Research Area:  Edge Computing

Abstract:

Workload prediction is a fundamental task in edge data centers, which aims to accurately estimate the workload to achieve an in-situ resource provisioning for workload execution. In this paper, we propose a deep learning model termed SG-CBA to predict workload, which is powered by Savitzky-Golay filter (SG filter), Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with Attention mechanism. First, raw time series of the workload is normalized and smoothed by a preprocessing module with SG filter. Following that, we establish a deep learning module based on CNN and BiLSTM with attention mechanism to extract and process the features for the accurate workload prediction. Real-world workload from Alibaba cluster is adopted to validate our proposed model in the experiments. Experimental results demonstrate that SG-CBA can achieve accurate workload prediction, which outperforms the alternatives, including BTH-ARIMA, LSTNet, OCRO-MLNN, Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), LSTM and BiLSTM under different evaluation metrics.

Keywords:  
workload prediction
edge data centers
Recurrent Neural Network
Gated Recurrent Unit
LSTM
deep learning
Convolutional Neural Network
Bidirectional Long Short-Term Memory

Author(s) Name:  Lei Chen, Weiwen Zhang & Haiming Ye

Publisher name:  Springer

DOI:  10.1007/s10489-021-03110-x

Volume Information:  volume 52

Paper Link:   https://link.springer.com/article/10.1007/s10489-021-03110-x