• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2020, Vol. 42 ›› Issue (07): 1168-1173.

• 高性能计算 • 上一篇    下一篇

基于组合预测模型的云计算资源负载预测研究

林涛, 冯竞凯, 郝章肖, 黄少群   

  1. (1.河北工业大学人工智能与数据科学学院,天津 300401;2.哈尔滨商业大学管理学院,黑龙江 哈尔滨 150000)
  • 收稿日期:2019-11-13 修回日期:2020-05-06 接受日期:2020-07-25 出版日期:2020-07-25 发布日期:2020-07-25
  • 基金资助:
    国家自然科学基金(61976242)

Cloud computing resource load prediction  based on combined prediction model

LIN Tao, FENG Jing-kai, HAO Zhang-xiao, HUANG Shao-qun   

  1. (1.School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401;

    2.School of Management,Harbin University of Commerce,Harbin 150000,China)

  • Received:2019-11-13 Revised:2020-05-06 Accepted:2020-07-25 Online:2020-07-25 Published:2020-07-25

摘要: 随着云计算技术的不断发展,云计算资源负载变化呈现出越来越复杂的特征。针对云计算资源的负载预测问题,综合考虑云计算环境中资源负载时间序列的线性与非线性特性,提出了一种基于自回归移动平均模型ARIMA与长短期记忆网络LSTM的组合预测模型LACL。使用公开数据集与传统负载预测模型进行了对比实验,实验结果表明,该云计算资源组合预测模型预测精度明显高于其他预测模型,显著
降低了云环境中对资源负载的实时预测误差。

关键词: 云计算, 资源管理, 负载预测, LSTM, ARIMA

Abstract: With the continuous development of cloud computing technology, cloud computing resource load changes exhibits more and more complex features. For the workload prediction problem of cloud computing resources, the linear and nonlinear characteristics of resource workload time series in cloud computing environment are considered comprehensively. This paper proposes a combined prediction model based on auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM). The experiments are carried out to compare the proposal and the traditional load prediction algorithm on the public dataset. The experimental results show that the cloud computing resource combination prediction model has significantly higher prediction accuracy than other prediction models, which significantly reduces the real-time prediction error of resource workload in the cloud environment.


Key words: cloud computing, resource management, load forecasting,
long short-term memory,
auto-regressive integrated moving average