Article
Algorithms for Service-Based Task Scheduling in Cloud Computing
Having focused on the maximum utilization of resources, reducing the execution time, and improving the operational efficiency of the system in general, this paper surveyed and analysed the approaches to the service-based scheduling of tasks in cloud computing environments. The effectiveness of the various scheduling methods, including heuristic-based, metaheuristic-based, and machine learning-based scheduling in dynamic workload and various kind of resources will be explored. The paper highlights critical considerations during the design of an algorithm such as the priority of the tasks, time limit constraint, and affordability. Comparative evaluations have shown that adaptive and smart scheduling techniques dynamically assigning work to appropriate virtual machines are better than traditional static algorithms in terms of scalability, fault tolerance, and performance aspects. Its outcomes allow the provision of enhanced service provision and user satisfaction by providing opportunities regarding the selection and formulation of suitable methods of schedule to meet diverse quality of service (QoS) requirements in clouds infrastructures.
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