5. Findings

This section details the findings from the two-day workshop along with follow-up discussions with staff from the project team and OUCS.

5.1. General

In order to create the optimal CMR design from an energy and carbon standpoint it is essential that the energy efficiency goals are communicated to the entire project team. However this alone will not result in an optimal design. The way the customer (OUCS – as the ‘end user’ of the CMR) plans to use the facility plays a very large part in turning an efficient design on paper into an efficient one in practice. This is the first barrier that M and E designers often come across and is the hardest part of turning a ‘template’ design into a facility that actually achieves high efficiency from day one.

There are many factors that affect efficiency within a data centre which, by its very nature, is a complex system. The data centre influences the efficiency of the ICT equipment housed within it and vice-versa. The resulting system can be difficult to predict and understand and inherent loops within the system can also make efficiency analysis or prediction difficult. Moreover the measurement of and the metrics to report energy efficiency within the data centre is a relatively new area that is not yet adequately developed.

5.2. Demand forecasting

The importance of business demand forecasting is often overlooked beyond the initial ‘wet-finger’ demand figures that are needed to support the business case for any new data centre facility. It is however a demonstrable fact that getting the most accurate and granular view of business demand and how that translates to IT demand in advance of committing the data centre design, is the largest factor in achieving a target design efficiency.

As mechanical and electrical engineers understand, knowing what load will be placed on their systems within the data centre as well as when and how this load will change and grow is critical information to enable them to design a suitably modular facility which can operate efficiently under both initial and fully filled out loads. An explanation of design capacity and its impact on energy efficiency can be found in section 6.1.

Business demand forecasting is not a simple task within a University environment. Furthermore the follow-on task of Business Impact Analysis can also be very challenging within an environment that operates on a very loose supplier-customer relationship basis.

However, difficulty aside, the effort expended in carrying out these activities in order to produce a clear IT requirements forecast is well invested time in terms of how efficient the new data centre will be on day one and also how flexible the resultant data centre design is in it’s ability to deal with variability in forecast, technology steps, unexpected requirements or demand yet still be able to operate in a highly efficient manner.

While an initial study had been carried out by a third party which helped produce the figures that went into the business case, a much more concerted effort was required by the computing services department in order to get the level of data required by the people designing the data centre M and E infrastructure.

Business Impact Analysis was found to be another challenging task within the University environment as defining ‘Business Impact’ could vary from one department to another. The normal approach would be to identify the business services provided by the computing services department and carry out an impact assessment to understand what level of design resilience was required from the IT platform(s) that underpinned that service. In this case there was much debate around what actually represented services as the computing department offered low level services such as networking, DNS, authentication to some departments and high-level services such as email, collaboration and intranet services to others.

However it was well understood within the computing services department after years of support experience what level of resilience or redundancy was required for most of the major services both low and high level. This enabled the department to add another dimension to the demand forecast; ‘do I require redundancy?’ to achieve the required resilience.

5.3. IT requirements

One of the features of the initial OUCS CMR design was limited UPS capacity due to constrained physical space and budget. Having established the business demand forecast along with an understanding of the level of resilience required on a service-by-service basis, the computing services department were encouraged to make efficient use of the UPS resource by creating different infrastructure resilience levels to maximise the use of the UPS where it was needed most. For example, for dual power supplied IT equipment the following simple policy could be adopted:

Resilience level requiredA-feedB-feed
High UPS UPS
Medium UPS Non-UPS
Low Non-UPS Non-UPS

The additional advantage available in this instance was the fact that the new CMR was planned as incremental rather than replacement capacity to the existing MR. This would allow the computing service department to gain a much higher level of achieved reliability by splitting critical services across both facilities and only require the medium level of local UPS resilience.

Finally it was discussed and accepted that in general IT equipment lifespan was around 10%-20% of the expected lifespan of the CMR itself and that a CMR designed today wouldlikely run into technology generation mismatch issues within 3-5 years. The best example of this that can be observed in almost every data centre today is the issue of power density. In other words the general trend for equipment to get smaller while heat output continues to rise. This leads us to look at a much more modular and flexible CMR design which is in harmony with the energy efficiency goals of modular provisioning of M and E infrastructure to maximise the point in time load and thus the energy efficiency.

5.3.1. Data centre (CMR) requirements

Having already made a solid case for a more modular design, it was decided that addressing the power density issue to help future proof the facility whilst maximising the efficiency was the critical step in the CMR design requirements.

Many variables need to be considered to achieve an optimum design. The initial design for Oxford was a standard raised floor with underfloor cooling in conjunction with a hot-aisle / cold-aisle design and opposing CRAC units.

The physical requirements being driven out of the IT requirements were steering the CMR design to a zoned approach with different zones being capable of supporting differing power densities.

The simplest approach to achieving this was to use either partial or full hot or cold aisle containment with a pair of CRAC units matched to each aisle end. This design would enable the CMR to start on day one with a single pair of CRAC units cooling a single ‘zone’. The partial or full containment would ensure minimum air remix and thus maximum efficiency. It was also agreed that containing the hot or cold air and avoiding remix would allow a much better tracking of fan power within the CRAC to IT load within the zone, again ensuring maximum efficiency through matching the delivered M and E cooling capacity to the local IT load.

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