ABSTRACT
When n number of IoT applications arrive at servers, then rearranging and scheduling them in such a way to obtain the most optimal criteria is termed as Task Scheduling. The Optimal criterion may be the least delays, low energy consumption and cost, maximum resource utilization, etc. Because a fog has limited storage and processing capacity, these criteria become bottlenecks when these IoT applications reach the Fog scenario. As a result, under the Fog environment, all real-time applications cannot be scheduled. It is also necessary to allocate these resources in the most efficient manner feasible. As a result, it is recommended that mission-critical applications be scheduled on the fog and non-mission-critical applications be scheduled in the cloud. This paper surveys the various conventional methods as well as machine learning algorithms and meta-heuristic approaches adopted for placing the applications and allocating the resources to them in the fog-cloud scenarios proposed by different researchers.
