  Modern real-time systems utilize considerable amounts of power while
executing computation-intensive tasks. The execution of these tasks leads to
significant power dissipation and heating of the device. It therefore results
in severe thermal issues like temperature escalation, high thermal gradients,
and excessive hot spot formation, which may result in degrading chip
performance, accelerating device aging, and premature failure. Thermal-Aware
Scheduling (TAS) enables optimization of thermal dissipation to maintain a safe
thermal state. In this work, we implement a new TAS algorithm, POD-TAS, which
manages the thermal behavior of a multi-core CPU based on a defined set of
states and their transitions. We compare the performances of a dynamic RC
thermal circuit simulator (HotSpot) and a reduced order Proper Orthogonal
Decomposition (POD)-based thermal model and we select the latter for use in our
POD-TAS algorithm. We implement a novel simulation-based evaluation methodology
to compare TAS algorithms. This methodology is used to evaluate the performance
of the proposed POD-TAS algorithm. Additionally, we compare the performance of
a state of the art TAS algorithm, RT-TAS, to our proposed POD-TAS algorithm.
Furthermore, we utilize the COMBS benchmark suite to provide CPU workloads for
task scheduling. Our experimental results on a multi-core processor using a set
of 4 benchmarks demonstrate that the proposed POD-TAS method can improve
thermal performance by decreasing the peak thermal variance by 53.0% and the
peak chip temperature of 29.01%. Using a set of 8 benchmarks, the comparison of
the two algorithms shows a decrease of 29.57% in the peak spatial variance of
the chip temperature and 26.26% in the peak chip temperature. We also identify
several potential future research directions.
