Thermal-aware correlated two-level scheduling of real-time tasks with reduced processor energy on heterogeneous MPSoCs

This paper tackles the above challenge through scheduling tasks leveraging correlated optimizations at two different scales. Precisely, a two-level thermal-aware energy-efficient scheduling algorithm for real-time tasks on DVFS-enabled heterogeneous MPSoC systems is developed considering the constraints of task deadlines, task precedences, and chip peak temperature limit. At the processor level, a multi-processor model supporting dynamic voltage/frequency scaling is transformed to a virtual multi-processor model supporting only one fixed frequency level. At the core level, real-time tasks are assigned to individual cores of the virtual processor under the constraints of task precedence and peak temperature limit. Through nicely interleaving optimizations at both levels, high quality task scheduling solutions can be computed efficiently. Extensive simulations of synthetic real-time tasks and real-life benchmarks are performed to validate the proposed algorithm. Experimental results demonstrate the effectiveness of the proposed algorithm as compared to the benchmarking schemes.