本文選題：云計(jì)算　切入點(diǎn)：蟻群算法　出處：《微電子學(xué)與計(jì)算機(jī)》2017年11期 　論文類(lèi)型：期刊論文

【摘要】：提出了一種兼顧任務(wù)最短完成時(shí)間、成本和負(fù)載均衡的改進(jìn)的集多目標(biāo)優(yōu)化的蟻群任務(wù)調(diào)度算法(time,cost and load balance ant colony optimization,TCL-ACO).首先,針對(duì)云計(jì)算下任務(wù)調(diào)度的特點(diǎn)定義任務(wù)完成時(shí)間成本的約束函數(shù)和負(fù)載均衡度函數(shù).對(duì)于蟻群算法進(jìn)行初始信息素、啟發(fā)函數(shù)、信息素更新方式進(jìn)行改進(jìn).然后,用改進(jìn)的蟻群算法求解目標(biāo)約束函數(shù)得到全局最優(yōu)解.最后在cloudsim下進(jìn)行仿真,并與Min-Min算法和ACO算法進(jìn)行仿真對(duì)比,實(shí)驗(yàn)結(jié)果表明本文算法在成本、任務(wù)的執(zhí)行時(shí)間和負(fù)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(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;重慶郵電大學(xué)圖書(shū)館;
【分類(lèi)號(hào)】：TP18;TP3
，

本文編號(hào)：1628508