Abstract
With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.
Highlights
With the increasing number of cloud computing and Internet services, high energy consumption contributed by data center loads has become a crucial issue [1]
As our objective is to minimize the variation of energy consumption and unusable power budget for fuel cell powered data centers, we focus on the metrics in four aspects: (1) the performance of
This paper focuses on the power budget fragmentation problem in data center architecture powered by fuel cells
Summary
With the increasing number of cloud computing and Internet services, high energy consumption contributed by data center loads has become a crucial issue [1]. Besides the rising pressure from energy consumption and the deterioration of the climate, the power budget provided by the power transmission infrastructure of data centers usually limits the number of servers that can be added to address the growing load [5,6]. The problem is alleviated by developing new data center facilities and new power infrastructure, but this is expensive and time-consuming. In this resource-constrained environment, maximizing the use of the existing power infrastructure and becoming environmentally friendly are two important goals that should be considered.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
