An Improved LSTM-Based Prediction Approach for Resources and Workload in Large-Scale Data Centers

Accurate workload and resource prediction are critical for realizing proactive, dynamic, and self-adaptive resource allocation for building cost-effective, energy-efficient, and green cloud data centers (CDCs), providing satisfactory quality services to users and high revenue to cloud providers. It is challenging because patterns of dramatically increasing and large-scale workload and resource usage in CDCs vary significantly with time. Current prediction methods often fail to handle implicit noise data and capture nonlinear, long and short-term, and spatial characteristics in workload and resource time series, thus leading to limited prediction accuracy. To tackle these issues, this work designs a novel prediction approach named VSBG that seamlessly and innovatively combines variational mode decomposition (VMD), Savitzky Golay (SG) filter, bi-directional long short-term memory (LSTM), and grid LSTM to predict workload and resource usage in CDCs accurately. VSBG innovatively integrates VMD and an SG filter in a four-step manner before performing its prediction. VSBG leverages VMD to divide nonstationary workload and resource time series into multiple mode functions. Then, in VSBG, this work designs a quadratic penalty, minimizes it with a Lagrangian multiplier, and adopts a logarithmic operation and the SG filter to smooth the first mode function to eliminate noise interference. Finally, VSBG, for the first time, systematically captures both depth and temporal characteristics of fluctuating and complex time series data with two BiLSTM layers, between which a GridLSTM layer lies, thereby accurately predicting workload and resources in CDCs. Extensive experiments with different real-world data sets prove that VSBG outperforms a holistic set of state-of-the-art algorithms on prediction accuracy and convergence speed.