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Data centers have proliferated among U.S. federal agencies, growing from 400 in 1998 to over 2000 by 2010. Alarmed by growing costs and potential inefficiencies, the Obama Administration issued a Presidential Memorandum directing all federal agencies to "adopt a policy against expanding data centers beyond current levels, and develop plans to consolidate and significantly reduce data centers within 5 years.^[1]" As a result, Whitestone was tasked by a large agency to approximate the facility savings that might accrue from the closure of a typical data center.

We found that the range of possible cost savings attributable to center closures is quite broad. Our estimates span from $175 to $540 per square foot (SF) of computer room floor area, varying by the amount of redundant equipment and computing intensity. The precision of the estimates can be improved with knowledge of a center's design features, as well as its size, location, and levels of service.

Our definition of facility costs for data centers includes those directly attributable to the center, such as energy and maintenance and repair (M&R), and other operating costs-custodial, grounds, management, pest control, etc.-that reflect standard tenant services only indirectly related to the center. Not included are the costs of the programmatic staff-operators, engineers, and other technical staff-that run the computing activity. Unlike most commercial data centers, many federal centers are small and occupy only part of a multiuse building. Accordingly, our estimates assume a size of 500 SF of computing area.

Cost were estimated for five types of data centers, as shown in Table 1; four are categorized as Tier I through Tier IV centers, according to the redundancy of mechanical equipment and the independence of electrical distribution paths.^[2] A fifth "non tier" model is the simplest version of a data center, with no raised flooring, redundant systems or special power equipment. For each type of center, we defined representative models based on component inventories derived from case studies, and then verified by Jacobs Engineering. These are summarized in the Attachment. Energy demand was computed using an Energy Star calculator.^[3] Costs were generated using the MARS Facility Cost Forecast System.^[4]

Our estimates of energy costs do not vary for different tiers. This is because adding redundant mechanical and electrical capacity changes only the reliability of computing services, but not the demand for computing. However, energy costs do vary by computing intensity, as shown in Table 2.^[5] Note that the energy demand from computing (e.g. power required by servers, drives, and switches) is only part of total center demand. Other demand sources are from cooling and auxiliary equipment (e.g. lights, UPS). Our highest annual cost estimate, $540 per computer room SF, assumes a high level of computing intensity (substitute $209.86 for the Tier IV model energy costs in Table 1).^[6]

The M&R costs shown increasing by tier in Table 1 are a function of the cyclical maintenance and replacement tasks associated with the added mechanical and electrical equipment. The reported costs are based on a 50-year average (a period long enough to include the replacement of major cooling equipment).

Other operations costs are based on general tenant costs calibrated for levels of use appropriate for data centers. For example, security costs are based on systems and labor required for the relatively high level of security common in federal buildings: This includes "access control, system monitoring and intrusion detection systems; stationed security guard and daily patrol." Note that these costs increase by tier with the increase in "mechanical room" space required by additional equipment.^[7]

Our estimates provide an approximate range of savings for an individual data center, but they can also be used to demonstrate the savings that might accrue from closing a collection of facilities. Specifically, the Federal Data Center Consolidation Initiative (FDCCI) anticipates the closure of at least 800 centers by 2015.^[8] Assuming a 500 (computing area) SF data center and multiplying by our estimated costs, the annual savings for a single center could range from $140 to $270 thousand, while the annual savings for all 800 closings could range from a $112 to $216 million.

Our estimates could be refined with more information about the individual centers to be closed:^[9]

Size of the average center has a direct and obvious impact on savings. Increasing or decreasing the average size of a center to be closed could have a proportional affect on costs. For an actual commercial center (Tier III, unknown computing intensity) with 16,250 SF of computing area, we confirmed annual costs of $230 per SF. This is less than the $361 per SF we estimated for the (much) smaller center, and suggests there could be scale effects for major costs such as energy, M&R, and management.

Location determines the labor and utility rates and climatic conditions that drive many costs. For simplicity, our examples assume all centers are located in the Washington D.C. area; In comparison, operations costs in Honolulu were 35 percent higher than the D.C area in 2010, while costs in Alamogordo, NM were 28 percent less. Even greater variations can be seen for international locations.

Level of Service provided substantially affects operations costs such as M&R and management. Data centers operated over 80 hours per week create an estimated M&R requirement roughly 35 percent higher than those operated from 40 to 80 hours, as assumed in the estimates shown in Table 1. With regard to management costs, we assumed in our example that these costs are based on standard commercial practice, which is a fixed percentage of lease costs. If the center is part of a larger campus, then an in-house staff could handle management responsibilities for much less (about 80 percent less) than commercial costs.

In summary, our task was to provide a federal client with an approximation of the savings that follow from closing a small data center. We found that these savings can vary broadly by center design and computing intensity, and we suggested that additional data on size, location, and service levels could be used to refine estimates. We also demonstrated that our approach could estimate aggregate savings for multiple facility closures, such as those anticipated by the FDCCI.

-Luca Romani

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