Dynamic Load Balancing in Cloud Computing Using a Hybrid Bidirectional LSTM-RNN with Deep Learning VM Snapshot Protocols

Dynamic load balancing is the ability in cloud computing to distribute workload across multiple virtual machine (VM), such that the performance of overall performance and resource utilization can be optimized. The proposed system introduces a new hybrid approach of using a Hybrid Bidirectional Long Short-Term Memory RNN (BILSTM-RNN) and a Deep Learning-based VM snapshot Protocol. This approach dynamically schedules tasks in real time based on the prediction of task execution time, task resource requirement and task-VM allocation. This model, therefore, allows the Hybrid Bidirectional LSTM RNN to identify temporal trends in the workload allocation and accurately assign tasks the VM Snapshot Protocol to compensate for system restoration and VM state transfer, increasing the systems transferability of lift-ups and downwards-levelling. The system also leverages these deep learning techniques for better load balancing, quicker processing lags, and throughput. Extensive simulations in a cloud environment show that this model surpasses traditional load balancing algorithms in task scheduling efficiency, scalability, and response time reduction, making it well-suited for large-scale, diverse cloud infrastructures.