A Comprehensive Analysis of optimization of task scheduling algorithms for IOT applications in a Combined Environment of Cloud, Fog and Edge

Optimising the use of resources in data centres requires effective task scheduling on the cloud. As 5G and IoT applications proliferated and produced a vast amount of jobs with strict latency requirements, it became even more difficult. The mid-layer(fog), a layer that complements cloud computing, is born out of this. Because network processing is done closer to end devices and users in fog computing, task latency can be decreased. However, because there aren’t enough resources available, not all the jobs can be scheduled in the fog. Better quality of service can only be attained by using specifically created and fine-tuned scheduling algorithms, as conventional scheduling algorithms frequently fall short in utilising the diverse resources. This research delves into a wide range of aspects about the most recent work scheduling algorithms used in cloud and fog environments. In this study, we are going to analyse various scheduling algothims based on optimization methods Constraints such as energy efficiency, time efficiency and cost efficiency based scheduling. In contrast to a packages of jobs and resources, a larger percentage of tasks are autonomous, and the majority of scheduling algorithms have been found to be dynamic and non- preemptive in nature. From this study various scheduling objectives are derived based on Quality metrics such as makespan, resource utilization, delay, load balancing, and energy consumption.