The quality of human life increases with increased usage of the Internet of Things (IoT). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.

Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.

The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.

Since our work is related to the cloud fog environment, we have uploaded the relevant coding for the readers reference. The code is basically written in JAVA based ifog simulator. 

All the relevent information are expilicitly illustrated in the manuscript. See build.xml for examples of how to customize the build. If further details are required, the author would be happy to fully address.