论文标题
主机负载预测在云计算中具有双向长期记忆的双向长期记忆
Host Load Prediction with Bi-directional Long Short-Term Memory in Cloud Computing
论文作者
论文摘要
主机负载预测是用于管理云平台上计算资源使用情况的基本决策信息，其准确性对于实现Servicelevel协议至关重要。与网格计算相比，云环境中的主机负载数据是更高的波动性和噪声，传统数据驱动的方法在处理云计算的主机负载时往往具有低预测精度，因此，我们在本文中提出了一种基于双向短期内存（BILSTM）的主机负载预测方法。我们的基于Bilstm的Apporach提高了LSTM和LSTM编码器编码器（LSTM-ED）的内存胶粘性和非线性建模能力，在最近的现在工作中使用，以评估我们的方法，我们使用了1个月的Google Data Center进行了超过十二千千万个机器进行实验。我们基于BILSTM的方法成功地达到了与以前的其他模型相比，包括最近的LSTM ONE和LSTM-ED ONE。
Host load prediction is the basic decision information for managing the computing resources usage on the cloud platform, its accuracy is critical for achieving the servicelevel agreement. Host load data in cloud environment is more high volatility and noise compared to that of grid computing, traditional data-driven methods tend to have low predictive accuracy when dealing with host load of cloud computing, Thus, we have proposed a host load prediction method based on Bidirectional Long Short-Term Memory (BiLSTM) in this paper. Our BiLSTM-based apporach improve the memory capbility and nonlinear modeling ability of LSTM and LSTM Encoder-Decoder (LSTM-ED), which is used in the recent previous work, In order to evaluate our approach, we have conducted experiments using a 1-month trace of a Google data centre with more than twelve thousand machines. our BiLSTM-based approach successfully achieves higher accuracy than other previous models, including the recent LSTM one and LSTM-ED one.
