Designing a Hybrid Load Balancing Algorithm for Optimized Resource Allocation in Cloud Environments Using Python

Abstract

To keep up with fluctuating workloads and guarantee optimal system performance, effective resource management strategies are essential in the ever-expanding world of cloud computing. This research presents a hybrid load balancing approach to improve resource allocation in cloud environments. The proposed algorithm combines techniques such as genetic algorithms (GA) and machine learning (ML) with traditional approaches like round-robin and least-connections. By using the strengths of both approaches, the hybrid algorithm aims to minimize wasted resources, improve task distribution, and make the system more scalable. To test and refine the hybrid load balancing strategy, this study simulates the cloud environment in Python. In order to optimize both energy efficiency and performance, the proposed algorithm dynamically modifies resource allocation depending on real-time workload circumstances. When compared to more conventional load balancing methods, the proposed hybrid algorithm shows considerable improvements in all three metrics such as load distribution, task completion time, and resource utilization. The results show that hybrid cloud systems, which use both traditional and advanced load balancing strategies, are better than existing methods.