An Efficient Dynamic Load Balancing and Resource Provisioning Scheme for Cloud Computing Environments

Effective resource management is essential for maximizing performance and guaranteeing adherence to service level agreements (SLAs) in the ever-changing world of cloud computing. While reactive fault tolerance techniques usually handle problems only after they arise, resulting in downtime and inefficiencies, traditional load balancing techniques frequently find it difficult to adjust to changing demands. This study suggests a novel hybrid approach that uses machine learning techniques to combine proactive fault tolerance mechanisms with dynamic load balancing. The system predicts changes in workload and possible errors by examining real-time metrics and previous data, which allows for more efficient resource allocation and a reduction in SLA breaches. According to preliminary findings, resource utilization has improved by more than 80%, and fault recovery times have significantly decreased . By providing a thorough foundation for further study and opening the door for more robust cloud computing systems that put efficiency and dependability first, this work fills in the gaps in literature