Data Center Temperature

There are presently many articles and posts around encouraging increasing data center temperatures.  ASHRAE has widened the temperature and humidity recommendations for data centers, and many blogs are recommending and discussing drastically increased temperatures.   Lately the discussions have been around the work environment of over 110F in data centers.  Note, that these high temperatures are in new or re-designed data centers with hot/cold isle separation (hot isle containment or cold isle containment).

Existing data centers should definitively investigate possible temperature increases as well as humidity range broadening.  However any such temperature change should be made slowly and with considerable monitoring.  In traditional data centers it is easy to get upside down with temperature changes.

Keep in mind that the recommendations are for  equipment inlet temperatures.  The temperatures over 90F that are being talked about are rack outlet temperatures.  Invest in at least some moderate temperature monitoring for the rack inlets before making any changes.  Get at least some trending data before changing temperatures and change them gradually.  You want to make sure that you are not getting a lot of hot air recirculation through the equipment, particularly top of rack and end of isle trouble spots.  If your data center doesn't already have them, invest in some rack blanking panels to prevent recirculation within the rack.

Some other points to watch for are that in some traditional data centers with CRA/C units against the walls you might find a slight problem with the location of the temperature sensor for the CRA/C units.  Many of these units are designed for open returns usually an open top for down draft units.   Some of these units have their temperature sensor right on top right in the return air stream.  So these units are measuring and using for control, air that is not the rack inlet supply temperature.

So the CRA/C unit is being controlled by the hot isle temperature instead of the cold isle temperature, but you only care about the hot isle temperature.  Therefore to get the cold isle temperature up to 75-77F the CRA/C set point might have to be set up to 80F or 85F.  The best results will be from relocating the temperature sensor to the cold isle or using multiple remote sensors in the cold isles.

You may not see much savings though depending on the type of cooling involved.  If you are using chilled water you might want to experiment with increasing the water temperature once you have the data center temperature stabilized.  You will want to keep monitoring the data center rack inlet temperatures as you increase the chilled water temperatures.  The highest savings will come from economizers, either chilled water or air systems.  As the data center temperature is increased the effectiveness of economizers increases.

Ultimately you should have a real time power monitoring system in the data center in addition to the temperature monitoring system, before making any changes.  This will help insure that there is some savings as there will be a temperature that proves to be most efficient for your data center and equipment, above which efficiency will start to decrease.

Use total data center (including cooling) energy use to find the best temperature for  your data center.  Do not use PUE for this.  PUE can be deceptive when changing data center temperature.  The problem comes from the fans in the IT equipment.  They are usually variable speed, temperature controlled, such that the fan speed starts to ramp up as the inlet temperature goes above 78F.    Most computers have 5 to 10 fans leaving about 300 fans per rack.  What happens is you decrease the infrastructure side of PUE (cooling) at the same time you increase the IT side (IT equipment fans) which shifts much of the cooling costs to the IT equipment which is much less efficient.  This will result in lowered PUE but increased overall energy utilization.  Using the overall energy utilization will avoid this situation.

You might also investigate moving the CRA/C units to be aligned with the hot isles if they are not already so aligned.  Another option is to install ducted returns from the hot isles which may be more efficient and/or cheaper to implement.  It may be possible to use the data center drop ceiling for a ducted return plenum.  These changes will allow you to increase temperature further and maintain more accurate inlet temperatures.

Software efficiency IS part of data center efficiency

 A few companies are spending effort to optimize hardware and processes towards the reduction of energy in the data center.  Some companies have facilities based projects and others IT based, with a few doing both.  The graphic below from a white paper at Emerson depicts the current thinking in that the further to the left you can save a watt, the more actual savings you receive (assuming right scaling). Thus the further to the right of the graph you save a watt, the less additional savings you receive through this cascade.

Graphic from white paper at http://emerson.com/  

What is missing from this graph, and not often spoken of in data center efficiency conversations, is software efficiency.  I am not talking about virtualization which is allowing you to make better use of hardware and is thus part of the "server component" in the cascade chart above.  I am talking about actual algorithm optimization. 
There are still code shops out there that attempt to optimize their code but as most of us know, most code is rarely optimized these days.  Code optimization has long since fallen under the Moore's Law knife of accounting; "It's cheaper to buy faster hardware than to pay for developer time".  Often time to market pushes aside code optimization (and some times even debugging).

If converting an application from say perl to C++, or simply turning on some compiler options, allows a bit of code to finish in 30 minutes instead of 60 and/or to use less RAM then you likely have a significant measurable power difference that can then trickle down the cascade effect.  I am not by any means saying that all perl or java code should be converted to C or C++ (or any other language).  Just that if you have a piece of code that takes a significant amount of time to run OR is run a significant number of times, spending some effort to optimize it can result in significant savings.

