DRABC-LB: A Novel Resource-Aware Load Balancing Algorithm Based on Dynamic Artificial Bee Colony for Dynamic Resource Allocation in Cloud

The purpose of this research is to propose a dynamic configuration of resources in a cloud computing (CC) environment by using dynamic optimization method, and to provide task- and resource-aware scheduling techniques for the efficient mapping of tasks on VMs (virtual machines) in cloud data centres (DCs). To equal out resource allocation among virtual machines (VMs), it is necessary to devise an unique load balancing (LB) system architecture predicated on a dynamic optimization technique with a RALBA mechanism. Therefore, a novel DRABC-LB (Dynamic Resource-To assure a balanced workload distribution relying on the computational capacities of VMs in a CC environment, a new algorithm (called a "Aware Load Balancing Method based on Dynamic ABC") is presented. This algorithm works in three phases: resource filling scheduler, optimized spill scheduler, and dynamic updater. In the first phase, it performs scheduling depending on the computing capabilities of VMs. In the second phase, a VM with an initial finish time is chosen for task mapping. The last phase uses the dynamic ABC optimization technique to optimize TS (task scheduling) and update the allocation. The experiments have been performed with a realistic GoCJ workload and a synthetic workload of cloud data. The DRABC-LB surpasses the traditional DRALBA method on every metric studied, including average response time, make span, throughput, and resource usage ratio, when applied to cloud data.