Cyber Times International Journal of Technology & Management

Cloud computing has revolutionized resource management in contemporary computing environments. Task scheduling is essential for efficiently allocating and utilizing cloud resources. In cloud environments, tasks are assigned based on availability, capacity, and priority. A comprehensive study of cloud task scheduling approaches, algorithms, and strategies is presented. In this study, we investigate performance, scalability, and resource utilization challenges, advantages, and limitations associated with each method. It also examines recent advancements made in task-scheduling mechanisms. In particular, machine learning and artificial intelligence techniques are incorporated to implement intelligent and dynamic scheduling methods. These algorithms adapt to changing workload conditions, optimize resource usage, and enhance performance. Additionally, cloud computing task scheduling mechanisms are evaluated on the basis of load balancing, energy efficiency, fault tolerance, and compliance. Several task scheduling mechanisms have been proposed and implemented in cloud computing. Scheduling methods include priority-based, load-balancing, metaheuristic, elastic, and machine-learning-based schedules. Although each mechanism has its advantages and limitations, their common goal is to improve system performance and load balancing. Nevertheless, cloud computing task scheduling still faces challenges like the dynamic nature of cloud environments, resource heterogeneity, workload variation, and resource contention. However, task scheduling mechanisms have made significant progress in improving resource allocation.