CODES+ISSS '11 Proceedings of the seventh IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Improving energy efficiency for mobile platforms by exploiting low-power sleep states
Proceedings of the 9th conference on Computing Frontiers
Optimal DPM and DVFS for frame-based real-time systems
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
ACM Transactions on Embedded Computing Systems (TECS)
ACM Transactions on Embedded Computing Systems (TECS) - Special Issue on Design Challenges for Many-Core Processors, Special Section on ESTIMedia'13 and Regular Papers
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
Power-aware fixed priority scheduling for sporadic tasks in hard real-time systems
Journal of Systems and Software
| Hi-index | 14.98 |
Voltage/Frequency Scaling (VFS) and Device Power Management (DPM) are two popular techniques commonly employed to save energy in real-time embedded systems. VFS policies aim at reducing the CPU energy, while DPM-based solutions involve putting the system components (e.g., memory or I/O devices) to low-power/sleep states at runtime, when sufficiently long idle intervals can be predicted. Despite numerous research papers that tackled the energy minimization problem using VFS or DPM separately, the interactions of these two popular techniques are not yet well understood. In this paper, we undertake an exact analysis of the problem for a real-time embedded application running on a VFS-enabled CPU and using multiple devices. Specifically, by adopting a generalized system-level energy model, we characterize the variations in different components of the system energy as a function of the CPU processing frequency. Then, we propose a provably optimal and efficient algorithm to determine the optimal CPU frequency as well as device state transition decisions to minimize the system-level energy. We also extend our solution to deal with workload variability. The experimental evaluations confirm that substantial energy savings can be obtained through our solution that combines VFS and DPM optimally under the given task and energy models.