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。
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.
作者
林涛
冯竞凯
郝章肖
黄少群
LIN Tao;FENG Jing-kai;HAO Zhang-xiao;HUANG Shao-qun(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401;School of Management,Harbin University of Commerce,Harbin 150000,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第7期1168-1173,共6页
Computer Engineering & Science
基金
国家自然科学基金(61976242)。
关键词
云计算
资源管理
负载预测
LSTM
ARIMA
cloud computing
resource management
load forecasting
long short-term memory
auto-regressive integrated moving average
