An On-Board Executable Pareto-Based Iterated Local Search Algorithm for Embedded Multi-Core Processor Task Scheduling

The advancement of wearable electronic technology has facilitated the integration of smart wearable devices into artificial intelligence (AI)-driven medical assisted diagnosis. Embedded multi-core processors (MPs) have gradually emerged as pivotal hardware components for smart wearable medical diagnostic devices due to their high performance and flexibility. However, embedded MPs face the challenge of balancing performance, power consumption, and load-balancing. In response, we introduce a Pareto-based iterated local search (PILS) algorithm for task scheduling, which systematically optimizes multiple objectives, alongside a task list model to reduce the dimension of the decision space and enhance scheduling performance. In addition, we present a two-stage discretization scheme to ensure that the proposed algorithm offers meaningful guidance throughout the scheduling process. Simulation and on-board testing results show that the proposed algorithm effectively optimizes energy consumption, task execution time, and load-balancing in embedded MPs task scheduling, indicating the potential of the proposed algorithm in enhancing the performance of smart wearable medical diagnostic devices powered by embedded MPs.