本文選題：云計(jì)算　切入點(diǎn)：資源調(diào)度　出處：《江西理工大學(xué)》2014年碩士論文

【摘要】：云計(jì)算是當(dāng)前信息技術(shù)領(lǐng)域的熱點(diǎn)話題之一,受到了工業(yè)界、學(xué)術(shù)界、政府和社會(huì)各界廣泛關(guān)注。經(jīng)過學(xué)術(shù)界與產(chǎn)業(yè)界的不斷努力,云計(jì)算技術(shù)正逐漸從理論走向?qū)嵺`,各IT巨頭紛紛加入到云計(jì)算的應(yīng)用中來。云環(huán)境中的資源調(diào)度策略已成為云計(jì)算領(lǐng)域的一個(gè)重要研究方向。云計(jì)算調(diào)度策略的核心技術(shù)是將網(wǎng)絡(luò)上眾多的軟硬件設(shè)備和資源,通過虛擬化技術(shù)整合成一個(gè)靈活的云處理系統(tǒng),實(shí)現(xiàn)有效的監(jiān)管。因此,對(duì)資源調(diào)度策略的研究一直是云計(jì)算領(lǐng)域的一個(gè)研究熱點(diǎn)。本文在系統(tǒng)分析云計(jì)算的各種關(guān)鍵技術(shù)基礎(chǔ)上,針對(duì)云環(huán)境下資源調(diào)度算法開展了重點(diǎn)研究,具體工作如下:(1)本文從資源管控策略、調(diào)配路徑及調(diào)配算法等方面對(duì)云計(jì)算供應(yīng)策略、云計(jì)算資源調(diào)度策略以及資源調(diào)度的性能指標(biāo)、云計(jì)算的負(fù)載均衡技術(shù)等進(jìn)行了全面分析研究。(2)本文探索了混洗蛙跳算法(SFLA)在云環(huán)境資源調(diào)度中的運(yùn)用,在分析混洗蛙跳算法不足的基礎(chǔ)上對(duì)種群選擇策略進(jìn)行了改進(jìn)。改進(jìn)后的混洗蛙跳算法(ISFLA)在種群初始化時(shí)引入輪盤賭隨機(jī)選取策略,通過加強(qiáng)位置較優(yōu)個(gè)體的適應(yīng)度值提高了算法的收斂速度;在子種群重新混洗時(shí)引入元胞自動(dòng)機(jī)策略,有效避免算法陷入局部最優(yōu)。(3)將上述改進(jìn)的SFLA算法與量子粒子群搜索策略結(jié)合,提出了基于量子粒子群局部搜索的新混洗蛙跳算法(QPSO-SFLA),從而提高了算法的局部搜索效率,加速算法的收斂。(4)在Cloud Sim平臺(tái)上模擬云計(jì)算資源調(diào)度的過程及任務(wù)請(qǐng)求方式,對(duì)上述改進(jìn)算法類進(jìn)行了實(shí)驗(yàn)驗(yàn)證。將QPSO-SFLA算法、ISFLA算法及平臺(tái)自帶算法對(duì)比實(shí)驗(yàn)結(jié)果表明,QPSO-SFLA算法在云計(jì)算資源調(diào)度上具有明顯的效率和成本優(yōu)勢。
[Abstract]:Cloud computing is one of the current hot topics in the field of information technology, which has received extensive attention from industry, academia, government and all walks of life. Through the continuous efforts of academia and industry, cloud computing technology is gradually moving from theory to practice. Various IT giants have joined the application of cloud computing. Resource scheduling strategy in cloud environment has become an important research direction in cloud computing field. The core technology of cloud computing scheduling strategy is to put many hardware and software devices and resources on the network. Through virtualization technology to integrate into a flexible cloud processing system to achieve effective regulation. Therefore, The research of resource scheduling strategy has been a hot topic in cloud computing field. Based on the systematic analysis of various key technologies of cloud computing, this paper focuses on resource scheduling algorithm in cloud environment. The specific work is as follows: (1) this paper analyzes the cloud computing supply strategy, cloud computing resource scheduling strategy and resource scheduling performance index from the aspects of resource control strategy, deployment path and deployment algorithm, etc. The load balancing technology of cloud computing is analyzed and studied comprehensively. 2) this paper explores the application of shuffled leapfrog algorithm (SFLAs) in cloud environment resource scheduling. Based on the analysis of the deficiency of the shuffled breaststroke algorithm, the population selection strategy is improved. The improved shuffling breaststroke algorithm (ISFLA) introduces the roulette random selection strategy when the population is initialized. The convergence rate of the algorithm is improved by strengthening the fitness value of the individuals with better position, and the cellular automata strategy is introduced when the subpopulation is remixed. The improved SFLA algorithm is combined with quantum particle swarm optimization (QPSO) strategy, and a new hybrid leapfrog algorithm (QPSO-SFLAA) based on QPSO local search is proposed, which improves the local search efficiency of the algorithm. Accelerate the convergence of the algorithm on the Cloud Sim platform to simulate the process of cloud computing resource scheduling and task request mode, The experimental results show that QPSO-SFLA algorithm has obvious efficiency and cost advantage in cloud computing resource scheduling.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.07;TP18

【共引文獻(xiàn)】

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2 孫小雁;;云存儲(chǔ)技術(shù)及其發(fā)展[J];玉林師范學(xué)院學(xué)報(bào);2014年02期

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5 李貞強(qiáng);陳康;武永衛(wèi);鄭緯民;;大數(shù)據(jù)處理模式——系統(tǒng)結(jié)構(gòu),方法以及發(fā)展趨勢[J];小型微型計(jì)算機(jī)系統(tǒng);2015年04期

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本文編號(hào)：1668994