Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

As the Internet of Things (IoT) and mobile devices have rapidly proliferated, their computationally intensive applications developed into complex, concurrent IoT-based workflows involving multiple interdependent tasks. By exploiting its low latency high bandwidth, edge computing (MEC) has emerged to achieve high-performance computation offloading these satisfy quality-of-service requirements devices. In this study, we propose an strategy for in a MEC environment. The proposed task-based consists optimization problem that includes task dependency, communication costs, workflow constraints, device energy consumption, heterogeneous characteristics addition, optimal placement tasks is optimized using discrete teaching learning-based (DTLBO) metaheuristic. Extensive experimental evaluations demonstrate effective at minimizing consumption reducing execution times compared strategies different metaheuristics, including particle swarm ant colony optimization.