Synchronization-Aware Energy Management for VFI-Based Multicore Real-Time Systems

• Published in: IEEE transactions on computers Vol. 61; no. 12; pp. 1682 - 1696
• Main Authors: Jian-Jun Han, Xiaodong Wu, Dakai Zhu, Hai Jin, Yang, L. T., Gaudiot, Jean-Luc
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access protocols; DVFS; Dynamical systems; Dynamics; Electric potential; Energy consumption; Energy efficiency; Energy management; Mathematical models; multicore; Multicore processing; Power system management; Program processors; Real time systems; shared resources; Studies; Synchronism; Synchronization; Tasks; Voltage; Voltage frequency island (VFI)
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2012.136

Voltage and frequency island (VFI) was recently adopted as an effective energy management technique for multicore processors. For a set of periodic real-time tasks that access shared resources running on a VFI-based multicore system with dynamic voltage and frequency scaling (DVFS) capability, we study both static and dynamic synchronization-aware energy management schemes. First, based on the enhanced MSRP resource access protocol with a suspension mechanism, we devise a synchronization-aware task mapping heuristic for partitioned-EDF scheduling, which assigns tasks that access similar set of resources to the same core to reduce the synchronization overhead and thus improve schedulability. Then, static schemes that assign uniform and different scaled frequencies for tasks on different VFIs are studied. To further exploit dynamic slack, we propose an integrated synchronization-aware slack management framework to appropriately reclaim, preserve, release and steal slack at runtime to slow down the execution of tasks subject to the common voltage/frequency limitation of VFIs and timing/synchronization constraints of tasks. Taking the additional delay due to task synchronization into consideration, the new scheme allocates slack in a fair manner and scales down the execution of both noncritical and critical sections of tasks for more energy savings. Simulation results show that, the synchronization-aware mapping can significantly improve the schedulability of tasks. The energy savings obtained by the static scheme with different frequencies for tasks on different VFIs is close to that of an optimal Integer Nonlinear Programming (INLP) solution. Moreover, compared to the simple extension of existing solutions for uniprocessor systems, our schemes can obtain much better energy savings (up to 40 percent) with comparable DVFS overhead.