Metaheuristic Optimization for Dynamic Task Scheduling in Cloud Computing Environments

Abstract

Cloud computing enables the sharing of resources across the Internet in a highly adaptable and quantifiable way. This technology allows users to access customizable distributed resources and offers various services for resource allocation, scientific operations, and service computing via virtualization. Effectively allocating tasks to available resources is essential to providing reliable consumer performance. Task scheduling in cloud computing models presents substantial challenges as it necessitates an efficient scheduler to map multiple tasks from numerous sources and dynamically distribute resources to users based on their requirements. This study presents a metaheuristic optimization methodology that integrates load balancing by dynamically distributing tasks across available resources based on current load conditions. This ensures an even distribution of workloads, preventing resource bottlenecks and enhancing overall system performance. The suggested method is suitable for both constant and variable activities. Our technique was compared with established metaheuristic methods, including HDD-PLB, HG-GSA, and CAAH. The proposed method demonstrated superior performance due to its adaptive load balancing mechanism and efficient resource utilization, reducing task completion times and improving overall system throughput.