当前位置:主页 > 科技论文 > 自动化论文 >

云计算任务调度的粒子群算法

发布时间:2018-07-29 12:40
【摘要】:云计算技术已然成为当今最热门的网络技术之一.云计算技术的兴起,既是信息技术迅速发展的产物,也是人类社会对生活工作提出更高要求的体现.云计算技术虚化了个人计算机的概念,而是通过第三方来实现计算机的存储和计算任务,然后通过按需付费的方式提供给大众使用.因此在第三方数据中心中如何快速高效的调度和使用巨大的资源,已经成为云计算技术发展的关键.首先,本文将粒子群算法成功的应用于云计算任务调度中,为了避免标准粒子群算法易陷入局部最优的缺陷,因此引入了切比雪夫混沌扰动策略,通过扰动策略使得粒子群算法在运算后期有能力跳出局部最优,使得粒子群算法可以得到更好的全局寻优结果.通过运用云计算仿真平台Cloudsim进行验证,实验结果表明改进后的粒子群算法与其他一些传统算法相比,在云计算任务调度中可以更短的时间内获得较好的调度结果.其次,本文在引入切比雪夫混沌扰动策略的同时,还加入了动态惯性权重策略,使得改进后的粒子群算法既有能力跳出局部最优,还可以根据实际问题动态的调节自身全局搜索和局部搜素的能力.并将改进后的算法应用于云计算任务调度中,通过运用云计算仿真平台Cloudsim进行验证,实验结果表明改进后的算法比上述的改进算法具有更优异的调度结果,且所用的时间更短.最后,对多目标粒子群算法进行学习和研究,并应用于云计算任务调度中.通过引入动态惯性权重策略以及自适应进化学习策略,将多目标粒子群算法进行改进.通过运用云计算仿真平台Cloudsim进行验证,实验结果表明改进后的多目标粒子群算法在多目标云计算任务调度中在较短的时间内可以获得较好的调度结果.
[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.
【学位授予单位】:北方民族大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TP18

【参考文献】

相关期刊论文 前10条

1 张佩云;凤麒;;一种云计算环境下的工作流双向调度方法[J];计算机科学;2015年S2期

2 吴丽;余文春;;基于多服务器最优配置的云计算利润最大化技术研究[J];计算机应用研究;2015年01期

3 王建林;吴佳欢;张超然;赵利强;于涛;;基于自适应进化学习的约束多目标粒子群优化算法[J];控制与决策;2014年10期

4 罗亮;吴文峻;张飞;;面向云计算数据中心的能耗建模方法[J];软件学报;2014年07期

5 刘川意;林杰;唐博;;面向云计算模式运行环境可信性动态验证机制[J];软件学报;2014年03期

6 任昱;李青荣;;基于VMware vSphere虚拟化资源管理平台研究[J];计算机应用与软件;2012年05期

7 王晟;赵壁芳;;云计算中MapReduce技术研究[J];通信技术;2011年12期

8 罗军舟;金嘉晖;宋爱波;东方;;云计算:体系架构与关键技术[J];通信学报;2011年07期

9 华夏渝;郑骏;胡文心;;基于云计算环境的蚁群优化计算资源分配算法[J];华东师范大学学报(自然科学版);2010年01期

10 陈康;郑纬民;;云计算:系统实例与研究现状[J];软件学报;2009年05期



本文编号:2152699

资料下载
论文发表

本文链接:https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2152699.html


Copyright(c)文论论文网All Rights Reserved | 网站地图 |

版权申明:资料由用户31bea***提供,本站仅收录摘要或目录,作者需要删除请E-mail邮箱bigeng88@qq.com