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Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism

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Abstract

The resources required to service cloud computing applications are dynamic and fluctuate over time in response to variations in the volume of incoming requests. Proactive autoscaling techniques attempt to predict future resource demand and assign resources before the request. Thus, establishing accurate prediction methods optimizes resource allocation and avoids service-level agreement violations (SLA). Most existing methods for predicting resource usage in cloud computing rely on univariate time series prediction models. These models consider only a single resource usage metric, make future predictions based on historical data for the target metric, and provide predictions for a single step ahead. This paper provides a hybrid method for multivariate time series workload prediction of host machines in cloud data centers that predicts the workload for the next few steps. First, a statistical analysis is used to construct the training set. Then, a convolutional neural network (CNN) is employed to extract the hidden spatial features between all correlated variables. Finally, the spatial features extracted by the CNN are fed into a GRU network optimized with the attention mechanism in order to extract the temporal correlation features. Two experiments employing Google cluster data were conducted to evaluate the proposed method. Experimental results reveal that our method improves prediction accuracy by 2% to 28% compared to baseline methods and previous research.

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Data Availability

The dataset analyzed during experiments in the current study is available at https://github.com/google/cluster-data.

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Authors and Affiliations

  1. Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Mollasadara St., Shiraz, 71348-5115, Iran

    Javad Dogani, Farshad Khunjush & Mehdi Seydali

  2. Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran

    Mohammad Reza Mahmoudi

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  1. Javad Dogani
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  2. Farshad Khunjush
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Contributions

JD contributed to methodology, software, simulation, and writing—original draft. FK contributed to conceptualization, validation, methodology, and writing—review and editing. MRM contributed to statistical analysis, validation, and methodology. MS contributed to conceptualization, software, and writing—review and editing.

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Correspondence to Farshad Khunjush.

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Dogani, J., Khunjush, F., Mahmoudi, M.R. et al. Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism. J Supercomput 79, 3437–3470 (2023). https://doi.org/10.1007/s11227-022-04782-z

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