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ESNemble: an Echo State Network-based ensemble for workload prediction and resource allocation of Web applications in the cloud

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Workload prediction is an essential prerequisite to allocate resources efficiently and maintain service level agreements in cloud computing environment. However, the best solution for a prediction task may not be a single model due to the challenge of varied characteristics of different systems. Thus, in this work, we propose an ensemble model, namely ESNemble, based on echo state network (ESN) for workload time series forecasting. ESNemble consists of four main steps, including features selection using ESN reservoirs, dimensionality reduction using kernel principal component analysis, features aggregation using matrices concatenation, and regression using least absolute shrinkage and selection operator for final predictions. In addition, necessary hyperparameters for ESNemble are optimized using genetic algorithm. For experimental evaluation, we have used ESNemble to combine five different prediction algorithms on three recent logs extracted from real-world web servers. Through our experimental results, we have shown that ESNemble outperforms all component models in terms of accuracy and resource allocation and presented the running time of our model to show the feasibility of our model in real-world applications.

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This research was supported by International Research and Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning of Korea (2016K1A3A7A03952054), and Smart City R&D project of the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (MOLIT), Ministry of Science and ICT (MSIT) (Grant 18NSPS-B149386-01).

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Correspondence to Hoang Minh Nguyen.

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Nguyen, H.M., Kalra, G., Jun, T.J. et al. ESNemble: an Echo State Network-based ensemble for workload prediction and resource allocation of Web applications in the cloud. J Supercomput 75, 6303–6323 (2019).

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