Optimal Dynamic Load Balancing for Cloud Task Distribution Using Bayesian Model
Keywords:
Cloud computing, load balancing, task allocation, optimization, Bayesian modelAbstract
Cloud computing offers users on-demand services through internet on the basis of pay-per-use model. Due to the increased user, the need for resource sharing and utilization is growing quickly, which presents many difficulties for cloud computing. One of the most crucial aspects of cloud computing is load balancing, which evenly distributes the workload on available resources to avoid overloaded or under-load situation and improve the resource utilization. This paper suggests an optimal dynamic load balancing for task distribution problem in cloud environment using Bayesian Model called as LBBM. The two main components of this model are Load Balancer (LdBr) and Virtual Machine Monitor (VMMr). The LdBr assigns user tasks between available VMs and the VMMr analyze the available VMs and send the current status of each VMs to LdBr for task distribution. This approach optimally allocate task to the selected VM using Bayesian Model which reduces the makespan and increase resource utilization. The proposed LBBM approach is simulated using CloudSim Simulator. Simulation findings clearly demonstrate that the suggested strategy outperforms previous approaches in terms of lowering makespan and improving resource consumption.
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