Formal Modelling and Verification of Effective Probabilistic Neural Networks for Load Balancing in a Cloud Environment

Load balancing plays a crucial role in distributed and cloud computing by evenly distributing workloads across multiple servers or network resources, ensuring optimal performance and resource utilization. It improves system reliability, fault tolerance, and response time by preventing overloading and rerouting tasks from failed or underperforming resources. This paper explores advanced load balancing techniques, focusing on machine learning integration for better handling imbalanced data and task distribution. We introduce an Effective Probabilistic Neural Network (EPNN) model that selects the best cluster for load distribution. Complementing this, we propose a Round Robin Assigning Algorithm (RRAA) for task allocation and a Data Discovery Algorithm (DDA) for identifying optimal nodes or clusters. The EPNN model’s accuracy is verified through formal modeling using the Event-B tool, ensuring the correctness of the algorithm via automated and manual proof generation. This research aims to optimize load balancing in neural network environments, offering the highest probability algorithm for efficient resource management.