Improved round-robin rule learning for optimal load balancing in distributed cloud systems

Abstract

A newly emerging technology that distributes virtualised computer resources across the internet is referred to as 'cloud computing' and is gaining popularity. These clouds' ability to evenly distribute the load is vital. Load balancing in cloud computing distributes dynamic workloads so no database server is overcrowded or underloaded (LB). As a result, a dynamic load balancing technique in the cloud may contribute to improved service stability and resource utilisation. In this study, we model a load balancing system that balances the cloud's available resources using a rule-based round-robin method. Cloud parameters are used to calculate and allocate resources using a rule-based round-robin technique. The study analyses cloud metrics to determine cloud loads and resources. The cloud sim tool is used to allocate resources, and its effectiveness is tested against a variety of performance measures such as overhead, migration time, and throughput rate. Simulation results show that rule-based round-robin improves cloud performance. When compared to existing algorithms, the proposed methodology improves task performance by about 4%.