Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization

  • Thieu Nguyen
  • Binh Minh NguyenEmail author
  • Giang Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11436)


In this paper, we present a novel intelligent proactive auto-scaling solution for cloud resource provisioning systems. The solution composes of an improvement variant of functional-link neural network and adaptive bacterial foraging optimization with life-cycle and social learning for proactive resource utilization forecasting as a part of our auto-scaler module. We also propose several mechanisms for processing simultaneously different resource metrics for the system. This enables our auto-scaler to explore hidden relationships between various metrics and thus help make more realistic for scaling decisions. In our system, a decision module is developed based on the cloud Service-Level Agreement (SLA) violation evaluation. We use Google trace dataset to evaluate the proposed solution well as the decision module introduced in this work. The gained experiment results demonstrate that our system is feasible to work in real situations with good performance.


Proactive auto-scaling Functional-link neural network Adaptive bacterial foraging optimization Multivariate time series data Cloud computing Google trace dataset 



This research is supported by Vietnamese MOETs project “Research on developing software framework to integrate IoT gateways for fog computing deployed on multi-cloud environment” No. B2017-BKA-32, Slovak APVV-17-0619 “Urgent Computing for Exascale Data”, and EU H2020-777536 EOSC-hub “Integrating and managing services for the European Open Science Cloud”.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information and Communication TechnologyHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Institute of InformaticsSlovak Academy of SciencesBratislavaSlovakia

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