Retention-Based Energy-Efficient and High-Core-Utilization Scheduling for Arbitrary-Deadline DAGs

Abstract

Modern clustered (power-island) multicore processors increasingly expose retention as a low-overhead low-power state, yet exploiting it in real-time systems critically depends on whether the scheduler can create sufficiently long cluster-level all-cores-idle windows that exceed a break-even length. This feasibility bottleneck is particularly acute for always-on arbitrary-deadline Directed Acyclic Graph (DAG) workloads, where precedence-induced execution fluctuations fragment idle times and the relative deadline may exceed the period. This paper proposes a retention-oriented scheduling framework for such workloads, consisting of two algorithms with a clear progression. PRES performs design-time planning and constructs a postponable division for each DAG, reshaping execution to aggregate slack into contiguous cluster-level idle windows; a lightweight event-driven runtime then enforces the plan and triggers retention without increasing online scheduling complexity. Building on PRES, PREHS further improves core utilization via multi-DAG merging and intra-cluster spatial sharing, consolidating workload onto fewer active clusters to reduce static/leakage energy while preserving deadline guarantees. We conduct extensive experiments across diverse cluster configurations and DAG structural characteristics, and further include a case study based on an application-derived DAG from a real-world communication/signal-processing pipeline. The results show that, compared with a representative work-conserving baseline with automatic retention/PG, PREHS reduces static energy consumption by up to 65.62%; moreover, under the same resource constraints, PREHS improves task acceptance ratio over PRES by up to 43 percentage points, with the largest gains observed in resource-tight configurations.