本文選題：云計算 + K-means聚類��； 參考：《內(nèi)蒙古農(nóng)業(yè)大學》2014年碩士論文

【摘要】：本文通過深入研究K-means算法,針對傳統(tǒng)K-means算法的不足,提出將細菌覓食算法和粒子群優(yōu)化算法應用于K-means算法的改進,在MATLAB環(huán)境下仿真實驗,驗證了改進后的K-means算法的聚類質(zhì)量有明顯的提高。然后通過對基于傳統(tǒng)K-means的任務調(diào)度算法的理論研究發(fā)現(xiàn),該算法所表現(xiàn)的調(diào)度能力有待提高。因此利用改進后的K-means算法合理分組處理任務,然后進行Min-Min算法調(diào)度任務,以達到或接近最優(yōu)解即任務分配方案。通過CloudSim云平臺的仿真實驗,,實驗結果表明,該算法不僅有效降低了任務調(diào)度的總體完成時間,而且提高了任務分配效率和系統(tǒng)資源的利用率,在任務完成時間和負載平衡性上優(yōu)于Min-Min算法和基于傳統(tǒng)K-means的Min-Min算法。本文的研究思路將為以后的云計算任務分配策略的研究提供參考和幫助。
[Abstract]:In this paper, through in-depth study of K-means algorithm, aiming at the shortcomings of traditional K-means algorithm, this paper proposes to apply bacterial foraging algorithm and particle swarm optimization algorithm to the improvement of K-means algorithm, and simulates the experiment in MATLAB environment. The clustering quality of the improved K-means algorithm is obviously improved. Then, through the theoretical research of the traditional K-means based task scheduling algorithm, it is found that the scheduling ability of the algorithm needs to be improved. Therefore, the improved K-means algorithm is used to deal with tasks reasonably, and then Min-Min algorithm is used to schedule tasks in order to achieve or approach the optimal solution, that is, the task allocation scheme. The simulation results of CloudSim cloud platform show that the algorithm not only reduces the overall completion time of task scheduling, but also improves the efficiency of task allocation and the utilization of system resources. It is superior to Min-Min algorithm and Min-Min algorithm based on traditional K-means in task completion time and load balance. The research ideas of this paper will provide reference and help for the future research of cloud computing task allocation strategy.
【學位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09

【參考文獻】

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本文編號：2063274