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

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

Sudipta Sahana, Tanmoy Mukherjee, Debabrata Sarddar
Copyright: © 2020 |Pages: 14
DOI: 10.4018/IJCAC.2020040102
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Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.
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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.

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