Data analysis for efficient dynamic IoT task scheduling in a simulated edge cloud environment

Abstract Applications of modern Internet of Things (IoT) devices generate many tasks that are often real-time and require short response times and elimination of delays. Edge and Fog computing are suitable platforms for processing IoT applications. The contribution of this article is the design and implementation of a new algorithm, improved earliest deadline first (EDF), which provides pseudo-optimal scheduling of IoT tasks on microcloud computing machines by minimizing the completion time of the last task in the schedule (makespan). We experimentally evaluated the algorithm on scaled sets of up to 5000 tasks. The new algorithm ensures that all scheduled IoT tasks meet their deadlines as required and optimizes the completion time of the last task of the schedule significantly better than the original EDF algorithm. In the next part of this article, we focus on the analysis of scheduling large, scaled sets of up to 20000 tasks. Our novel approach consists of a comprehensive analysis and a method for finding task schedules that satisfy the required task deadlines while optimizing two other key parameters, which are makespan and the total power consumption required to compute the tasks according to a given schedule. We designed experiments and based on simulations, measurements, visualizations, computations, and analyses, we found optimized schedules for scaled sets of dimensional task datasets, which is the main contribution of this work.