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Link to original content: https://doi.org/10.1007/s10586-024-04665-3
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A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

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Abstract

Cloud computing enables global access to tool-based IT services, accommodating a wide range of applications across consumer, scientific, and commercial sectors, operating on a pay-per-use model. However, the substantial energy consumption of data centers hosting cloud applications leads to significant operational costs and environmental impact due to carbon emissions. Each day, these centers handle numerous requests from diverse users, necessitating powerful servers that consume substantial energy and associated peripherals. Efficient resource utilization is essential for mitigating energy consumption in cloud centers. In our research, we adopted a novel hybrid approach to dynamically allocate resources in the cloud, focusing on energy reduction and load prediction. Specifically, we employed neural fuzzy systems for load prediction and the ant colony optimization algorithm for virtual machine migration. Comparative analysis against existing literature demonstrates the effectiveness of our approach. Across 810 time periods, our method exhibits an average resource loss reduction of 21.3% and a 5.6% lower average request denial rate compared to alternative strategies. Using the PlanetLab workload and the created CloudSim simulator, the suggested methods have been assessed. Moreover, our methodology was validated through comprehensive experiments using the SPECpower benchmark, achieving over 98% accuracy in forecasting energy consumption for the proposed model. These results underscore the practicality and efficiency of our strategy in optimizing cloud resource management while addressing energy efficiency challenges in data center operations.

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Acknowledgements

Project Title: Development and Application of a Learning Assistant System for the Course “Fundamentals of Computer Applications” (Project Number: GJJ203803), funded by the Science and Technology Research Department of Jiangxi Provincial Department of Education.

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The authors did not receive any financial support for this study.

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Authors and Affiliations

  1. School of Mathematics and Science, Fuzhou Preschool Education College, Fuzhou, 344000, Jiangxi, China

    Hong Huang & Hong Wang

  2. Department of General Basic Courses Teaching, Fuzhou Preschool Education College, Fuzhou, 344000, Jiangxi, China

    Yu Wang

  3. Hubei Preschool Teachers College, Wuhan, 430000, Hubei, China

    Yue Cai

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All authors contributed to the study conception and design. Data collection, simulation and analysis were performed by “Hong Huang and Yu Wang”. The first draft of the manuscript was written by “Yue Cai and Hong Wang” and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yu Wang.

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Huang, H., Wang, Y., Cai, Y. et al. A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems. Cluster Comput 27, 14515–14538 (2024). https://doi.org/10.1007/s10586-024-04665-3

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