摘要：

针对目前负载预测模型不容易捕获数据时序信息和特征信息的问题，提出一种双重注意力机制的负载预测模型。该模型融合特征注意力机制和时序注意力机制，自适应地挖掘服务器负载的特征信息和时序信息，有效增强网络对特征数据和时序数据的关键信息表达。基于CRITIC方法对服务器各项特征进行加权，计算服务器负载值，从而更加全面和准确地评估服务器下一时刻的负载状态。双重注意力机制网络在长短期记忆网络（LSTM）的基础上引入特征注意力机制和时序注意力机制，以负载值的历史数据为输入对未来时刻服务器的负载值进行预测，有效提高网络对单步预测和多步预测的准确性。在阿里巴巴Cluster-trace-v2018公开数据集上的实验结果表明，双重注意力机制网络相对于LSTM基础的负载预测网络，平均绝对误差和均方误差分别下降了9.2%和16.8%，具有较好的稳定性和准确性。

关键词: 云计算, CRITIC方法, 长短期记忆网络, 负载预测, 注意力机制

Abstract:

In response to the challenge of capturing both timing and feature information in current load forecasting models, we propose a dual attention mechanism-based load forecasting model. This model seamlessly integrates both feature attention and temporal attention mechanisms, allowing it to adaptively extract feature and temporal information from server load data. This enhanced approach effectively emphasizes key information within feature and temporal data within the network. To comprehensively and accurately evaluate server load status for the next moment, we employ the CRITIC objective weighting method. This method assigns weights to various server characteristics, facilitating precise load value calculations. The resulting dual attention mechanism network builds upon a foundation of short-term and Long Short-Term Memory(LSTM) networks. It introduces both characteristic and temporal attention mechanisms while utilizing historical load data as input to predict future server load values. This approach significantly enhances the accuracy of the network for both single-step and multi-step load predictions. Experimental results using the Alibaba Cluster-trace-v2018 public dataset demonstrate the superiority of our dual attention mechanism network over LSTM-based load prediction networks. Specifically, the Mean Absolute Error(MAE) and Mean Square Error(MSE) of the dual attention mechanism network show impressive reductions of 9.2% and 16.8% respectively. This performance improvement underscores the network's stability and accuracy.

Key words: cloud computing, CRITIC method, Long Short-Term Memory(LSTM) network, load forecasting, attention mechanism