Improving TAS Adaptability with a Variable Temperature Threshold
Anthony Dowling, Ming-Cheng Cheng, Yu Liu
TL;DR
This paper addresses thermal management in Thermal-Aware Scheduling by removing the need for an offline search to set static temperature thresholds. It introduces VTF-TAS, a dynamic, fluid-scheduling–driven approach that updates the temperature threshold $T_H$ during schedule construction and uses a four-state CPU core model to better control core temperatures. The method achieves lower peak temperatures than POD-TAS without deadline violations and uses an enhanced evaluation methodology with a more realistic idle-but-powered thermal initialization. The contributions include the variable threshold update mechanism, integrated task-to-core mapping, and a pragmatic evaluation framework that demonstrates reduced thermal stress and overhead. This work has practical impact for real-time systems requiring predictable thermal behavior with reduced computational overhead.
Abstract
Thermal-Aware Scheduling (TAS) provides methods to manage the thermal dissipation of a computing chip during task execution. These methods aim to avoid issues such as accelerated aging of the device, premature failure and degraded chip performance. In this work, we implement a new TAS algorithm, VTF-TAS, which makes use of a variable temperature threshold to control task execution and thermal dissipation. To enable adequate execution of the tasks to reach their deadlines, this threshold is managed based on the theory of fluid scheduling. Using an evaluation methodology as described in POD-TAS, we evaluate VTF-TAS using a set of 4 benchmarks from the COMBS benchmark suite to examine its ability to minimize chip temperature throughout schedule execution. Through our evaluation, we demonstrate that this new algorithm is able to adaptively manage the temperature threshold such that the peak temperature during schedule execution is lower than POD-TAS, with no requirement for an expensive search procedure to obtain an optimal threshold for scheduling.
