Models for improving of scheduling of task and checking its efficiency in cloud computing

Abstract

Cloud computing is a platform that can enable elastic applications in order to manage a limited number of virtual machines and computing servers to provide application services at a certain point in time. It is necessary to verify and schedule available resources in the cloud using an effective task scheduler so that they may be assigned to customers depending on their demands. Scheduling is the most important task in any working framework, and it is controlled by the CPU. This is due to the fact that resources are not stored in a particularly designated manner, but rather in a strictly handy manner in order to ensure maximum skills. Task scheduling and resource deployment have been unified controlled by cloud computing service providers through the use of virtualized technologies in the cloud computing environment. Cloud computing is a distributed and parallel computing paradigm that is becoming increasingly popular. This type of cloud computing comprises a group of heterogeneous linked datacenters that are reliant on virtualization techniques and that are provided as a dynamically to the customer through a negotiation between cloud service providers and cloud users. In this research The Deadline-Aware Priority Scheduling (DAPS) model will be the first of the scheduling models. When using the Budget-Aware Scheduling (BAS) model, tasks will be scheduled and assigned based on available resources, with the goal of lowering the total time required to complete the tasks while staying within the budget constraints. This tool should be flexible enough to support this environment while also receiving an increasing number of user requests. The experiments were carried out on the BAS model and compared to state-of-the-art scheduling algorithms, demonstrating that the BAS minimizes the makespan, response time, and number of violations for task execution on VMs, as well as increasing resource utilization and provider profit, and achieving an acceptable total gain cost for any user. Simulation results indicate that the Deadl