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首页 > 过刊浏览>2021年第44卷第9期 >109-116. DOI:10.11835/j.issn.1000-582X.2021.09.011
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基于QoS云计算任务调度优化
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2018年教育部第二批产学协作育人项目立项项目（201802002058）；成都市交通+旅游大数据应用技术研究基地项目（2019001，2018022）。


Task scheduling optimization based on QoS cloud computing
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    摘要:

    由于云计算技术快速发展，为了满足日益多样化的云计算用户服务质量（QoS需求）以及提高云计算资源调度的效率，提出基于改进蚁群算法的云计算资源调度优化算法，包括建立云计算资源模型和用户QoS需求模型。为了得到更准确的结论，针对传统蚁群算法过快收敛造成的局部最优解现象，在传统的蚁群算法的基础上加入随机选择机制，时间、成本和结果有效可用性适应度因子进行了优化改良，以求得全局最优解。通过仿真实验将传统的蚁群算法、Min-Min调度算法和改进的蚁群优化算法进行比较，实验表明，改进的蚁群优化算法在调度效率、节约成本、减少任务执行时间和任务得到结果质量方面有明显的优势。

    Abstract:

    Cloud computing technology is in rapid development. In order to meet the increasingly diverse cloud computing user service quality (QoS) requirements and to improve the efficiency of cloud computing resource scheduling, a cloud computing resource scheduling optimization algorithm based on improved ant colony algorithm is proposed, including establishing cloud computing-resource model and user QoS requirements model. In order to obtain better results and solve the problem of the local optimal solution caused by the fast convergence of traditional ant colony algorithm, a random selection mechanism is added to the traditional ant colony algorithm. The time, cost and effective availability fitness factor of results are optimized and improved to obtain the global optimal solution. The traditional ant colony algorithm, Min-Min scheduling algorithm and improved ant colony optimization algorithm are compared by simulation experiments. Experimental results show that the improved ant colony optimization algorithm has advantages in scheduling efficiency, cost saving, time-saving and quality results in task execution.

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聂清彬,陈飞旭,秦美峰,曹耀钦.基于QoS云计算任务调度优化[J].重庆大学学报,2021,44(9):109-116.

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  • 收稿日期:2019-05-10
  • 在线发布日期: 2021-10-08
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