Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing

Abstract

In a cloud computing environment, tasks are divided among virtual machines (VMs) with different start times, duration and execution periods. Thus, distributing these loads among the virtual machines is crucial, in order to maximize resource utilization and enhance system performance, load balancing must be implemented that ensures balance across all virtual machines (VMs). In the proposed framework, a credit-based resource-aware load balancing scheduling algorithm (HO-CB-RALB-SA) was created using a hybrid Walrus Optimization Algorithm (WOA) and Lyrebird Optimization Algorithm (LOA) for cloud computing. The proposed model is developed by jointly performing both load balancing and task scheduling. This article improves the credit-based load-balancing ideas by integrating a resource-aware strategy and scheduling algorithm. It maintains a balanced system load by evaluating the load as well as processing capacity of every VM through the use of a resource-aware load balancing algorithm. This method functions primarily on two stages which include scheduling dependent on the VM’s processing power. By employing supply and demand criteria to determine which VM has the least amount of load to map jobs or redistribute jobs from overloaded to underloaded VM. For efficient resource management and equitable task distribution among VM, the load balancing method makes use of a resource-aware optimization algorithm. After that, the credit-based scheduling algorithm weights the tasks and applies intelligent resource mapping that considers the computational capacity and demand of each resource. The FILL and SPILL functions in Resource Aware and Load utilize the hybrid Optimization Algorithm to facilitate this mapping. The user tasks are scheduled in a queued based on the length of the task using the FILL and SPILL scheduler algorithm. This algorithm functions with the assistance of the PEFT approach. The optimal threshold values for each VM are selected by evaluating the task based on the fitness function of minimising makespan and cost function using the hybrid Walrus Optimization Algorithm (WOA) and Lyrebird Optimization Algorithm (LOA).The application has been simulated and the QOS parameter, which includes Turn Around Time (TAT), resource utilization, Average Response Time (ART), Makespan Time (MST), Total Execution Time (TET), Total Processing Cost (TPC), and Total Processing Time (TPT) for the 400, 800, 1200, 1600, and 2000 cloudlets, has been determined by utilizing the cloudsim tool. The performance parameters for the proposed HO-CB-RALB-SA and the existing models are evaluated and compared. For the proposed HO-CB-RALB-SA model with 2000 cloudlets, the following parameter values are found: 526.023 ms of MST, 12741.79 ms of TPT, 33422.87$ of TPC, 23770.45 ms of TET, 172.32 ms of ART, 9593 MB of network utilization, 28.1 of energy consumption, 79.9 Mbps of throughput, 5 ms of TAT, 18.6 ms for total waiting time and 17.5% of resource utilization. Based on several performance parameters, the simulation results demonstrate that the HO-CB-RALB-SA strategy is superior to the other two existing models in the cloud environment for efficient resource utilization.