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Adaptive Neuro-Fuzzy Inference System with Reinforcement Learning for Enhanced Dynamic Resource Allocation in Cloud Computing Environments (P1-P1)

Authors: Aditi Singh

Published: 2025/5/2

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Abstract

Cloud computing environments demand efficient and dynamic resource allocation strategies to meet fluctuating user demands and optimize resource utilization. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with Reinforcement Learning (RL), specifically Q-learning, for enhanced dynamic resource allocation in cloud environments. The proposed system, ANFIS-RL, leverages the learning capabilities of RL to adapt the fuzzy rules of ANFIS, enabling it to dynamically adjust resource allocation policies based on real-time system performance and changing workload patterns. We detail the architecture of ANFIS-RL, the Q-learning algorithm implementation, and the experimental setup used to evaluate its performance. The results demonstrate that ANFIS-RL significantly outperforms traditional rule-based fuzzy systems and standard Q-learning approaches in terms of resource utilization, response time, and overall system efficiency. This hybrid approach offers a robust and adaptive solution for managing the complexities of resource allocation in dynamic cloud environments.

Keywords

Neuro-Fuzzy Systems, Reinforcement Learning, Dynamic Resource Allocation, Cloud Computing, Adaptive Control, Fuzzy Logic, Neural Networks, Q-Learning, Cloud Optimization, Virtual Machine Management

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How to Cite

Aditi Singh , (2025/5/2). Adaptive Neuro-Fuzzy Inference System with Reinforcement Learning for Enhanced Dynamic Resource Allocation in Cloud Computing Environments. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 4.

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