Improvement in task allocation for VM and reduction of Makespan in IaaS model for cloud computing

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters.