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<p>Energy consumption is an increasingly critical concern in multicore embedded real-time systems, especially when it is referred to systems that operate under battery constraints as mobile devices. Moreover, these systems must enhance performance such as the execution time while meeting temporal constraints to reduce deadline miss rates. Although Heterogeneous Multi-Processing (HMP) architectures offer opportunities for power optimization using dynamic voltage and frequency scaling (DVFS), standard operating system (OS) governors do not consider benchmark characteristics, memory or CPU usage, and optimal co-runner configurations. This limitation leads to scheduling decisions that trigger early battery depletion or extreme deadline violations under high system utilization. To solve these challenges, this paper proposes a two-level scheduling and DVFS framework. The proposed solution performs profiling benchmark-pair combinations to construct a static co-runner compatibility matrix. Online, a supervisor level (Partitioner) dynamically evaluates global system constraints, tracking remaining battery status and runtime deadline miss rates to adaptively select an heuristic strategy (A_max, B_max, B_eff). In the second level, a Task Scheduler executes tasks based on Earliest Deadline First (EDF) policy, integrated with a power estimation model. This structure translates a nonlinear, NP-hard multi-objective optimization problem into a deterministic, constant-time O(1) runtime overhead per task. Experimental evaluations are conducted on an ODROID XU4 platform containing a Samsung Exynos 5422 heterogeneous multicore processor, based on an ARM big.LITTLE architecture, using a set of embedded real-time benchmarks. The results show that: (i) B_max strategy delivers a suitable trade-off by simultaneously reducing mean energy consumption by about 7% and execution time by around 8%, compared to the OS., at the same time reducing drastically deadline failures by 72.61% under a 50% workload; (ii) the aggressive B_eff configuration, decreases power consumption by 50% in situations with ample slack time; and (iii) the A_max recovery strategy achieves about 29% acceleration, hence preventing system degradation under extreme workload saturation. Finally, integrating these context-aware mechanisms prevents the hardware from performance collapse, maximizing battery longevity while ensuring suitable real-time predictability.</p>

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Power-aware scheduling strategies for improving performance and energy consumption in heterogeneous multicore platforms

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Ricardo Mazón,
  • Houcine Hassan

摘要

Energy consumption is an increasingly critical concern in multicore embedded real-time systems, especially when it is referred to systems that operate under battery constraints as mobile devices. Moreover, these systems must enhance performance such as the execution time while meeting temporal constraints to reduce deadline miss rates. Although Heterogeneous Multi-Processing (HMP) architectures offer opportunities for power optimization using dynamic voltage and frequency scaling (DVFS), standard operating system (OS) governors do not consider benchmark characteristics, memory or CPU usage, and optimal co-runner configurations. This limitation leads to scheduling decisions that trigger early battery depletion or extreme deadline violations under high system utilization. To solve these challenges, this paper proposes a two-level scheduling and DVFS framework. The proposed solution performs profiling benchmark-pair combinations to construct a static co-runner compatibility matrix. Online, a supervisor level (Partitioner) dynamically evaluates global system constraints, tracking remaining battery status and runtime deadline miss rates to adaptively select an heuristic strategy (A_max, B_max, B_eff). In the second level, a Task Scheduler executes tasks based on Earliest Deadline First (EDF) policy, integrated with a power estimation model. This structure translates a nonlinear, NP-hard multi-objective optimization problem into a deterministic, constant-time O(1) runtime overhead per task. Experimental evaluations are conducted on an ODROID XU4 platform containing a Samsung Exynos 5422 heterogeneous multicore processor, based on an ARM big.LITTLE architecture, using a set of embedded real-time benchmarks. The results show that: (i) B_max strategy delivers a suitable trade-off by simultaneously reducing mean energy consumption by about 7% and execution time by around 8%, compared to the OS., at the same time reducing drastically deadline failures by 72.61% under a 50% workload; (ii) the aggressive B_eff configuration, decreases power consumption by 50% in situations with ample slack time; and (iii) the A_max recovery strategy achieves about 29% acceleration, hence preventing system degradation under extreme workload saturation. Finally, integrating these context-aware mechanisms prevents the hardware from performance collapse, maximizing battery longevity while ensuring suitable real-time predictability.