【摘要】：近年來，移動(dòng)互聯(lián)網(wǎng)業(yè)務(wù)迅猛發(fā)展。與PC和傳統(tǒng)互聯(lián)網(wǎng)業(yè)務(wù)相比，，移動(dòng)云計(jì)算有著瘦終端、網(wǎng)絡(luò)帶寬重載和高時(shí)延等特點(diǎn)，這就決定了有效的資源管理成為了移動(dòng)云計(jì)算中的熱點(diǎn)與難點(diǎn)。 本文首先在深入研究現(xiàn)有云計(jì)算和移動(dòng)互聯(lián)網(wǎng)技術(shù)的基礎(chǔ)上，分析針對(duì)移動(dòng)云計(jì)算的資源管理需求。其次，為虛擬化資源管理平臺(tái)設(shè)計(jì)了一個(gè)多層映射的管理模型。然后，提出了基于元胞粒子群的資源調(diào)度算法，基于小波分解和支持向量機(jī)的資源分配預(yù)測(cè)算法。最后，分別通過實(shí)驗(yàn)驗(yàn)證了上述兩種算法的正確性和優(yōu)越性，檢驗(yàn)了虛擬化資源管理模型的效果。 本文的主要?jiǎng)?chuàng)新點(diǎn)如下： (1)提出層次化的虛擬化資源管理模型。根據(jù)服務(wù)調(diào)度粒度和靈活性，提出了服務(wù)-資源映射模型，同時(shí)給出資源分配的方案，實(shí)現(xiàn)了細(xì)粒度化用戶請(qǐng)求。 (2)提出了基于元胞粒子群的虛擬資源調(diào)度算法。通過分析虛擬化資源調(diào)度問題的特點(diǎn)，構(gòu)建合理的多目標(biāo)優(yōu)化模型，結(jié)合微粒群優(yōu)化算法和元胞自動(dòng)機(jī)，設(shè)計(jì)了合理的、高效的資源調(diào)度算法。 (3)提出了基于小波分解和支持向量機(jī)的資源分配預(yù)測(cè)算法。根據(jù)DB2小波分解和SVM提出了以小波分解-SVM建模-小波重構(gòu)為過程的預(yù)測(cè)算法，對(duì)資源使用的歷史數(shù)據(jù)進(jìn)行訓(xùn)練學(xué)習(xí)，能夠準(zhǔn)確實(shí)現(xiàn)下個(gè)時(shí)間節(jié)點(diǎn)的資源分配預(yù)測(cè)。
[Abstract]:In recent years, mobile Internet services are developing rapidly. Compared with PC and traditional Internet services, mobile cloud computing has the characteristics of thin terminal, network bandwidth overload and high delay, which determines that effective resource management has become a hot and difficult point in mobile cloud computing. This paper firstly analyzes the resource management requirements of mobile cloud computing on the basis of in-depth research on the existing cloud computing and mobile Internet technologies. Secondly, a multi-layer mapping management model is designed for the virtualized resource management platform. Then, a resource scheduling algorithm based on cellular particle swarm optimization and a resource allocation prediction algorithm based on wavelet decomposition and support vector machine are proposed. Finally, the correctness and superiority of the two algorithms are verified by experiments, and the effectiveness of the virtual resource management model is tested. The main innovations of this paper are as follows: (1) A hierarchical virtual resource management model is proposed. According to the granularity and flexibility of service scheduling, the service-resource mapping model is proposed, and the scheme of resource allocation is given, which realizes the fine-grained user request. (2) A virtual resource scheduling algorithm based on Cellular Particle Swarm Optimization (CPSO) is proposed. By analyzing the characteristics of the virtual resource scheduling problem, a reasonable multi-objective optimization model is constructed, and a reasonable and efficient resource scheduling algorithm is designed by combining the particle swarm optimization algorithm and cellular automata. (3) Resource allocation prediction algorithm based on wavelet decomposition and support vector machine is proposed. According to DB2 wavelet decomposition and SVM, a prediction algorithm based on wavelet decomposition-SVM modeling and wavelet reconstruction is proposed. The historical data of resource utilization can be trained and studied, and the prediction of resource allocation at the next time node can be realized accurately.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP3;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 孫健;賈曉菁;;Google云計(jì)算平臺(tái)的技術(shù)架構(gòu)及對(duì)其成本的影響研究[J];電信科學(xué);2010年01期

2 陳國(guó)宏;蔡彬清;李美娟;;元胞自動(dòng)機(jī):一種探索管理系統(tǒng)復(fù)雜性的有效工具[J];中國(guó)工程科學(xué);2007年01期

3 張榮建;張志強(qiáng);祖述勛;;鋼管混凝土拱橋安全性評(píng)價(jià)的SVM機(jī)器模型[J];混凝土;2011年11期

4 劉勇;馬良;;元胞微粒群算法及其在多維背包問題中的應(yīng)用[J];管理科學(xué)學(xué)報(bào);2011年01期

5 林立;鄒昌偉;;基于Android平臺(tái)的云計(jì)算研究[J];軟件導(dǎo)刊;2010年11期

6 劉勇;馬良;;非線性0-1規(guī)劃的元胞蟻群算法[J];系統(tǒng)管理學(xué)報(bào);2010年03期

相關(guān)碩士學(xué)位論文 前1條

1 王華軍;基于小波分解和ARIMA的網(wǎng)絡(luò)流量模型[D];山東大學(xué);2011年

本文編號(hào)：2307990