本文選題：云計算 + 架構(gòu)�。� 參考：《蘭州理工大學(xué)》2014年碩士論文

【摘要】：隨著網(wǎng)格計算、普適計算以及計算機通訊技術(shù)的快速發(fā)展,人們越來越希望能把資源、軟件及應(yīng)用更好地整合在一起,并以服務(wù)的形式向外提供給用戶,因此云計算應(yīng)運而生。為了實現(xiàn)資源和服務(wù)的整合,需要一個更為通用和面向服務(wù)的云架構(gòu)；同時由于云計算環(huán)境的異構(gòu)性、分布式、自治性以及服務(wù)的多樣性特征,對云平臺調(diào)度機制也提出了更高的要求,因此關(guān)于云架構(gòu)及其調(diào)度機制的研究得到了業(yè)界越來越多的關(guān)注。 本文首先對云計算的基本理論、云架構(gòu)和云任務(wù)調(diào)度機制進(jìn)行了深入的分析和研究,并總結(jié)了當(dāng)前云架構(gòu)及調(diào)度機制存在的問題。針對這些問題并結(jié)合云計算的特點,主要做了以下兩個方面的改進(jìn)和研究： (1)針對云計算環(huán)境中的大型復(fù)雜任務(wù)請求提出了改進(jìn)的MapReduce模型。改進(jìn)后的模型在Map過程開始之前,先把大型復(fù)雜任務(wù)請求轉(zhuǎn)化為DAG圖,然后將其轉(zhuǎn)化為最小生成樹,簡化了復(fù)雜任務(wù)的執(zhí)行過程,減少了任務(wù)執(zhí)行時間,有效地提高了任務(wù)的執(zhí)行效率。改進(jìn)后的模型增強了Hadoop架構(gòu)的性能,擴大了Hadoop架構(gòu)的應(yīng)用領(lǐng)域,使得Hadoop架構(gòu)不僅可以應(yīng)用于交互式應(yīng)用,而且還可以應(yīng)用于科學(xué)計算領(lǐng)域。 (2)針對在云計算環(huán)境中利用基本蟻群算法進(jìn)行任務(wù)調(diào)度時存在的缺點,對蟻群算法進(jìn)行了改進(jìn),提出了基于兄弟螞蟻和剩余生命信息素的BBPA算法,然后在CloudSim平臺上對BBPA算法進(jìn)行了仿真模擬,仿真結(jié)果表明,改進(jìn)后的蟻群算法在云計算環(huán)境中具有更好的任務(wù)搜索效率和任務(wù)執(zhí)行效率 最后,對該論文中提出的調(diào)度模型和調(diào)度算法的創(chuàng)新性方面進(jìn)行了總結(jié),并對今后云計算的發(fā)展和任務(wù)調(diào)度的研究方向進(jìn)行了展望。
[Abstract]:With the rapid development of grid computing, pervasive computing and computer communication technology, more and more people hope to integrate resources, software and applications together better and provide them to users in the form of services. Therefore, cloud computing emerges as the times require. In order to integrate resources and services, we need a more general and service-oriented cloud architecture, and because of the heterogeneity, distribution, autonomy and diversity of services in the cloud computing environment, Therefore, more and more attention has been paid to the research of cloud architecture and its scheduling mechanism. In this paper, the basic theory of cloud computing, cloud architecture and cloud task scheduling mechanism are analyzed and studied deeply, and the existing problems of cloud architecture and scheduling mechanism are summarized. In view of these problems and combined with the characteristics of cloud computing, we mainly do the following two aspects of improvement and research: 1) an improved MapReduce model is proposed for large complex task requests in cloud computing environment. Before the process of Map, the improved model transforms the large complex task request into DAG graph, and then transforms it into the minimum spanning tree, which simplifies the execution process of complex task and reduces the task execution time. The efficiency of task execution is improved effectively. The improved model enhances the performance of Hadoop architecture and expands the application field of Hadoop architecture. Hadoop architecture can be used not only in interactive applications but also in scientific computing. 2) aiming at the shortcomings of using basic ant colony algorithm to schedule tasks in cloud computing environment, this paper improves the ant colony algorithm, and proposes a BBPA algorithm based on sibling ants and residual life pheromones. Then the BBPA algorithm is simulated on CloudSim platform. The simulation results show that the improved ant colony algorithm has better task search efficiency and task execution efficiency in cloud computing environment. Finally, the innovative aspects of scheduling model and scheduling algorithm proposed in this paper are summarized, and the future development of cloud computing and the research direction of task scheduling are prospected.
【學(xué)位授予單位】：蘭州理工大學(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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本文編號：1905946