Task Scheduling and Temperature Optimization Co-Design for Multi-Core Embedded Systems

Multi-core platforms’ high integration and power density increase energy consumption and chip temperatures, affecting reliability and accelerating aging. Effective energy and temperature management is crucial to maintaining performance and extending processor lifespans. This paper proposes a real-time task scheduling approach using Dynamic Voltage and Frequency Scaling (DVFS) to optimize energy and temperature in embedded Multi-Processor System-on-Chip (MPSoC) platforms. We introduce a thermal model and develop a Mixed-Integer Nonlinear Programming (MINLP) model to optimize task allocation and scheduling, considering real-time, dependency, non-overlapping, and thermal constraints. To simplify the problem, we linearize the MINLP model into a Mixed-Integer Linear Programming (MILP) model. A Genetic Algorithm (GA)-based method is also proposed to improve scalability. The GA method accounts for task allocation, processor selection, and thermal constraints, while prioritizing lower execution frequencies to reduce energy use. Simulation results show that the proposed approach effectively reduces energy consumption and computation time, which is suitable for complex multi-core platforms.