Metaheuristic-Driven Optimization for Efficient Resource Allocation in Cloud Environments
DOI:
https://doi.org/10.48047/3atjys28Keywords:
Resource Allocation, Cloud Environment, Metaheuristic, Load Balancing.Abstract
Cloud computing offers diverse resource allocation schemes tailored to meet user requirements. Still, ensuring reliability remains a critical challenge in this dynamic environment. This paper contributes an innovative approach by presenting an efficient and dependable resource allocation framework specifically designed for the complexities of cloud environments. By harnessing the
power of metaheuristic optimization algorithms, this research endeavors to significantly elevate resource utilization beyond the limitations of traditional techniques. Through comprehensive simulations and extensive evaluations, the results unmistakably demonstrate the pronounced effectiveness of the proposed method. The reductions observed in network overhead, average
response time, and memory load validate the robustness and practicality of this heuristic scheme. Hence, this framework not only presents a promising solution but also stands as a well-crafted and forward-thinking strategy, poised to address the evolving demands of stakeholders within the realm of cloud computing.
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References
P. Kumar, A. Tharad, U. Mukhammadjonov and S. Rawat, "Analysis on Resource Allocation for parallel processing and Scheduling in Cloud Computing," IEEE, 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2021, pp. 1-6.
D. K. Jain, S. K. S. Tyagi, S. Neelakandan, M. Prakash and L. Natrayan, "Metaheuristic Optimization-Based Resource Allocation Technique for Cybertwin-Driven 6G on IoE Environment," in IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4884-4892, July 2022.
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