Abstract
This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.
Original language | English (US) |
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Pages (from-to) | 10-20 |
Number of pages | 11 |
Journal | Integration, the VLSI Journal |
Volume | 48 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2015 |
Keywords
- Power management
- Reinforcement learning
- Semi-Markov decision process
- Temporal difference learning
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Electrical and Electronic Engineering