RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environment

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively.