(untitled) (Statistics, Optimization & Information Computing, 2024)
Nowadays, Internet of Everything (IoE) devices are growing rapidly, producing vast amounts of data. Cloud computing offers processing, analysis, and storage solutions to manage these large data volumes. However, the rising latency and bandwidth usage could be more suitable for real-time applications, including intelligent healthcare devices, online gaming, and surveillance via video. In order to tackle the rise in latency and bandwidth utilization in cloud computing technology, Fog Computing (FC) has been developed as it offers networking, processing, storage, and analytics functions. Since FC is still in its early stage, scheduling jobs and allocating resources are two significant issues. With the help of this innovation, there are resource limitations on the fog devices at the network’s edge. Consequently, scheduling is crucial for choosing a fog node for a job assignment. An intelligent and effective work scheduling algorithm can decrease energy usage and application request response time. This research introduces an innovative Quality of Service Priority Tuple Scheduling (QoSPTS) scheduler that optimizes network capacity and latency while enabling service for the IoE. This case study demonstrates the effective management of IoE device requirements by efficiently allocating resources across fog devices and optimizing scheduling to enhance quality of life. The study uses iFogSim to compare the proposed scheduling algorithm with other methods by considering energy efficiency, network utilization, cost, and latency as performance measures. Results showed that the proposed Scheduler’s latency network bandwidth, energy utilization and cost are highly enhanced compared to traditional approaches such as FCFS, Concurrent, Delay-Priority, and QoS-Aware.