Jointly Optimizing the IT and Cooling Systems for Data Center Energy Efficiency based on Multi-Agent Deep Reinforcement Learning

2020 ◽  
Author(s):  
Ce Chi ◽  
Kaixuan Ji ◽  
Avinab Marahatta ◽  
Penglei Song ◽  
Fa Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Data Center ◽  
Cooling Systems ◽  
Multi Agent

scholarly journals Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2071
Author(s):  
Ce Chi ◽  
Kaixuan Ji ◽  
Penglei Song ◽  
Avinab Marahatta ◽  
Shikui Zhang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Data Center ◽  
Data Centers ◽  
Optimization Method ◽  
Comparison Method ◽  
Cooling Systems ◽  
Model Free ◽  
Multi Agent

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

From Chip to Cooling Tower Data Center Modeling: Influence of Air-Stream Containment on Operating Efficiency

2011 ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Cullen E. Bash ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Cooling System ◽  
Heat Index ◽  
Operating Efficiency ◽  
Cooling Systems ◽  
Mixing Effects ◽  
Air Stream ◽  
Data Center Cooling ◽  
Air Streams

In the drive to enhance data center energy efficiency, much attention has been placed on the prospect of airflow containment in hot-aisle cold-aisle raised floor arrangements. Such containment prevents airflow recirculation, eliminating the mixing effects of the hot and cold air streams that can cause an undesirable temperature rise at the inlet of the equipment racks. The intuitive assessment of the industry has been that the elimination of such mixing effects increases the energy efficiency of the data center cooling system by enabling delivery of air at higher inlet temperatures, thus reducing the amount of infrastructure cooling required. This paper employs an end-to-end modeling approach to analyze the effect of air stream containment in the computer room and its impact on the holistic system efficiency. Dimensionless heat index parameters are employed to characterize the effects of containment, recirculation and mixing within the computer room environment. The extent of recirculation is shown to primarily influence the operation of the rack and CRAC level cooling systems, with the chiller systems also impacted. The overall effect on the complete cooling system performance and data center efficiency requires balancing of these effects. Through this model analysis, it is shown that containment may negatively impact overall energy efficiency in some circumstances, and that recirculation may actually be beneficial to overall energy efficiency under certain system dependent operating thresholds.

From Chip to Cooling Tower Data Center Modeling: Influence of Air-Stream Containment on Operating Efficiency1

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Thomas J. Breen ◽  
Ed J. Walsh ◽  
Jeff Punch ◽  
Amip J. Shah ◽  
Cullen E. Bash ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Cooling System ◽  
Heat Index ◽  
Cooling Systems ◽  
Mixing Effects ◽  
Air Stream ◽  
Data Center Cooling ◽  
Air Streams ◽  
Dimensionless Heat

In the drive to enhance data center energy efficiency, much attention has been placed on the prospect of airflow containment in hot-aisle cold-aisle raised floor arrangements. Such containment prevents airflow recirculation, eliminating the mixing effects of the hot and cold air streams that can cause an undesirable temperature rise at the inlet of the equipment racks. The intuitive assessment of the industry has been that the elimination of such mixing effects increases the energy efficiency of the data center cooling system by enabling delivery of air at higher inlet temperatures, thus reducing the amount of infrastructure cooling required. This paper employs an end-to-end modeling approach to analyze the effect of air stream containment in the computer room and its impact on the holistic system efficiency. Dimensionless heat index parameters are employed to characterize the effects of containment, recirculation, and mixing within the computer room environment. The extent of recirculation is shown to primarily influence the operation of the rack and computer room air conditioning (CRAC) level cooling systems, with the chiller systems also impacted. The overall effect on the complete cooling system performance and data center efficiency requires balancing of these effects. Through this model analysis, it is shown that containment may negatively impact overall energy efficiency in some circumstances, and that recirculation may actually be beneficial to overall energy efficiency under certain system dependent operating thresholds.

THE ENERGY EFFICIENCY ANALYSIS OF DATA CENTER COOLING BASED ON SEPARATED HEAT PIPE

2018 ◽  
Author(s):  
Tao Wang ◽  
Yuhua Li ◽  
Huan Liu ◽  
Lei Zhang ◽  
Yuyan Jiang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Heat Pipe ◽  
Data Center ◽  
Efficiency Analysis ◽  
Data Center Cooling ◽  
Energy Efficiency Analysis
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