Dependency, Deadline and Priority Aware Multi-Queue Dynamic Task Scheduling Using Heterogeneous Resources in Fog Environment

Internet of Things (IoT) has emerged as a transformative paradigm and revolutionized our way of engaging with technology in everyday life. The limited computing resources and storage capacity of IoT devices have necessitated the integration of heterogeneous fog resources in IoT task execution. Moreover, some IoT applications such as healthcare services are highly delay-sensitive; hence need immediate processing and response. On the other hand, the best utilization of heterogeneous storage and processing capacity of fog resources plays a pivotal role in improving the response time of IoT applications. Thus, an adaptive task scheduling algorithm for executing IoT tasks in a fog environment is required which will consider the heterogeneity of available resources and various response time requirements of different IoT applications. In this paper, we propose a non-preemptive batch task scheduling algorithm with dynamic resource allocation. The proposed method addresses the challenges of scheduling tasks with varying priority levels, deadline constraints, and dependencies utilizing the concepts of Directed Acyclic Graphs (DAGs), priority queues, and dynamic queue switching. We introduce a novel numerical scoring mechanism that prioritizes the execution of tasks with higher urgency and critical dependencies. The major goals of the proposed technique are to reduce the overall average response time, makespan, and increase the throughput. We have implemented our algorithm in a Java based proprietary simulator. Our simulation results demonstrate that the proposed algorithm significantly improves the average response time, makespan and throughput compared to the existing priority based batch scheduling algorithms. Precisely, our algorithm reduces average response time by at least 50.2%, makespan by at least 8.18%, and increases throughput by at least 13%, task completion rate by at least 4.3% compared to Shortest Job First (SJF) and Multi Level Feedback Queue (MLFQ).