Here is another example.  Say there is a large web application written in some fictional interpreted language (say Perava ;).  The bulk of this code is infrequently hit and performs perfectly well.  But there is one function that is hit repeatedly for every web page.  This function takes 4 seconds to complete and to meet performance requirements for the number of web customers etc. the company deploys 10 redundant servers.  Each server uses 300 watts for a combined 3Kw for servers or following the above cascade up, about 8.5Kw in the data center.
If the optimization of the one segment of code or translation to some fictional compiled language managed to cut the run time in half (overly simplistic I know), so that only 10 servers were needed, and thus just 1.5Kw for servers or just 4.25Kw in the data center.

I have seen this same thing in high performance grid computing as well.  By just turning on optimization flags when compiling programs that are run 100k times a day for minutes at a time, managed to eliminate the need to expand the compute cluster.

We are starting to see some real push towards compiler optimizations particularly around auto-parallelization, which with modern chips is proving hugely successful.  Because of this cascade multiplier effect we can see some real gains on software optimization efforts.

There is another area for optimization which is between the software and hardware layer.  We are used to getting the right hardware to meet the software specs, but what I am thinking about is building the software towards hardware specs.  For example instead of building a single threaded process that would require a very fast processor, build a multy threaded process that can take advantage of a lower watt multi core processor (yes this is already the case for commercial software but in house software can do the same).

Probably a much better and direct example is an in house application that currently, to meet performance requirements keeps all the data in RAM for a particular job.  This might be able to be rewritten to use a combination of much less RAM, multi processing, and SSD (Solid State Disks) to reach even better performance than the original.   I am not talking about just putting SSD drives in a server or using SSDs for SWAP and running the application, but rather the application is altered to use the much faster access times of the SSD.  Bioinformatics, geo-imaging, weather simulations, and many other large data set research programs currently use large RAM systems, MPI clusters, or other methods to handle the large data sets.

Here is a great article on how facebook is drastically increasing performance by cross compiling PHP to C++.

Resolve to Measure PUE in 2010

Measure PUE in 2010! Why measure PUE after I blog about it not being perfect, just last month?
Well, it is better than nothing. Actually it is more than that, it is the minimum insight into your data center.
Think of driving your car late at night in an unfamiliar location. You have no idea were the next open gas station will be:
How comfortable would you be without a gas gauge?
This auto analogy isn't too far off. Many corporate data centers have some monitoring system to let someone know :

• when the temperature gets too high
• when there is a water leak
  
• generator status
• UPS (battery) status

And then on the IT side there is often some monitoring system or other keeping track of most of the critical servers, storage, backups, firewalls, even critical applications. Often there are all kinds of graphs and analysis of these systems with trends and pretty graphs over time showing how much work has been done in the data center.
With all this often real time monitoring and trending, few corporate IT managers have no idea what their date centers PUE is for any point much less over time. Though PUE would be more closely related to MPG than a gas gauge. PUE gives you a base line. As an IT or facilities director, hopefully you are looking at reducing costs in this economy for 2010. The data used to calculate PUE can easily be used to calculate energy cost of the data center.
IT directors are used to showing costs of new projects amortized over time showing costs for:

• computers
• storage
• network
• cables
• support
• replacement hardware
• helpdesk
• even data center space

but usually not electricity. Some Directors are starting to include anticipated electric costs in the projections but most still don't consider it because the costs are not in their budget. Getting individual project electric cost projections can be much more difficult than measuring the data center as a whole. Planning a major new project for 2010, maybe some virtualization or new storage. Measure PUE before and after implementation.

Getting back to the gas gauge theme, my first cars were all older than me. They all pretty much had the same instruments in the dash: speedometer, gas gauge, odometer, and check engine light. My dad was a master mechanic from the navy and he had a term for that check engine light. He called it an "idiot light" because you were an idiot to drive with it on. If you don't measure PUE then you are relying on the "idiot light" which for many data centers comes in the form of high temperature alerts, and by then things are too late.

I have several vehicles now, from my motorcycle to a prius. Now that prius puts that gas gauge and "idiot light" to shame. There is a row of lights to tell me what is going on with the engine, check engine, change oil, change filter, tire pressure, rotate tires. Right in the middle of the car is a computer display showing instantaneous MPG, MPG for since last reset, a graph of MPG for the last 30 minutes, and a gas gauge.

This is the kind of information you want for your data center. A nice little graph showing how much energy is being used every 5 minutes. If you don't measure the PUE for you data center than it is worse than driving a 1955 Cadillac with a broken gas gauge (you do not want to guess how much gas you have in a car that gets less than 10 MPG).
Lets get started in 2010 by measuring the PUE.

Data center efficiency measure

There is a lot of talk about Green data centers or data center efficiency, but how does one measure data center efficiency? We would have to define efficiency first. Most Chief Financial Officer's (CFO's) would define efficiency as something along the lines of getting the needed work done with the least money spent, or better yet, getting the most work completed with the least possible money spent. Seems rather simple doesn't it.

