Open Access
ARTICLE
FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers
Yang Yang1,*, Jing Dong1, Chao Fang2, Ping Xie3, Na An3
1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
2 Beijing Smartchip Microelectronics Technology Company Limited, Beijing, China
3 The 54th Research Institute of CETC, Shijiazhuang, China
* Corresponding Author: Yang Yang. Email:
Computer Modeling in Engineering & Sciences 2020, 123(3), 1015-1031. https://doi.org/10.32604/cmes.2020.09404
Received 10 December 2019; Accepted 17 March 2020; Issue published 28 May 2020
Abstract
The development of cloud computing and virtualization technology has
brought great challenges to the reliability of data center services. Data centers
typically contain a large number of compute and storage nodes which may fail
and affect the quality of service. Failure prediction is an important means of
ensuring service availability. Predicting node failure in cloud-based data centers
is challenging because the failure symptoms reflected have complex characteristics, and the distribution imbalance between the failure sample and the normal
sample is widespread, resulting in inaccurate failure prediction. Targeting these
challenges, this paper proposes a novel failure prediction method FP-STE (Failure
Prediction based on Spatio-temporal Feature Extraction). Firstly, an improved
recurrent neural network HW-GRU (Improved GRU based on HighWay network)
and a convolutional neural network CNN are used to extract the temporal features
and spatial features of multivariate data respectively to increase the discrimination
of different types of failure symptoms which improves the accuracy of prediction.
Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the
future. SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning. Experimental results
based on real data sets confirm the effectiveness and superiority of FP-STE.
Keywords
Cite This Article
Yang, Y., Dong, J., Fang, C., Xie, P., An, N. (2020). FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers.
CMES-Computer Modeling in Engineering & Sciences, 123(3), 1015–1031. https://doi.org/10.32604/cmes.2020.09404