Abstract:
Every day, an increasing number of Internet of Everything (IoE) devices generate enormous volumes of data. Cloud computing provides processing, analysis, and storage services to handle these kinds of large data quantities. The increasing latency and bandwidth consumption are unacceptable for real-time applications such as smart healthcare devices, online gaming, and video monitoring. In order to tackle the rise in latency as well as bandwidth usage in cloud computing technology, FC (Fog Computing) has been developed. At the periphery of a network, FC offers networking, processing, storage, and analytics functions. Since FC is still in its infancy, scheduling jobs and allocating resources are two of its major issues. With the help of this innovation, there are resource limitations on the fog devices at the network's edge. Consequently, choosing a fog node for a job's assignment and scheduling is crucial. The energy usage and application request response time can be decreased with an intelligent and effective work scheduling algorithm. This research presents a novel Quality of Service Priority Tuple Scheduling (QoSPTS) scheduler that maximizes network capacity and latency while supporting service provisioning for the IoE. Presented here is a case study showing the effective management of IoE device requirements, efficiently allocating resources across fog devices, and optimizing scheduling to enhance quality of life. Taking energy efficiency and latency into account as performance measures, here, using iFogSim to compare the suggested scheduling algorithm to other methods. According to the findings, the suggested scheduler's latency and network bandwidth improved by 34% and 18%, respectively, in comparison with the FCFS (First Come First Serve) approach.
