HiDVFS: Hierarchical Multi-Agent DVFS for Real-Time OpenMP DAG Workloads
For developers of real-time embedded systems using OpenMP, HiDVFS provides a temperature-aware, deadline-respecting DVFS method that significantly improves performance and energy efficiency over existing heuristics.
HiDVFS is a hierarchical multi-agent DVFS scheduler for real-time OpenMP DAG workloads that achieves a 2.83x speedup and 32.9% energy reduction over a baseline on Jetson TX2, and a 4.62x average speedup with 55.7% energy reduction across 12 benchmarks.
Leakage power in multicore embedded systems now rivals dynamic power, so DVFS schedulers must respect deadlines and thermal limits, not just average makespan. Existing heuristics lack per-core, temperature-aware control and overlook the irregular execution of OpenMP DAGs. We propose HiDVFS, a general, extensible hierarchical multi-agent DVFS scheduler: a profiler agent selects cores and frequencies, a thermal agent groups cores by temperature, and a priority agent orders tasks under contention, all trained with a makespan-focused reward using short-horizon future-state shaping for sample efficiency. Deadlines are soft, derived from a measured reference cost; a federated schedulability gate keeps operating points feasible, and a calibrated split-conformal shield bounds each action's predicted response time. On Jetson TX2 with multi-seed validation, HiDVFS attains a 4.16+/-0.58 s L10 makespan, a 2.83x speedup and 32.9% energy reduction over a fairness-corrected GearDVFS port, and a 4.62x average speedup with 55.7% energy reduction across all 12 BOTS benchmarks. Cross-platform results on TX2, Orin NX, and RubikPi show deadline-aware DVFS cuts energy 15 to 18% versus pinning the maximum frequency, and a measured mixed-criticality study shows cluster-aware reservation is required to keep a high-criticality task's deadline-miss ratio at zero.