Technical Considerations for Energy-efficient Data Centers

By Rahul Guhathakurta

Cover Image Attribute: Image by tstokes from Pixabay
Cover Image Attribute: Image by tstokes from Pixabay 

Data centers (DCs) design elements (incl. physical surroundings) are some of the most complicated and energy-intensive of all building types. In addition, the trends of expanding data center capacity and power used by information technology (IT) equipment reemphasize the need to lower cooling loads, i.e., the initial cost for heating, ventilation, and air conditioning (HVAC) equipment, and related energy and operational expenses. Note these ongoing trends will keep driving up the cost of doing business as the costs of electrical power consumption during the lifetime of a data center may exceed the price of the installed electrical power system equipment, including the cost of an Uninterrupted Power Supply (UPS).


Figures 1: Standard breakdown of data centre energy consumption / IndraStra Global

Figure 2: Standard breakdown of HVAC power consumption in data centers / IndraStra Global

Figures 1 and 2 depict a typical breakdown of measured data in data centers [2]. However, considerable variations can be noticed between facilities due to the evolution of equipment efficiencies or the kind of cooling and air-handling systems.

 

But generally, it has been seen when networks and DCs are constructed, immediate attention is typically placed on higher performance and high throughput rather than energy efficiency, which is rarely a design concern. However, this article will discuss how several energy-efficiency solutions can be implemented in DCs without a substantial increase in overheads and other administrative burdens. 


Dynamic Power Management (DPM) 


DPM is a set of approaches for dynamically powering down devices based on the current load. The number of active electronic components is kept to a minimum while meeting all performance and Quality of Service (QoS) requirements. During regular operation, the electronic components, both as a whole and as interconnected devices, experience non-uniform workloads. Based on the recent workload history derived from the Dynamic Power Manager or Autonomic Power Manager framework, these components' future workloads can be forecasted with some degree of uncertainty using probabilistic approaches.


Dynamic Voltage/Frequency Scaling


DVFS is an approach based on the observation that the power consumption of a processor chip is inversely proportional to the voltage that is supplied, and the following equation characterizes it;

 

P = V2. f

 

where P represents the amount of power used, V represents the voltage, and f represents the frequency. As a result, one can lessen their reliance on the power supply by lowering either the voltage or the switching frequency. The CPU power is the only component that will experience a reduction in frequency because the bus, memory, and discs do not depend on the CPU frequency. In addition, the DVFS approach cannot be implemented successfully without the support of the hardware.

 

Additionally, most DVFS schemes depend on implementing the Advanced Configuration and Power Interface (ACPI) standard [3]. ACPI is a power management and configuration standard independent of operating systems and used by most manufacturers.


Virtualization


Virtualization is the technology for power management and resource allocation that has seen the most widespread adoption among operators of data centers. It is a method that enables several virtual machines (VM) to share the resources of a single physical server, with the result that each VM can support a unique set of software programs. A VM's CPU and memory resources can be dynamically provisioned by the requirements placed on it for its present level of performance. Because of this, virtualization is an excellent solution for meeting the stringent criteria set on energy efficiency in data centers. [4]


Simulations


Most researchers have relied on testbeds to evaluate the effectiveness of the energy-saving strategies they have recommended for data centers. However, the most significant drawback is the issue of scalability of the testbed to the real-world DC environments, which are far greater than the size of the testbed environment.

 

MDCSim, CloudSim, and GreenCloud are the names of three well-known data center energy-aware simulation environments. According to the comparison [5], GreenCloud is superior to MDCSim and CloudSim simulators in several respects. In contrast to GreenCloud, which is an extension of NS2 and a simulator that captures the specifics of the simulation at the packet level, MDCSim and CloudSim are event-based simulators that do not capture these details. In addition, neither MDCSim nor CloudSim offers GUI functionality, whereas NS2-based GreenCloud simulator has Nam animator support [6] - that's why its usage is preferred in energy-aware DC simulation exercises.


About the Author:


Rahul Guhathakurta (ORCID: 0000-0002-6400-6423) is a strategic management consultant. A primary investor in IndraStra Global — a US-based publishing company.


References

 

[1] Constantinos A.Balaras, et al. "High-Performance Data Centers and Energy Efficiency Potential in Greece." - ScienceDirect, 11 Apr. 2017, www.sciencedirect.com/science/article/pii/S1878029617300956. Accessed 22 Nov. 2022.

 

[2] Home | High-Performance Buildings for High-Tech Industries, hightech.lbl.gov. Accessed 22 Nov. 2022.

 

[3] Hebbar, Rajan. et al “An Experimental Evaluation of Workload Driven DVFS.” ACM Digital Library, dl.acm.org/doi/10.1145/3447545.3451192. Accessed 22 Nov. 2022.

 

[3] Chernicoff D, “The shortcut guide to data center energy efficiency.” Realtime Publisher, New York. http://nexus.realtimepublishers.com/sgdcee.php?ref=gbooks. Accessed 22 Nov. 2022.

 

[4] Kliazovich, Dzmitry, et al. “GreenCloud: A Packet-level Simulator of Energy-aware Cloud Computing Data Centers - the Journal of Supercomputing.” SpringerLink, 9 Nov. 2010, link.springer.com/article/10.1007/s11227-010-0504-1.

 

[5] Chen, D., et al. “Usage centric green performance indicators” ACM Digital Library, dl.acm.org/doi/10.1145/2160803.2160868. Accessed 22 Nov. 2022.


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IndraStra Global: Technical Considerations for Energy-efficient Data Centers
Technical Considerations for Energy-efficient Data Centers
By Rahul Guhathakurta
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