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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini3, Sahil Verma3, Kavita3, Ruba Abu Khurma4,5, Pedro A. Castillo6,*

1 University Institute of Computing Department, Chandigarh University, Punjab, 140413, India
2 Department of Computer Science and Engineering, Chandigarh University, Punjab, 140413, India
3 Universidade Federal do Piauí, Teresina, Piauí, 64049-550, Brazil
4 MEU Research Unit, Faculty of Information Technology, Middle East University, Amman, 11831, Jordan
5 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
6 Department of Computer Engineering, Automatics and Robotics, University of Granada, Granada, 18071, Spain

* Corresponding Author: Pedro A. Castillo. Email: email

Computers, Materials & Continua 2024, 79(3), 3757-3782. https://doi.org/10.32604/cmc.2024.046516

Abstract

Cloud computing is a dynamic and rapidly evolving field, where the demand for resources fluctuates continuously. This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments. The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently. By adhering to the proposed resource allocation method, we aim to achieve a substantial reduction in energy consumption. This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most, aligning with the broader goal of sustainable and eco-friendly cloud computing systems. To enhance the resource allocation process, we introduce a novel knowledge-based optimization algorithm. In this study, we rigorously evaluate its efficacy by comparing it to existing algorithms, including the Flower Pollination Algorithm (FPA), Spark Lion Whale Optimization (SLWO), and Firefly Algorithm. Our findings reveal that our proposed algorithm, Knowledge Based Flower Pollination Algorithm (KB-FPA), consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction. This paper underscores the profound significance of resource allocation in the realm of cloud computing. By addressing the critical issue of adaptability and energy efficiency, it lays the groundwork for a more sustainable future in cloud computing systems. Our contribution to the field lies in the introduction of a new resource allocation strategy, offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

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Cite This Article

APA Style
Chauhan, N., Kaur, N., Saini, K.S., Verma, S., Kavita, et al. (2024). Maximizing resource efficiency in cloud data centers through knowledge-based flower pollination algorithm (KB-FPA). Computers, Materials & Continua, 79(3), 3757-3782. https://doi.org/10.32604/cmc.2024.046516
Vancouver Style
Chauhan N, Kaur N, Saini KS, Verma S, Kavita , Khurma RA, et al. Maximizing resource efficiency in cloud data centers through knowledge-based flower pollination algorithm (KB-FPA). Comput Mater Contin. 2024;79(3):3757-3782 https://doi.org/10.32604/cmc.2024.046516
IEEE Style
N. Chauhan et al., "Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)," Comput. Mater. Contin., vol. 79, no. 3, pp. 3757-3782. 2024. https://doi.org/10.32604/cmc.2024.046516



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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