  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.
