Effective Task Scheduling in Critical Fog Applications Using Critical Task Indexing Scheduler (CTIS)

To decrease time delay, energy consumption, and network utilization in the Internet of Things (IoT) is a key research area. The rapid advancement in IoT, leading to increase in size and cost makes traditional protocols and algorithms unfeasible to work with and requires redevelopment. Effective redevelopment may result in less energy consumption and resource utilization. Task scheduling algorithms on the Fog platform are one of the most time-consuming areas in IoT because it defines flow and queue for task processing. In time-critical applications like healthcare, an immediate response and access is required to the Fog platform. In that framework, this study evaluates a case study of healthcare applications to propose an efficient task scheduling algorithm known as Critical Task Indexing Scheduler (CTIS), that reduces the total time delay, energy consumption, and network utilization by indexing a task according to its criticality determined by its pre classified source. When compared with its predecessors such as First Come First Serve (FCFS), Shortest Job First (SJF), Critical Task First Scheduler (CTFS) and Cloud-only platforms, our proposed algorithm outperforms in all the three major parameters.