[1]李思莉,杨井荣.基于遗传算法的改进时序预测模型研究[J].计算机技术与发展,2020,30(11):84-88.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 016]
　LI Si-li,YANG Jing-rong.Research on Improved Time Series Prediction Model Based on Genetic Algorithm[J].,2020,30(11):84-88.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 016]
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基于遗传算法的改进时序预测模型研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年11期
页码:
84-88
栏目:
智能、算法、系统工程
出版日期:
2020-11-10

文章信息/Info

Title:
Research on Improved Time Series Prediction Model Based on Genetic Algorithm
文章编号:
1673-629X(2020)11-0085-05
作者:
李思莉; 杨井荣
成都理工大学 工程技术学院 电子信息与计算机工程系,四川 乐山 614000
Author(s):
LI Si-li; YANG Jing-rong
Department of Electronic Information and Computer Engineering,The Engineering & Technical College of Chengdu University of Technology,Leshan 614000,China
关键词:
云计算; 弹性伸缩; 动态分配; 遗传算法; ARIMA
Keywords:
cloud computing; elastic expansion; dynamic allocation; genetic algorithm; ARIMA
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 11. 016
摘要:
云计算系统通过对存储、软件、服务等资源进行统一调度来为用户提供所需的服务。 用户的需求具有多样性、多变性, 使用弹性伸缩技术可以提高用户满意度,很好地解决资源利用率和应用系 统之间的矛盾, 是云计算的关键技术之一。 然而,网络应用程序的工作负载通常是动态的,并且很难预测。 因此, 云计算中 Web 应用的关键技术是根据负载进行资源的动态分配,这是研究的热点,也是难点。 目前,针对动态伸缩算法,提出的解决方案多是独立的、单一的或基于过去资源使用率进行提前预测。 但这些方法容易导致资源利用不足。 该文提出利用遗传算法改进时序预测模型 ARIMA? 计算所需的虚拟主机数,以实现提高资源利用率,达到资源快速伸缩的目的。 所提出的模型已经用几个基准工作负载进行了验证,在虚拟主机数和响应时间方面有一定的改善。
Abstract:
Cloud computing system provides users with the required services through the unified scheduling of storage,software,services and other resources. Users’ needs are diverse and changeable. The use of elastic and scalable technology can improve user satisfaction and solve the contradiction between resource utilization rate and application system, which is one of the key technol-ogies of cloud computing. However,the workload of network applications is usually dynamic and difficult to predict. Therefore,the key technology of Web application in cloud computing is the dynamic allocation of resources according to the load,which is the research hotspot and also the difficulty. At present,for dynamic scaling algorithms,most of the proposed solutions are independent, single or based on the past resource utilization to predict in advance. But these methods are easy to lead to insufficient utilization of resources. We adopt genetic algorithm to improve the time series prediction model to calculate the number of virtual hosts needed,so as to improve the utilization rate of resources and achieve the rapid resource expansion. The proposed model has been validated by several benchmark workloads,and has some improvement in the number of virtual hosts and response time.

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更新日期/Last Update: 2020-11-10