The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A reinforcement learning-based GWO-RNN approach for energy efficiency in data centers by minimizing virtual machine migration
Open accessAbstract
In the era of exponential data growth, data centers face a pressing need to manage energy consumption while maintaining performance. Existing methods for optimizing energy efficiency, particularly in the context of virtual machine (VM) migrations, often fall short due to their inability to manage the complex, nonlinear relationships between resource utilization, and energy consumption. Moreover, many rely on static thresholds or single-resource metrics, which do not capture the dynamic and multi-faceted nature of data centers. In contrast, this paper introduces a novel approach that integrates a recurrent neural network (RNN) with a gray wolf optimizer (GWO). Unlike traditional models, this approach predicts future energy consumption using a more comprehensive set of resource metrics and dynamically manages workloads, reducing unnecessary VM migrations. The use of GWO optimizes the RNN's ability to capture nonlinearities, while reinforcement learning allows for continuous improvement based on real-time performance feedback. This novel combination demonstrates high predictive accuracy with minimal energy overhead, reducing the error margin to only 11% compared to optimal solutions. By doing so, the GWO-RNN framework provides a robust, adaptive solution for energy-efficient VM management in cloud environments, offering a significant advancement in the quest for sustainable data centers.
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“Similarly, although concentrating on energy or VM management, algorithms such as GWO-RNN [42] and MODJAYA [45] failed to sufficiently record SLA breaches or sustain load balance.”
From Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement in Cloud Data Centers Using Deep Q-Networks and Agglomerative Clustering (2025) · cites this paper as [42]
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Research works printed but not linked: 6
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Cited by
Alex, M., Ojo, S. O., & Awuor, F. M. (2025) · Computers · doi:10.3390/computers14070280 · Cited by 22
Metadata
- Authors
- Parsafar, P.
- Affiliations
- University of Mazandaran (IR)
- Corresponding author
- Parsafar, Parsa
- ORCID
- Parsafar, Parsa
- Venue
- The Journal of Supercomputing
- Published
- 2024-11-15
- Volume
- 81
- Issue
- 1
- ISSN
- 0920-8542 (ISSN-L)
- 1573-0484
- OpenAlex
- W4404396183
- Cited by
- 9
- References
- 38
- Citations by year
- 2026: 4 · 2025: 5
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- The Journal of Supercomputing (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- data center energy efficiency, virtual machine migration, grey wolf optimizer, recurrent neural networks, reinforcement learning, load forecasting, workload management, resource utilization, data center sustainability, cloud computing
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- Parsafar, P. (2025). A reinforcement learning-based GWO-RNN approach for energy efficiency in data centers by minimizing virtual machine migration. The Journal of Supercomputing. https://doi.org/10.1007/s11227-024-06510-1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
