Abstract

In the current scenario, Fog Computing is a new technology deployed between cloud computing systems and Internet of Things (IoT) devices, to filter out important information from a massive amount of collected IoT data. The IoT-based applications are diverse which include emergence response, video surveillance, smart transportation, traffic control, health care, and smart homes. Cloud computing offers several advantages, but also has the disadvantages of high latency and network congestion, when processing a vast amount of data collected from various devices and sources. For overcoming these problems in fog computing environments, an efficient model is proposed in this article for precise Load Balancing (LB). The proposed fractional selectivity model significantly handles LB in fog computing by reducing network bandwidth consumption, latency, task-waiting time, and also enhances the quality of experience. The proposed fractional selectivity model allocates the required resources by eliminating sleepy, unreferenced, and long-time inactive services. The fractional selectivity model’s performance is investigated on three application scenarios, namely Virtual Reality (VR) game, Electroencephalogram (EEG) healthcare and toy game. The efficiency of the introduced model is analyzed on the basis of its makespan, Average Resource Utilization (ARU), Load Balancing Level (LBL), and total cost. In comparison to the traditional task allocation models, the proposed model reduces almost 5% to 15% of the total cost and makespan time.