Optimizing Load Balancing and Task Scheduling in Cloud Computing Based on Nature-Inspired Optimization Algorithms

Abstract

A paradigm for high-performance computing services, cloud computing integrates the latest developments in distributed computing, virtualization, load balancing, parallel processing, network storage, and hot backup redundancy. In light of the fact that there is currently no reliable method for DAG task scheduling that guarantees a balanced distribution of resources across nodes, this study suggests an approach for LB algorithms in cloud computing. This research presents a new paradigm for resource selection and job scheduling, which is abstracted from swarm intelligence such as PSO, SA, Cuckoo search, etc. The LBA tackles critical issues with the system under investigation, such as system overhead or resource limits, with the goal of achieving lowest makespan and execution time while concurrently improving resource usage. This component is incorporated in the CloudSim simulation environment where the framework measures performance under different cloud models, task intensity and resource provisioning. Experimental outcomes prove that an LBA is better than an existing algorithm, where it gives an average makespan of 894.85ms, execution time of 614.88ms, and resource utilization was 69%. Comparative analysis with PSO and CSSA confirms the superior efficiency of LBA in maximizing resource allocation. These results highlight the possibility of optimization methods derived from nature to improve cloud performance by means of efficient scheduling and load balancing.