Energy-Efficient Task Scheduling and Resource Allocation in Edge-Heterogeneous Computing Systems Using Multiobjective Optimization

With the explosive growth of Internet of Things (IoT) devices and the enormous data they generate, edge computing with heterogeneous architecture has been a research hotspot. Limited by a lack of power supply in the edge infrastructure, dynamic voltage and frequency scaling (DVFS)-based optimization becomes an effective way to balance energy cost and delay. However, achieving optimal performance efficiency in power-constrained edge computing environments remains a significant challenge, particularly in processing the tremendous requests from IoT endpoints. Therefore, we propose an energy-efficient task scheduling and resource allocation technique for edge-heterogeneous computing. Specifically, we first utilize the directed acyclic graph (DAG) to describe IoT requests, and formulate task scheduling and resource allocation as a multiobjective mathematical programming model. Second, a local search enhanced nondominated sorting genetic algorithm-II (LS-NSGA) is designed as the solution method, where LS seeks to improve the problem solving capacity of NSGA-II. Additionally, we develop a strategy that automatically adjusts the DVFS setting for each processor in existing scheduling solutions, exploiting the potential energy reduction. Experimental results indicate that LS-NSGA outperforms existing state-of-the-art algorithms. In the best-case scenario, our method achieves 52% lower execution time and 73% lower energy cost than alternative approaches. Moreover, the ablation study shows that the proposed adaptive DVFS strategy can achieve additional energy savings of approximately 4%–7% for various task-scale problems.