Abstract-Multi-core processors are anticipated to become a major development platform for real-time systems. However, existing power management algorithms are not designed to sufficiently utilize the features available in many multi-core processors, such as shared L2 caches and per-core DVFS, to effectively minimize processor energy consumption while providing real-time guarantees. In this paper, we propose a twolevel utilization control solution for energy efficiency in multicore real-time systems. At the core level, our solution addresses two optimization objectives: controlling the CPU utilization of each core to its desired schedulable bound and minimizing the core energy consumption by adopting per-core DVFS and dynamic L2 cache partitioning to adapt both the CPU frequencydependent and independent portions of the task execution times of the core. Since traditional control theory cannot handle multiple optimization objectives, a novel utilization controller is designed based on advanced multi-objective model predictive control theory. At the processor level, a cache demand arbitrator is proposed to coordinate the cache size demand from each core and conduct dynamic cache resizing to minimize the leakage power consumption of the shared L2 caches. The energy and time overheads of the proposed control solution are analyzed and addressed in the experiments with well-known benchmarks. Our extensive results demonstrate that our solution outperforms two state-of-the-art power management algorithms that do not consider L2 caches or per-core DVFS, by having more accurate utilization control and less energy consumption.
The authors in REF propose a two-level utilization control solution for energy optimization in multicore real-time systems, in which the cache assigned to a task is upper-bounded by the cache quota of the core.
Cache-Aware Utilization Control for Energy Efficiency in Multi-Core Real-Time Systems
2011 23rd Euromicro Conference on Real-Time Systems
2011 23rd Euromicro Conference on Real-Time Systems
Computer Science