【摘要】：日前，人們對(duì)計(jì)算能力、軟件服務(wù)質(zhì)量以及大規(guī)模數(shù)據(jù)量的處理要求越來越高，而現(xiàn)有的計(jì)算能力不能滿足這些需要，于是云計(jì)算得以提出。云計(jì)算發(fā)展到今天，不論是在學(xué)術(shù)界還是在商業(yè)領(lǐng)域都有著非常廣泛的應(yīng)用�？萍嫉陌l(fā)展使得現(xiàn)在數(shù)據(jù)量的級(jí)別從最早的GB級(jí)上升到TB級(jí)乃至PB級(jí)，因此研究出更好的云平臺(tái)計(jì)算服務(wù)迫在眉睫。 本文一開始對(duì)云計(jì)算的定義做了歸納，提到計(jì)算能力作為一種商品向用戶提供并按使用情況收取服務(wù)費(fèi)用，接著列舉了云計(jì)算系統(tǒng)的特點(diǎn)以及架構(gòu)，并對(duì)云計(jì)算實(shí)現(xiàn)的關(guān)鍵技術(shù)做了詳細(xì)的分析，然后介紹了當(dāng)今流行的云平臺(tái)。為了能讓本文提出的算法在云平臺(tái)上模擬實(shí)驗(yàn)，本文還研究了MapReduce機(jī)制的原理、執(zhí)行流程、 Hadoop的架構(gòu)等。同時(shí)，為了比較本文提出的算法和Hadoop資源調(diào)度算法的異同，本文就當(dāng)今流行的三種作業(yè)調(diào)度算法：FIFO隊(duì)列調(diào)度算法、Fair公平調(diào)度算法以及基于計(jì)算性能的Capacity算法做了詳細(xì)的研究，分析了每一種算法的優(yōu)劣，以便同本文的算法進(jìn)行更為詳細(xì)的比較。 分布在云平臺(tái)下的節(jié)點(diǎn)資源數(shù)量非常巨大，這就不可避免的造成了不可靠節(jié)點(diǎn)資源的出現(xiàn)，，這些節(jié)點(diǎn)會(huì)對(duì)應(yīng)用程序的執(zhí)行和調(diào)度任務(wù)產(chǎn)生很大的影響。在本文中，受貝葉斯認(rèn)知模型的啟發(fā)和社會(huì)學(xué)的信任關(guān)系模型的引導(dǎo)，本文首先提出了一種新的基于貝葉斯方法的認(rèn)知信任模型，然后，將這種模型應(yīng)用到資源調(diào)度系統(tǒng)中。理論分析和仿真實(shí)驗(yàn)證明，本文提出的方法能有效的滿足云計(jì)算對(duì)節(jié)點(diǎn)資源的信任要求，并且犧牲較少的時(shí)間成本，確保在一個(gè)相對(duì)安全的節(jié)點(diǎn)資源池中執(zhí)行云計(jì)算任務(wù)。
[Abstract]:A few days ago, the demands of computing power, software quality of service and large amount of data were higher and higher, but the existing computing power could not meet these needs, so cloud computing was put forward. Cloud computing has been widely used in both academic and commercial fields. With the development of science and technology, the level of data volume has risen from the earliest GB level to the TB level and even the PB level, so it is urgent to develop a better cloud platform computing service. At the beginning of this paper, we summarize the definition of cloud computing, mention that computing power is provided to users as a commodity and charge the service fee according to the usage, and then enumerate the characteristics and architecture of cloud computing system. The key technologies of cloud computing are analyzed in detail, and then the popular cloud platform is introduced. In order to enable the algorithm proposed in this paper to simulate the experiments on the cloud platform, this paper also studies the principle of MapReduce mechanism, execution flow, Hadoop architecture and so on. At the same time, in order to compare the similarities and differences between the proposed algorithm and Hadoop resource scheduling algorithm, this paper makes a detailed study on three popular job scheduling algorithms: FIFO queue scheduling algorithm, Fair fair scheduling algorithm and Capacity algorithm based on computational performance. The advantages and disadvantages of each algorithm are analyzed in order to compare with the algorithm in detail. The number of node resources distributed in the cloud platform is very large, which inevitably leads to the emergence of unreliable node resources, and these nodes will have a great impact on the execution and scheduling of applications. In this paper, inspired by Bayesian cognitive model and guided by sociological trust relationship model, this paper first proposes a new cognitive trust model based on Bayesian method, and then applies this model to resource scheduling system. Theoretical analysis and simulation experiments show that the proposed method can effectively meet the trust requirements of cloud computing to node resources, and at the expense of less time cost, ensure the implementation of cloud computing tasks in a relatively secure node resource pool.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 王峰;;Hadoop集群作業(yè)的調(diào)度算法[J];程序員;2009年12期

2 查禮,徐志偉,林國(guó)璋,劉玉樹;基于Simgrid的網(wǎng)格任務(wù)調(diào)度模擬[J];計(jì)算機(jī)工程與應(yīng)用;2003年14期

3 張曉東;張培林;傅建平;王成;;基于Matlab/Fluent的協(xié)同仿真技術(shù)[J];計(jì)算機(jī)工程;2010年20期

4 孔邵穎;郭宏亮;;混合算法在網(wǎng)格任務(wù)調(diào)度中的應(yīng)用研究[J];計(jì)算機(jī)仿真;2011年09期

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