【摘要】：云計算技術已然成為當今最熱門的網絡技術之一.云計算技術的興起,既是信息技術迅速發(fā)展的產物,也是人類社會對生活工作提出更高要求的體現(xiàn).云計算技術虛化了個人計算機的概念,而是通過第三方來實現(xiàn)計算機的存儲和計算任務,然后通過按需付費的方式提供給大眾使用.因此在第三方數(shù)據(jù)中心中如何快速高效的調度和使用巨大的資源,已經成為云計算技術發(fā)展的關鍵.首先,本文將粒子群算法成功的應用于云計算任務調度中,為了避免標準粒子群算法易陷入局部最優(yōu)的缺陷,因此引入了切比雪夫混沌擾動策略,通過擾動策略使得粒子群算法在運算后期有能力跳出局部最優(yōu),使得粒子群算法可以得到更好的全局尋優(yōu)結果.通過運用云計算仿真平臺Cloudsim進行驗證,實驗結果表明改進后的粒子群算法與其他一些傳統(tǒng)算法相比,在云計算任務調度中可以更短的時間內獲得較好的調度結果.其次,本文在引入切比雪夫混沌擾動策略的同時,還加入了動態(tài)慣性權重策略,使得改進后的粒子群算法既有能力跳出局部最優(yōu),還可以根據(jù)實際問題動態(tài)的調節(jié)自身全局搜索和局部搜素的能力.并將改進后的算法應用于云計算任務調度中,通過運用云計算仿真平臺Cloudsim進行驗證,實驗結果表明改進后的算法比上述的改進算法具有更優(yōu)異的調度結果,且所用的時間更短.最后,對多目標粒子群算法進行學習和研究,并應用于云計算任務調度中.通過引入動態(tài)慣性權重策略以及自適應進化學習策略,將多目標粒子群算法進行改進.通過運用云計算仿真平臺Cloudsim進行驗證,實驗結果表明改進后的多目標粒子群算法在多目標云計算任務調度中在較短的時間內可以獲得較好的調度結果.
[Abstract]:Cloud computing technology has become one of the most popular network technologies. The rise of cloud computing technology is not only the product of the rapid development of information technology, but also the embodiment of human society to put forward higher requirements for life and work. Cloud computing technology is a virtual concept of personal computers, but through a third party to achieve computer storage and computing tasks, and then through on-demand payment to the public to use. Therefore, how to quickly and efficiently schedule and use huge resources in third-party data centers has become the key to the development of cloud computing technology. First of all, particle swarm optimization algorithm is successfully applied to cloud computing task scheduling. In order to avoid the defect that standard particle swarm optimization algorithm is easy to fall into local optimum, Chebyshev chaos perturbation strategy is introduced. The PSO algorithm is able to jump out of the local optimum in the later stage of operation by perturbation strategy, so that the PSO algorithm can get better global optimization results. The experimental results show that the improved particle swarm optimization algorithm can obtain better scheduling results in a shorter time than other traditional algorithms by using cloud computing simulation platform Cloudsim. Secondly, the Chebyshev chaos perturbation strategy is introduced, and the dynamic inertial weight strategy is added, which makes the improved particle swarm optimization algorithm have the ability to jump out of the local optimum. The ability of global search and local search can be adjusted dynamically according to the actual problem. The improved algorithm is applied to the task scheduling of cloud computing and verified by the cloud computing simulation platform Cloudsim. The experimental results show that the improved algorithm has better scheduling results than the above improved algorithm and the time used is shorter. Finally, the multi-objective particle swarm optimization algorithm is studied and applied to cloud computing task scheduling. By introducing dynamic inertial weight strategy and adaptive evolutionary learning strategy, the multi-objective particle swarm optimization algorithm is improved. By using cloud computing simulation platform Cloudsim, the experimental results show that the improved multi-objective particle swarm optimization algorithm can obtain better scheduling results in a short time in multi-objective cloud computing task scheduling.
【學位授予單位】：北方民族大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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