一种多变量云工作负载预测方法,集成卷积非线性尖端神经模型与双向长短期记忆
Minglong He1, Nan Zhou1, Hong Peng1
1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
International journal of neural systems
|September 30, 2025
概括
本研究介绍了一种新的混合模型,用于云计算中的多变量工作负载预测. 拟议的模型显著提高了预测准确性,超过了现有的深度学习方法.
科学领域:
- 云计算 云计算 云计算 云计算
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 多变量工作负载预测对于高效的云资源管理至关重要.
- 现有的模型难以捕捉复杂的变量间相关性和时间动态.
研究的目的:
- 为准确的多变量工作负载预测开发一个先进的模型.
- 增强非线性数据模式和长期时间依赖性的捕获.
主要方法:
- 提出了一个混合模型,将非线性尖端神经P系统 (ConvNSNP) 与双向长短期记忆 (BiLSTM) 网络集成在一起.
- ConvNSNP提取了时间和跨变量依赖关系,而BiLSTM加强了长期建模.
- 该模型在阿里巴巴和谷歌的公共云工作负载痕迹上进行了评估.
主要成果:
- 与各种深度学习方法 (CNN,RNN,LSTM,TCN,LSTNet,CNN-GRU,CNN-LSTM) 相比,拟议的模型表现出更高的性能.
- 在根平均平方误差 (RMSE) 中获得了高达9.9%的改善,在平均绝对误差 (MAE) 中获得了11.6%的改善.
- 在平均绝对百分比误差 (MAPE) 中表现出良好的表现.
结论:
- 混合ConvNSNP-BiLSTM模型在云环境中对多变量工作负载预测非常有效.
- 该模型能够处理非线性数据并捕获复杂的依赖关系,从而提高了预测准确度.
- 这项研究为云计算工作负载预测方法提供了重大进展.
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