Optimizing Resource Allocation and Load Balancing in Heterogeneous Cloud Computing Environments: A Machine Learning Approach

Abstract

The rapid growth of cloud computing has led to the deployment of diverse and heterogeneous
computing resources, posing significant challenges in terms of resource allocation and load
balancing. Inefficient resource management can result in underutilized infrastructure, degraded
application performance, and increased operational costs. This research paper proposes a novel
machine learning-based approach to optimize resource allocation and load balancing in
heterogeneous cloud computing environments. By leveraging advanced machine learning
techniques, such as deep reinforcement learning and graph neural networks, the proposed
framework learns to make intelligent decisions based on real-time system state and historical data.
The research methodology involves the development of a scalable and adaptive resource allocation
algorithm that considers multiple objectives, including maximizing resource utilization,
minimizing response time, and ensuring fair distribution of workload across heterogeneous
computing nodes. The proposed approach is evaluated through extensive simulations and realworld case studies, demonstrating its effectiveness in improving system performance, reducing
resource wastage, and enhancing the overall efficiency of cloud computing environments. The
study also presents a comprehensive analysis of the trade-offs between different optimization
objectives and provides insights into the scalability and robustness of the proposed framework
under dynamic workload conditions. The findings of this research have significant implications for
cloud service providers and system administrators, enabling them to make informed decisions
regarding resource provisioning, scheduling, and load balancing strategies. By leveraging machine
learning techniques, the proposed approach offers a flexible and adaptable solution to the complex
challenges of resource management in heterogeneous cloud computing environments. This research
contributes to the advancement of intelligent and autonomous cloud computing systems, paving
the way for more efficient, cost-effective, and user-centric cloud services.