Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes
Abstract
:1. Introduction
- Is it possible to achieve better energy efficiency in data centers just by changing the equipment’s layout and taking advantage of legacy resources?
- How to maintain the sustainable consumption of physical and energy resources in the data center despite the growing demand for processing and storage?
- What know-how has been learned in the face of the challenges of implementing a data center in the context of tropical climate?
2. Related Works
3. Proposed Solution to Improve Cooling Efficiency
3.1. Cooling with Insufflation Downwards (DownFlow)
3.2. High Flow Air Diffusers
3.3. Cold Aisle Confinement
- Lower implementation cost —adhering to the context and the search for making the new data center construction cost planning as small as possible without compromising the quality of air conditioning;
- Implementation simplicity—installing doors and a roof for the basic confinement of the aisle. The low implementation complexity helped to not compromise the planned schedule for putting the new data center into production;
- Increased operating time without direct power—during an event that causes the initialization of the auxiliary power systems that supply the CRAC (e.g., generator set) to fail to start, or even an eventual delay in starting it, the confinement of the cold aisle creates an internal bank of cold air storage that provides the servers a significant additional running time before they shut down due to excessive temperature. This attribute related to this type of aisle confinement makes it possible to have greater certainty that the automated process of shutting down our 346 pieces of old equipment, added to the more 80 new ones, would have the necessary time to be completed without being compromised by the excess of heat.
4. Results
- April 2018–April 2019—Period before moving to the new data center, from April 2018, when these servers were installed in the old data center, until April 2019, when we shut down these eight servers to change the building;
- May 2019–January 2020—Period after the change of building, from May 2019, when we started the initialization of the eight servers already in the new data center, until January 2020, when we set the cutoff point of this study.
5. Conclusions
- Better electricity efficiency in our new data center;
- Results of better cooling performance for old and new servers, even if geographically the new data center is located in a city with a humid tropical climate with an average temperature of 27 °C (with peaks of 37 °C).
- Since our equipment works smoothly in the temperature range of 20–22 °C, the servers’ input temperature value and the current value measured in the environment is around 15 and 16 °C. We use this difference in such a way as to reduce the operation time of the CRACs. This would reduce electricity consumption and consequently reduce expenses;
- The option to bring more servers into the new data center, since the refrigerated thermal load has clearance for this if we use 22 °C as the maximum input temperature limit in the cold aisles.
- Explore the techniques pointed out by industrial and practical related works, providing other practical experimentation results;
- Develop a methodology to optimize the energy efficiency based on software solution;
- Develop a new measure to evaluate the impact of the direction of the air in data centers.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Work | Approach | Energy Save |
---|---|---|
Heller et al. [41] | Central control software | up to 50% |
Han et al. [43] | Software-defined networks(SDN) | 18.75% |
Song et al. [28] | Air controller, layout and location changes | 35% |
Ma et al. [44] | Heuristic and optimization algorithms | 11% to 28% |
Sun et al. [45] | Deep learning by reinforcement and SDN | around 12% |
Saadi et al. [46] | Optimizing the workload cost function | 21% on average |
MirhoseiniNejad et al. [47] | Low complexity holistic model | around 11% |
Kaffes et al. [48] | Allocation mechanism of different sets of cores | 9% |
Equipment | Average Inlet Temperature (°C)—April 18–April 19 (SD) | Average Inlet Temperature (°C)—May 19–January 20 (SD) | Average Temperature Difference (°C) | Percentage of Reduction |
---|---|---|---|---|
GPU Server 01 | 25.76 () | 15.35 () | 10.41 | 40% |
GPU Server 02 | 22.60 () | 15.27 () | 7.33 | 32% |
GPU Server 03 | 26.83 () | 16.31 () | 10.53 | 39% |
GPU Server 04 | 25.55 () | 15.21 () | 10.34 | 40% |
GPU Server 05 | 22.77 () | 15.31 () | 7.47 | 33% |
GPU Server 06 | 28.18 () | 15.26 () | 12.91 | 46% |
GPU Server 07 | 28.04 () | 15.28 () | 12.76 | 46% |
GPU Server 08 | 27.34 () | 15.21 () | 12.13 | 44% |
Average | 25.88 | 15.40 | 10.48 | 41% |
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Machado, R.d.S.; Pires, F.d.S.; Caldeira, G.R.; Giuntini, F.T.; Santos, F.d.S.; Fonseca, P.R. Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes. Appl. Sci. 2021, 11, 4719. https://doi.org/10.3390/app11114719
Machado RdS, Pires FdS, Caldeira GR, Giuntini FT, Santos FdS, Fonseca PR. Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes. Applied Sciences. 2021; 11(11):4719. https://doi.org/10.3390/app11114719
Chicago/Turabian StyleMachado, Romulos da S., Fabiano dos S. Pires, Giovanni R. Caldeira, Felipe T. Giuntini, Flávia de S. Santos, and Paulo R. Fonseca. 2021. "Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes" Applied Sciences 11, no. 11: 4719. https://doi.org/10.3390/app11114719
APA StyleMachado, R. d. S., Pires, F. d. S., Caldeira, G. R., Giuntini, F. T., Santos, F. d. S., & Fonseca, P. R. (2021). Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes. Applied Sciences, 11(11), 4719. https://doi.org/10.3390/app11114719