Abstract
Since more power consumption results in more failures and degradations in system performance, reliability, and power bills, it has been a critical problem for not only large scale server system but also personal computers (PCs). Though much literature has focused on energy management and power budgeting for server systems, power consumption of PCs does not attain sufficient attentions fairly. In this paper an online power measurement and prediction framework is proposed and used to save more energy considering the PC as a whole controlled system. The framework includes parts such as power measurement unit, power prediction unit and a simple execution unit of power reduction decisions. A hardware-software joint prototype is implemented based on an intelligent digital multimeter. Experiments on a desktop PC and a laptop show that PC with the framework can save more power consumptions than that of the PCs without this framework.
Highlights
In the past decades, the performance of the personal computers (PCs) has increased approximately 500 times (e.g., CPU frequency from 6MHz to 3GHz) due to the intensity increase of VLSIs
Modern CPUs and graphics cards can change their power consumption code according to real workload and instructions execution, if more features, such as self-learning, workload characterization and prediction are integrated into the existing software, they can save more energy for computers running periodical tasks
We investigate the feasibility of hardware-software joint measurement and prediction of PC power consumption based on simple hardware implementation
Summary
The performance of the PC has increased approximately 500 times (e.g., CPU frequency from 6MHz to 3GHz) due to the intensity increase of VLSIs. Actual power consumption for a PC is far less than the nameplate rating This leads to over-demand for power and cooling infrastructure and provide a significant opportunity in enabling power management technologies. The U.S EPA estimates that if all PCs in the U.S used power saving technology, the US could save more than $300 million in energy costs each year. Modern CPUs and graphics cards can change their power consumption code according to real workload and instructions execution, if more features, such as self-learning, workload characterization and prediction are integrated into the existing software, they can save more energy for computers running periodical tasks.
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