本文選題：云計算　切入點：蟻群算法　出處：《微電子學與計算機》2017年11期 　論文類型：期刊論文

【摘要】：提出了一種兼顧任務最短完成時間、成本和負載均衡的改進的集多目標優(yōu)化的蟻群任務調(diào)度算法(time,cost and load balance ant colony optimization,TCL-ACO).首先,針對云計算下任務調(diào)度的特點定義任務完成時間成本的約束函數(shù)和負載均衡度函數(shù).對于蟻群算法進行初始信息素、啟發(fā)函數(shù)、信息素更新方式進行改進.然后,用改進的蟻群算法求解目標約束函數(shù)得到全局最優(yōu)解.最后在cloudsim下進行仿真,并與Min-Min算法和ACO算法進行仿真對比,實驗結(jié)果表明本文算法在成本、任務的執(zhí)行時間和負載均衡方面優(yōu)于這兩種算法.
[Abstract]:In this paper, an improved ant colony task scheduling algorithm, which takes into account the minimum completion time, cost and load balancing of tasks, is proposed, which is cost and load balance ant colony optimization TCL-ACOO. According to the characteristics of task scheduling in cloud computing, the constraint function and load balancing function of task completion time cost are defined. The initial pheromone, heuristic function and pheromone updating method of ant colony algorithm are improved. The improved ant colony algorithm is used to solve the objective constraint function to obtain the global optimal solution. Finally, the simulation is carried out under cloudsim, and compared with the Min-Min algorithm and ACO algorithm. The experimental results show that the proposed algorithm is cost effective. Task execution time and load balancing are better than these two algorithms.
【作者單位】： 重慶郵電大學計算機科學與技術(shù)學院;重慶郵電大學圖書館;
【分類號】：TP18;TP3
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本文編號：1628508