基于深度置信网络的云应用负载预测方法
发布时间：2019-04-19 22:38
【摘要】：为了准确预测云应用负载以便及时执行云应用自适应优化,从而保证云应用性能的稳定,根据云环境下应用负载预测问题的特点,提出了基于深度置信网络的云应用负载预测方法.首先给出能够有效描述负载数据的显式特征和隐式特征并定义了负载预测模型,进而给出基于深度置信网络的负载预测算法.对算法进行了分析并在真实数据集上与相关算法进行了比较,结果表明,本文提出的方法能够更加有效地解决云应用负载预测问题.
[Abstract]:In order to accurately predict cloud application load in order to perform cloud application adaptive optimization in a timely manner, so as to ensure the stability of cloud application performance, according to the characteristics of application load prediction problem in cloud environment, A cloud application load prediction method based on deep confidence network is proposed. Firstly, the explicit and implicit characteristics which can describe the load data effectively are given, and the load prediction model is defined. Then, the load prediction algorithm based on the depth confidence network is given. The algorithm is analyzed and compared with the related algorithms on the real data set. The results show that the proposed method can solve the problem of cloud application load prediction more effectively.
【作者单位】： 东北大学计算机科学与工程学院;
【基金】：国家科技支撑计划项目(2014BAI17B00) 国家自然科学基金资助项目(61572116,61572117,61502089)
【分类号】：TP393.09
本文编号：2461350
[Abstract]:In order to accurately predict cloud application load in order to perform cloud application adaptive optimization in a timely manner, so as to ensure the stability of cloud application performance, according to the characteristics of application load prediction problem in cloud environment, A cloud application load prediction method based on deep confidence network is proposed. Firstly, the explicit and implicit characteristics which can describe the load data effectively are given, and the load prediction model is defined. Then, the load prediction algorithm based on the depth confidence network is given. The algorithm is analyzed and compared with the related algorithms on the real data set. The results show that the proposed method can solve the problem of cloud application load prediction more effectively.
【作者单位】： 东北大学计算机科学与工程学院;
【基金】：国家科技支撑计划项目(2014BAI17B00) 国家自然科学基金资助项目(61572116,61572117,61502089)
【分类号】：TP393.09
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