MTFP: matrix-based task-fog pairing method for task scheduling in fog computing

In the present era of seamless connectivity which demands enormous smart devices to be allied and send data to the cloud, it seems imperative to organize and process cloud-based smart Internet of Things (IoT) applications in real time. Hence, to support the continuous demand for the scheduling of real-time latency-sensitive tasks; the adaptability of fog computing is necessary which provide close adjacency to the tasks generating sources. Fog computing ensures optimal scheduling of latency-sensitive tasks by appropriate resource allocation considering dynamic user requirements. But the process of scheduling is an open challenge due to limited availability and processing capacity of fog resources. Further, provisioning of an appropriate fog resource is also necessary for timely execution of tasks. Hence, the papers present a novel task-scheduling heuristic algorithm; Matrix-based Task-Fog Pairing (MTFP) that aim to provide a feasible solution for fog resource provisioning to latency sensitive tasks. The algorithm worked on two different matrixes called compatibility and execution time matrix for scheduling priority tasks in order to achieve the desired Quality of Experience (QoE) to the end-user. Finally, the proposed algorithm MTFP is compared with the present state-of-the-art and shown improvement in term of reducing tasks execution time by 18%, delay by 16% and energy consumption by 14.5%.