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Synergistic Integration of Graph Neural Networks and Reinforcement Learning for Enhanced Dynamic Resource Allocation in Cloud Computing Environments

Authors: Anirudh Pratap Singh

Published: 2025-04-28 19:15:14.960

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Abstract

Dynamic resource allocation in cloud computing environments is a complex optimization problem, characterized by high dimensionality, non-linearity, and stochastic workload patterns. Traditional methods often struggle to adapt effectively to these challenges, leading to suboptimal resource utilization and performance degradation. This paper proposes a novel approach that synergistically integrates Graph Neural Networks (GNNs) and Reinforcement Learning (RL) to address these limitations. We leverage GNNs to learn rich representations of the cloud infrastructure topology and resource dependencies, enabling a more informed and context-aware decision-making process. These representations are then fed into an RL agent, which learns an optimal policy for dynamic resource allocation through interaction with the cloud environment. We evaluate our approach in a simulated cloud environment, demonstrating significant improvements in resource utilization, task completion time, and overall system performance compared to existing state-of-the-art methods. Our findings highlight the potential of GNN-RL integration for enhancing dynamic resource allocation and improving the efficiency and scalability of cloud computing infrastructure.

Keywords

Graph Neural Networks (GNNs), Reinforcement Learning (RL), Resource Allocation, Cloud Computing, Dynamic Optimization, Deep Learning, Graph Representation, Multi-Agent Systems, Performance Optimization, Distributed Systems.

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How to Cite

Anirudh Pratap Singh, (2025-04-28 19:15:14.960). Synergistic Integration of Graph Neural Networks and Reinforcement Learning for Enhanced Dynamic Resource Allocation in Cloud Computing Environments. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 2.

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