A genetic algorithm with selective repair method under combined-criteria for deadline-constrained IoT workflow scheduling in Fog–Cloud computing

Abstract

Many IoT systems require deadline-constrained workflow scheduling, where missed deadlines can have serious consequences. Scheduling such IoT workflows in Fog–Cloud environments is challenging due to resource heterogeneity and the variability in workflow patterns and deadlines. Existing approaches, including heuristic and meta-heuristic algorithms, often fail to reliably satisfy deadline constraints while simultaneously minimizing the cost associated with the computational resources used for executing workflows. This paper introduces the Internet of Things Genetic Algorithm with Selective Repair under Combined Criteria (IoTGA-SRC²) to effectively tackle these challenges. IoTGA-SRC² introduces a novel selection mechanism that prioritizes solutions based on deadline violations and execution costs. It also features an innovative repair method, which can systematically detect infeasible solutions, perform a root cause analysis to identify the key factors causing deadline violations, and reallocate critical tasks using a multi-criteria method. By properly managing delays caused by execution time, communication time, and waiting time, IoTGA-SRC² can consistently satisfy deadline constraints across a wide range of problem configurations. Extensive experiments demonstrate that IoTGA-SRC² consistently outperforms multiple state-of-the-art methods in reducing execution costs while adhering to stringent deadline constraints, making it a valuable choice for various real-world applications in heterogeneous IoT–Fog–Cloud computing environments.