An EHO-Grounded Task Planning to Improve Resource Application in Cloud Computing
Keywords:
EHOTS, GA, PSO, OptimizationAbstract
In cloud computing, job scheduling is closely related to processing costs and resource use. To ensure the best work completion, a variety of optimal task scheduling techniques make good use of these parameters. Here, a task scheduling method called Elephant Herding Optimization based Task Scheduling (EHOTS) is used to give users with the best possible scheduling of their work to increase resource utilization and cut costs associated with processing in a cloud computing environment. The EHOTS is set up to perform an objective function using a variety of variables, including load and processing. The experiment is carried out using an appropriate tool, and simulation results have shown that EHOTS performs better than GA and PSO methods in terms of quality performance based on total cost with minimum and maximum numbers of repetitions and jobs. Task scheduling especially increases the cloud-based system's source utilization and processing costs. To provide optimal scheduling, numerous optimization methodologies are used to enhance task scheduling performance. Based on the scalability and price of resource distribution dynamically, the best job scheduling has improved cloud computing efficiency. To improve the exploration and exploitation capability of the search space and produce the best solution, oppositional logic is also integrated with optimization techniques.
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