Energy-Efficient Cache-Aware Scheduling on Heterogeneous Multicore Systems

The adoption of heterogeneous multicore architectures into deadline-constrained embedded systems has various benefits in terms of schedulability and energy-efficiency. Existing energy-efficient algorithms, in this domain, allocate tasks to their energy-favorable core-types while using dynamic voltage and frequency scaling to reduce energy consumption. However, the practicality of such algorithms is limited due to the underlying assumptions made to simplify the analysis. This article paves the way for more practical approaches to minimize the energy consumption on heterogeneous multicores. Specifically, we investigate the nonlinear impacts that core-frequency and cache-partitioning have on task-executions in a heterogeneous multicore environment. In doing so, we propose an algorithm that exploits this relationship to effectively allocate tasks to specific cores and core-types, and determine the number of cache-partitions for each core. Extensive simulations using real-world benchmarks show the proficiency of our approach by achieving an average and maximum energy savings of 14.9 and 20.4 percent, respectively for core-level energy consumption, and 20.2 and 60.4 percent, respectively for system-level energy consumption.