(diào)度算法，通過對任務(wù)賦予優(yōu)先級的方式來動(dòng)態(tài)分配虛擬機(jī)；之后，本文在此算法的基礎(chǔ)上提出了一種融合蟻群和優(yōu)先級調(diào)度的綜合性算法，該算法結(jié)合了蟻群和優(yōu)先級調(diào)度算法的優(yōu)勢，能夠在有限時(shí)間內(nèi)搜尋問題的優(yōu)化解。最后，在CloudSim平臺通過仿真實(shí)驗(yàn)對本文提出的蟻群算法的有效性進(jìn)行了分析。
[Abstract]:Cloud computing as a new business model has promoted the development of industrial production and other aspects. Cloud computing provides users with a variety of services in a transparent manner. Users do not need to know the technological implementation of cloud computing platform to obtain a variety of services according to their own needs. How to allocate virtual resources effectively to all kinds of users in cloud computing is a key technical problem. A good resource allocation strategy can improve the efficiency of the system while meeting the needs of users. It is of great theoretical significance to study the task scheduling strategy in cloud computing environment. In this paper, the key technologies of cloud computing are deeply analyzed, and the task matching algorithm in cloud computing is studied, aiming at some defects in the existing scheduling algorithms. In this paper, an optimized ant colony model is proposed to solve the scheduling problem of different task models. In this paper, ant colony algorithm is first used to solve the scheduling problem of independent task system, and then ant colony is used to search the optimal solution of DAG (directed acyclic graph) task scheduling problem. The following is the main work of this paper: (1) in the independent task scheduling system, the tasks are not related to each other, the process of assigning tasks to the virtual machine can be regarded as a multi-objective optimization problem. Ant colonies can iterate through a positive feedback mechanism to optimize the global search problem. According to these characteristics of ant colony, this paper proposes an ant colony model to solve the problem of task allocation. By constructing an intelligent "artificial ant colony", the ant colony can converge quickly and assign tasks to a reasonable virtual machine. In this paper, we do some experiments on a high-performance cloud computing simulation platform CloudSim, and extend the task scheduling module of cloud computing. The ant colony algorithm is compared with FCFS (first come, first served) and greedy scheduling strategy. (2) in the actual situation, there are some relationships between tasks. This paper describes the complex task scheduling system with the model of DAG workflow. Some existing DAG scheduling algorithms are studied. In order to solve the scheduling problem of this kind of tasks, a priority-based scheduling algorithm is proposed in this paper, which allocates the virtual machine dynamically by assigning priority to the task. Based on this algorithm, a comprehensive algorithm combining ant colony and priority scheduling is proposed in this paper. This algorithm combines the advantages of ant colony and priority scheduling algorithm, and can search the optimal solution of the problem in limited time. Finally, the effectiveness of the proposed ant colony algorithm is analyzed by simulation experiments on CloudSim platform.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.01

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