Economic analysis of energy use
The most commonly used energy-efficiency metric for data centers is power usage effectiveness (PUE), calculated as the ratio of total power entering the data center to the power used by IT equipment.
PUE = Total Facility Power/IT Equipment Power = 1 + Non IT Facility Energy/IT Equipment Energy
PUE measures the percentage of power used by overhead devices (cooling, lighting, etc.). The average U.S. data center has a PUE of 2.0, meaning two watts of total power (overhead + IT equipment) for every watt delivered to IT equipment. State-of-the-art data centers are estimated to have a PUE of roughly 1.2. Google publishes quarterly efficiency metrics from its data centers in operation. PUEs of as low as 1.01 have been achieved with two-phase immersion cooling.
The EPA has an Energy Star rating for standalone or large data centers. To qualify for the ecolabel, a data center must be within the top quartile in energy efficiency of all reported facilities. The Energy Efficiency Improvement Act of 2015 (U.S.) requires federal facilities—including data centers—to operate more efficiently. California's Title 24 (2014) of the California Code of Regulations mandates that every newly constructed data center have some form of airflow containment to optimize energy efficiency.
The European Union (EU) also has a similar initiative: EU Code of Conduct for Data Centres.
Efficiency improvements and renewable energy integration are helping to offset some emissions, but fossil fuels remain a major source of electricity for data center operations worldwide.
In 2011, server racks in data centers were designed for more than 25 kW, and the typical server was estimated to waste about 30% of the electricity it consumed. The energy demand for information storage systems is also rising. A high-availability data center is estimated to have a 1 MW demand and consume $20 million in electricity over its lifetime, with cooling accounting for 35% to 45% of the data center's total cost of ownership. Calculations show that in two years, the cost of powering and cooling a server could be equal to the cost of purchasing the server hardware. Research in 2018 showed that a substantial amount of energy could still be conserved by optimizing IT refresh rates and increasing server use. Research for optimizing task scheduling is also underway, with researchers looking to implement energy-efficient scheduling algorithms that could reduce energy consumption by anywhere between 6% and 44%.
In 2011, Facebook, Rackspace, and others founded the Open Compute Project (OCP) to develop and publish open standards for greener data center computing technologies. As part of the project, Facebook published the designs of its server, which it had built for its first dedicated data center in Prineville. Making servers taller left space for more effective heat sinks and enabled the use of fans that moved more air with less energy. By not buying commercial off-the-shelf servers, energy consumption from unnecessary expansion slots on the motherboard and unneeded components, such as a graphics card, was also reduced. In 2016, Google joined the project and published the designs of its 48V DC shallow data center rack. This design had long been part of Google data centers. By eliminating the multiple transformers usually deployed in data centers, Google had achieved a 30% increase in energy efficiency. In 2017, sales for data center hardware built to OCP designs topped $1.2 billion and are expected to reach $6 billion by 2021.
Power is the largest recurring cost to the user of a data center. In 2008, the ASHRAE recommended a temperature range of 64.4 °F (18.0 °C) to 80.6 °F (27.0 °C), though some data centers could operate at higher temperatures. However, cooling at or below 70 °F (21 °C) wastes money and energy. Furthermore, overcooling equipment in environments with a high relative humidity can expose equipment to a high amount of moisture that facilitates the growth of salt deposits on conductive filaments in the circuitry.
A power and cooling analysis, also referred to as a thermal assessment, measures the relative temperatures in specific areas as well as the capacity of the cooling systems to handle specific ambient temperatures. A power and cooling analysis can help identify hot spots, over-cooled areas that can handle greater power use density, the breakpoint of equipment loading, the effectiveness of a raised-floor strategy, and optimal equipment positioning (such as AC units) to balance temperatures across the data center. Power cooling density is a measure of how much square footage the center can cool at maximum capacity. The cooling of data centers is the second largest power consumer after servers, with cooling taking about 7% to 30% of energy usage (depending on efficiency), compared to an average of 60% of energy used by servers.
An energy efficiency analysis measures the energy use of data center IT and facilities equipment. A typical energy efficiency analysis measures factors such as a data center's Power Use Effectiveness (PUE) against industry standards, identifies mechanical and electrical sources of inefficiency, and identifies air-management metrics. However, the limitation of most current metrics and approaches is they do not include IT in the analysis. Case studies have shown that by addressing energy efficiency holistically in a data center, major efficiencies can be achieved that are not possible otherwise.
Computational fluid dynamics (CFD) analysis uses sophisticated tools and techniques to understand the unique thermal conditions present in each data center—predicting the temperature, airflow, and pressure behavior of a data center to assess performance and energy consumption using numerical modeling. By predicting the effects of these environmental conditions, CFD analysis can be used to predict the impact of high-density racks mixed with low-density racks and the onward impact on cooling resources, poor infrastructure management practices, and AC failure or AC shutdown for scheduled maintenance.
Thermal zone mapping uses sensors and computer modeling to create a three-dimensional image of the hot and cool zones in a data center. This information can help identify optimal positioning of data center equipment. For example, critical servers might be placed in a cool zone that is serviced by redundant AC units.
Data centers consume a lot of power, split between two main uses: running the equipment and cooling it. Power efficiency reduces the first category.
Cooling cost reduction through natural means includes location decisions. When the focus is on avoiding good fiber connectivity, power grid connections, and people concentrations for equipment management, a data center can be miles away from users. Mass data centers like Google or Facebook do not need to be near population centers. Arctic locations that can use outside air for cooling are becoming more popular. Countries with favorable conditions, such as Canada, Finland, Sweden, Norway, and Switzerland are trying to attract cloud computing data centers.
Singapore lifted a three-year ban on new data centers in April 2022. A major data center hub for the Asia-Pacific region, Singapore lifted its moratorium on new data center projects in 2022, granting four new projects, but rejecting more than 16 data center applications from over 20 received. Singapore's new data centers will meet very strict green technology criteria, including "Water Usage Effectiveness (WUE) of 2.0/MWh, Power Usage Effectiveness (PUE) of less than 1.3, and have a "Platinum certification under Singapore's BCA-IMDA Green Mark for New Data Centre" criteria that clearly addressed decarbonization and use of hydrogen cells or solar panels.
It is very difficult to reuse the heat generated by air-cooled data centers. For this reason, data center infrastructures are more often equipped with heat pumps.
