A Hybrid Fuzzy-Reinforcement Learning Framework for Dynamic Resource Management in Cloud Computing

Authors

  • R. Pradeep Kumar Reddy, G. Madhavi

Keywords:

Resource Allocation, Fuzzy Logic, Reinforcement Learning, Hybrid Approach, Dynamic Workloads, Resource Utilization, SLA Compliance, Cost Efficiency.

Abstract

This research explores the integration of fuzzy logic and reinforcement learning for enhancing resource allocation efficiency in cloud computing environments. Traditional methods often struggle with the dynamic and uncertain nature of workloads, leading to suboptimal resource utilization and performance. By leveraging fuzzy logic's ability to handle imprecision and uncertainty, coupled with the adaptive learning capabilities of reinforcement learning, our proposed hybrid approach demonstrates significant improvements. Experimental results indicate that the hybrid model achieved an average resource utilization of 85%, reduced average response time to 100 milliseconds, and enhanced cost efficiency to $160 per hour, with SLA compliance reaching 95%. These findings highlight the effectiveness of combining these methodologies, providing a robust solution for dynamic resource management in cloud computing, ultimately improving operational efficiency and user satisfaction.

DOI: https://doi.org/10.17762/ijisae.v7i4.8044

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References

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Published

30.06.2019

How to Cite

R. Pradeep Kumar Reddy. (2019). A Hybrid Fuzzy-Reinforcement Learning Framework for Dynamic Resource Management in Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 7(4), 297–302. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8044

Issue

Vol. 7 No. 4 (2019)

Section

Research Article

License

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