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Energy-aware QoS-based dynamic virtual machine consolidation approach based on RL and ANN

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Abstract

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency.

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Acknowledgments

This work was partially supported by the FCT (Fundação para a Ciência e a Tecnologia, Portugal) through the project UIDB/50021/2020.

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Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mahshid Rezakhani & Ali Movaghar

  2. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Nazanin Sarrafzadeh-Ghadimi & Reza Entezari-Maleki

  3. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Reza Entezari-Maleki

  4. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Reza Entezari-Maleki & Leonel Sousa

Authors
  1. Mahshid Rezakhani
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  2. Nazanin Sarrafzadeh-Ghadimi
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  3. Reza Entezari-Maleki
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  4. Leonel Sousa
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  5. Ali Movaghar
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Contributions

MR: Conceptualization, methodology, validation, formal analysis, investigation, data curation, writing the original draft. NS-G: Conceptualization, writing, review, and editing. RE-M: Conceptualization, methodology, review, and editing, supervision. LS: Conceptualization, methodology, review, and editing. AM: Conceptualization and supervision. All authors reviewed the manuscript.

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Correspondence to Reza Entezari-Maleki.

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Rezakhani, M., Sarrafzadeh-Ghadimi, N., Entezari-Maleki, R. et al. Energy-aware QoS-based dynamic virtual machine consolidation approach based on RL and ANN. Cluster Comput 27, 827–843 (2024). https://doi.org/10.1007/s10586-023-03983-2

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