Crêpe: Clock Reconfigurability for Preemption Control (Artifact)

Abstract

With the emergence of embedded system-on-chip (SoC) platforms, the development of energy-constrained real-time systems brings numerous novel challenges for optimal resource consumption. On these modern hardware platforms, complex clock subsystems make it possible to tradeoff between temporal performance and energy efficiency by reconfiguring the system, which exceeds the state-of-the-art of existing dynamic-voltage-frequency-scaling (DVFS) scheduling schemes. On embedded real-time systems, the usage of the devices (e.g., transceiver/memory/sensor devices) is an essential component to be able to interact with the surrounding world. Each device has precedence constraints with respect to specific clock sources and their settings. Therefore, to select resource-optimal configurations, we need to adapt the clock subsystem, which becomes especially challenging in the presence of asynchronous preemptions, often found during device interaction.
This artifact evaluation covers the work of Crêpe, an approach to clock-reconfiguration-aware preemption control on systems with devices. Crêpe makes use of the target platform’s clock subsystem, possible idle modes, and the reconfiguration penalties for adapting the clock subsystem. By combining a hardware model for the device under investigation with an awareness of the required clock configuration for each task, as well as possible interrupts causing preemptions during runtime, Crêpe employs a mathematical formalization to determine energy-minimal configuration sequences while meeting all given deadlines. Before runtime, Crêpe solves the mathematical problem with standard mathematical solver tools and generates optimal execution strategies and clock-system reconfigurations before runtime. These offline-generated schedules are then assessed by the dispatcher during runtime, leading to an overall minimized energy consumption with minimal overhead during execution. Crêpe also consists of an implementation based on a widely-used SoC platform (i.e., ESP32-C3) and an automated testbed for comprehensive energy-consumption evaluations. This artifact evaluation makes use of these to validate Crêpe’s claim of selecting resource-optimal settings under worst-case considerations by reproducing our results shown in the related Crêpe paper [Eva Dengler and Peter Wägemann, 2024].