Abstract
The Internet of Things (IoT) paradigm is at the forefront of the present and future research activities. The enormous amount of sensing data needing to be processed increases dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing the data. The fog computing technology is a model used to support cloud computing by implementing pre-processing tasks close to the end-user for achieving low latency, less power consumption, and high scalability. However, some resources in fog computing network are not suitable for some tasks, or the number of requests increases outside capacity. So, it is more efficient to reduce sending tasks to the cloud. Perhaps some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud. This issue affects the fog environment's performance when dealing with large applications or applications sensitive to time processing. This research aims to propose a holistic fog-based resource management model to efficiently discover all the available services placed in resources considering their capabilities, deploy jobs into appropriate resources in the network effectively, and improve the IoT environment's performance. Our proposed model consists of three main components: job scheduling, job placement, and mobile agent software, explained in detail in this paper.