A Novel Multi‐Objective Task Scheduling Using Hybrid Drawer‐Mother Optimization Algorithm in Cloud Computing

ABSTRACT Due to the emergence of cloud and its functions, it provides adaptable and dynamic scaled computing power used at a reasonable price. Efficiently assigning tasks with high computational resources to the cloud server is a critical problem that must be addressed to enhance system efficiency and ensure the satisfaction of cloud users. This process involves the consideration of factors such as the availability of resources, task priority, and dependencies among tasks. Despite the fact that there are numerous task‐planning algorithms, current methods mostly concentrate on reducing the total time taken to finish while disregarding load balance. With the aid of available assets, cloud computing has proven to be an effective technique for providing services to the customers. Due to the heavy stress on the assets, the network performance eventually suffers. One of the more challenging factors in the cloud is the effective use of the available computing power. This necessitates the creation of a task‐scheduling approach that is effective and efficient; thus, it has the potential to significantly impact the online computing system's functionality and performance as a whole. In a dynamic way, the scheduling process becomes critical while changing the environmental structure and managing the virtual computers in an optimal manner. Though several models are implemented for improving scheduling tasks in cloud environments, the problem is still unresolved. Additionally, the manual scheduling does not provide a feasible solution. To combat the above difficulties, a novel task scheduling process is to be carried out. Hence, a new optimal task scheduling model is developed using hybrid optimization algorithms for assigning the tasks to the machines on several assumptions. Here, the task scheduling process is executed via the hybridization of Iterative Concept of Drawer and Mother Optimization (ICDMO). This optimization algorithm allocates the tasks after checking whether the machines are in an idle state or not. During the task scheduling, a few multi‐objective constraints like makespan, energy, cost, active servers, throughput, and resource utilization are considered to enhance the performance. The developed model's efficiency is determined with the conventional task scheduling approaches to find the effectiveness of the developed task scheduling model.