So what goes into the data center? Well, the CFO would say pretty much almost every dollar that IT spends and that is spent in support of IT, as well as all the money spent on behalf of the data center by facilities and security. That's actually the easy part. How do you define the work done by the data center? This is not as easily defined. Do we use the number of bytes generated? Many processes perform considerable work just to reduce data.

For something as complex and flexible as a data center, defining the efficiency is very complex. This is why a simple rule of thumb such as PUE (Power Usage Effectiveness) and DCiE (Data Center infrastructure Efficiency) are so commonly used. PUE is not too difficult to measure. PUE is best calculated by measuring the ratio of total energy consumption (servers + cooling + power distribution + UPS) to 'useful' energy consumption (servers only). The ultimate efficient data center would have a PUE of 1, where the average enterprise data center is about 2. In general using PUE is a good start and seems pretty straight forward; a reduction in power used on the infrastructure side, gives lower PUE. However there are some anomaly's such as, increasing server power usage results in reduced PUE, though clearly less efficient. PUE and DCiE (which is a simple mathematical conversion of PUE) do not actually relate to the work done, instead they simply measure the loss of power in the infrastructure side of the data center (UPS, cooling, power distribution).

DCPE (Data Center Power Efficiency) which measures useful work to total facility power, is much better in theory, but more difficult in reality to measure. How does one define useful work for the entire data center? Until the Green Grid group comes up with a better definition of DCPE and "useful work", I suggest we use SWaP, which uses the potential for work (cpu performance benchmarks). SWaP is simply PERFORMANCE / (SPACE x POWER). In most data centers space is less important than power so a simple weight constant can be added to the equation. So what does "Performance" mean in the equation? You simply define it for your data center and systems;

• for storage for example you might define it at as capacity in GB or maybe TB
• for switch gear, as bandwidth maybe in Gb/s
• for computers, as relative performance metric to the use of the system.

SWaP does not measure anything about the data center power or cooling plant, just the efficiency of the server itself. With SWaP measuring the potential of the IT hardware for efficiency, PUE measuring the energy efficiency of the infrastructure (power distribution, UPS, and cooling), we come close to a total data center efficiency measure.

To evaluate the total efficiency of a data center, first measure the infrastructure efficiency with PUE, then measure the efficiency of all the major systems in the data center with SWaP. Then devise a plan to increase efficiency of BOTH. Often the systems measured by PUE are maintained and managed by the facilities department while the systems measured with SWaP are maintained by IT. There is no point designing a new infrastructure for the existing IT equipment and typically existing facilities are often not appropriate for the new optimized IT equipment.
For example, suppose you have a large data center that is mostly tied up with storage systems and storage capacity has grown 50% every year for the past decade. The facilities group would have seen this growth in power and cooling demand and a data center efficiency initiative on their part using PUE would likely involve more efficient systems as well as an increase in capacity. However the IT side would be using some measure of the efficiency of the storage system itself, using SWaP they decide to use one of the new hybrid storage systems and move from thousands of 72GB 15K RPM fiber disks to a system using hundreds of 2TB drives with some SSD drives for cache. Resulting in a drastic reduction in power and cooling requirements.

Data center power

A recent study by Lawrence Berkeley National Lab, Self-benchmarking Guide for Data Center Energy Performance, revealed that in a typical data center installation, an average of 33 percent of total power goes to IT equipment. The rest is consumed by cooling (50%), the power system (9%), and lighting (8%). The most efficient data centers can achieve 80% power utilization for IT equipment. This measurement, however, does not take into account the efficiency of the computer systems at doing the desired work; only the ratio of electric power for computers vs. power for support equipment.

The real goal is to do the same or more electronic work using less power. Many data centers have expanded to the point where there just isn’t enough power or cooling to allow new projects. Expanding data center power and cooling infrastructure can be very costly, and will only result in increased annual costs. Spending the money to make the center more efficient solves the same problem and reduces expenses while setting the data center on the GreenIT path.

The simplest and most direct way to reduce power consumption in a data center is to reduce the power used by the equipment. In the average data center, for every watt reduced on direct electronic equipment (computers, network equipment, and storage), at least one more watt will be saved on the facility side (HVA/C , UPS, power distribution). Further, in the average data center this equipment is already at or near capacity. Therefore the most direct path to savings is in reducing the IT equipment power needs with such methods as consolidation, virtualization, use of larger disks in storage systems, etc. Efforts spend on infrastructure without corresponding IT effort is wasted, as any savings will eventually be re-absorbed by continued wasteful IT side growth.

Successful efforts must include both Facility and IT systems. This is the crux of the problem as these two groups generally have little to do with each other with very different goals and needs. Facilities often has the electric costs and infrastructure maintenance costs, with goals like reduce electric bill, maintain data center temperature and power. IT usually doesn’t have the electric costs for the computers it maintains, and has goals such as maintain up time, reduce equipment and IT maintenance costs. None of these goals are in opposition but the two groups do not likely talk to each other and many of their terms sound like a different language to each other.