Title :
A Gray-Box Feedback Control Approach for System-Level Peak Power Management
Author :
Gong, Jiayu ; Xu, Cheng-Zhong
Author_Institution :
Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA
Abstract :
Power consumption has become one of the most important design considerations for modern high density servers. To avoid system failures caused by power capacity overload or overheating, system-level power management is required. This kind of management needs to control power consumption precisely. Conventional solutions to this problem mostly rely on feedback controllers which only concern the power itself, known as black-box approaches. They may not respond to the variation of system quickly. This paper presents a gray-box strategy to design a model-predictive feedback controller based on a pre-built power model and a performance prediction model to constraint the peak power consumption of a server. In contrast to the existing strategies, this gray-box approach uses the performance events, which bring more insights of the behaviors and power consumption of a system, for the purpose of model prediction. We implemented a prototype of this controller and evaluated it using SPECweb2005 benchmark on a web server. This controller can settle the power consumption below the power cap within 2 control periods for more than 75% of the power overloading regardless of workload variations, outperforming black-box approaches. Meanwhile, the performance of application can be maximized with this controller.
Keywords :
Internet; feedback; file servers; power aware computing; power consumption; power control; predictive control; telecommunication control; SPECweb2005 benchmark; Web server; gray-box feedback control approach; model-predictive feedback controller design; modern high density servers; performance prediction model; power capacity overload; power consumption control; power overloading; pre-built power model; system failure avoidance; system-level peak power management; Adaptive control; Biological system modeling; Power demand; Power measurement; Predictive models; Servers;
Conference_Titel :
Parallel Processing (ICPP), 2010 39th International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-7913-9
Electronic_ISBN :
0190-3918
DOI :
10.1109/ICPP.2010.63