Adaptive Prediction of Resources and Workloads for Cloud Computing Systems with Attention-based and Hybrid LSTM

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high performance in predicting resource usage and workload sequences. Much noise implicit in the original sequences of resources and workloads is another reason for their low performance. To address these problems, this work proposes a hybrid prediction model named SABG that integrates an adaptive Savitzky-Golay (SG) filter, Attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. SABG adopts an adaptive SG filter in the data pre-processing to eliminate noise and extreme points in the original time series. It uses bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Then, it utilizes an attention mechanism to explore importance of different data dimensions. SABG aims to predict resource usage and workloads in highly variable traces in cloud computing systems. Extensive experimental results demonstrate that SABG achieves higher-accuracy prediction than several benchmark prediction approaches with datasets from Google cluster traces.