QoS and reliability aware matched bald eagle task scheduling framework based on IoT-cloud in educational applications

Cloud computing is a popular paradigm that enables on-demand access to shared resources over the internet. Task scheduling is an important aspect of cloud computing that involves allocating resources to tasks in an efficient manner. The rapid growth of cloud computing has led to an increasing demand for efficient task scheduling algorithms. In cloud computing, task scheduling is critical for achieving high performance and resource utilization, while minimizing costs. However, traditional task scheduling algorithms often struggle to handle the intricacy and fluctuation in cloud computing environments. Therefore, a novel task scheduling framework called Matched Bald Eagle (MABLE) task scheduling framework for Cloud Computing to schedule tasks on Virtual Machines (VMs) in a cloud environment. The proposed framework consists of three major phases: matching, sorting and scheduling. The matching phase identifies the most suitable VMs for each task, while the sorting phase prioritizes the tasks based on their requirements and the types of VMs available. Finally, the scheduling phase uses the Enhanced Bald Eagle optimization (EBEO) algorithm in scheduling tasks on the chosen VMs. The simulated MABLE technique is proposed and its performance is compared with existing methods in terms of load balance, cost, resource utilization and makespan under two different scenarios. The outcomes demonstrate that the MABLE method outperforms existing techniques and is an efficient task scheduling framework for cloud computing.