A Fog Computing Framework for Quality of Service Optimisation in the Internet of Things (IoT) Ecosystem

Fog computing plays a pivotal role in the Internet of Things (IoT) ecosystem because of its ability to support delay-sensitive tasks, bringing resources from cloud servers closer to the “ground” to support IoT devices that are resource-constrained. Although fog computing offers a lot of benefits such as quick response to requests, geo-distributed data processing and data processing in the proximity of the IoT devices, the exponential increase of IoT devices and large volumes of data being generated has led to a new set of challenges. One such challenge is the allocation of resources to IoT tasks to match their computational needs and QoS requirements whilst meeting task deadlines. Most proposed solutions in existing works suggest task offloading mechanisms where IoT devices would offload their tasks randomly to the fog layer. Of course, this helps in minimizing the communication delay, however, most tasks would end up missing their deadlines as many delays are experienced when fog node is deciding to process part of the task or offloading it to the next fog node. In this paper, we propose and introduce a Resource Allocation Scheduler (RAS) at the IoT-Fog gateway whose goal is to decide where and when a task is to be offloaded either to the fog layer or the cloud layer based on their priority needs, computational needs and QoS requirements and minimize round-trip time. The study followed the four phases of the top-down methodology. To test the efficiency and effectiveness of the RAS, a model was evaluated in a simulated smart home setup. The important metrics that were used are the queuing time, offloading time and throughput. The results showed that RAS helps in minimizing the round-trip time, increase throughput and improve QoS. Furthermore, the approach addressed the starvation problem, which was affecting low priority tasks. Most importantly, the results provide evidence that if resource allocation and assignment are done properly, round-trip time (queuing time and offloading time) can be reduced and QoS can be improved in fog computing.