A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

Related Work

A research survey (Nayak, 2018) has been conducted about a load balancing model whose aim was to circulate the load in a uniform manner across all the nodes via virtualization through which dynamism and flexible scaling could also be achieved. Load balancing algorithms such as carton, compare and balance, event-driven, biased random sampling, active clustering, etc., were also discussed in this paper. Some of the functionalities of the hospital data management (HDM) were also discussed.

The authors (Rajani, 2018) talked about a task-based approach towards load balancing (TB-LB) in a Cloud environment based on clustering of virtual machines and heuristic algorithms. The proposed system solved the issue of population based and non-population-based problems. The combination of three heuristic algorithms, namely simulated annealing, particle swarm optimization and genetic algorithm was used to balance the load and the examination of task requirement helped in minimizing the make span of tasks and system performance. The virtual machines were organized into groups and the execution time got reduced, thanks to the k-means clustering approach. Through the proposed model, there was an enhancement of system performance through the reduction of make span and execution time.