AI-Based Security: Effective Load Balancing and Optimum Distribution of Resources for Enhancing Financial Cloud

Financial cloud computing is based on the virtualization technology paradigm, which has recently gained prominence in the information technology (IT) industry. The resource allocation in the existing system is not guaranteed, and convergence problems occasionally cause the process to move slowly. Consequently, there has been a notable decline in the overall efficacy of financial cloud computing. In this work, new methods are presented to improve the financial cloud. The three main stages of the proposed system are resource allocation, load balancing and cost-effective virtual machine (VM) migration. To enhance load balancing, this study employs the Enhanced Weighted Round Robin Algorithm (EWRR) technique. Load balancing is achieved by shifting workloads from overloaded to underloaded nodes. The Differential Evaluation-based Bat Algorithm (DEBA) efficiently chooses more optimal resources to accomplish the optimal resource allocation in the financial cloud. Resolute Support Vector Machine (RSVM) technique is utilized to Provide Cost-effective VM migration. The simulation's findings show that the recommended DEBA-EWRR algorithm performs better than existing techniques due to advancements in throughput, Makes pan, Fitness score and resource utilization.