The need for a fluid data centre, what that actually means, and the hurdles in achieving this goal

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NDBS: Helsinki 22nd September 2016The need for a fluid data centre, what that actually means,and the hurdles in achieving this goal.

futurefacilities.com

Optimal Performance?


The Data Center ChallengeBusinesses are subject to constant change, and this in turn means change for IT and the data centre facility.

RiskWaste


The Decision Making GapData Centre Facility1001010101101101101101010101110110101010001011001001010101010110IT

DataProcess

EngineeringThe Fluid Data Center fills the decision making gap and predicts the engineering impact of change

DCIM has started to bridge the gap for Data and Process

but, if using DCIM alone, the operator must make decisions without knowledge of the engineering impact they may have on the other side of the gap.

The result? Fear of failure.


The Business Impact of the Engineering Gap1001010101101101101101010101110110101010001011001001010101010110

Data GapProcess Gap

Engineering GapLoss of hardware AvailabilityLoss of Business

Stranded CapacityWasted CAPEX

Loss of Cooling EfficiencyIncreased OPEX

Cost IT

Data Centre Facility


How Can We Predict the Engineering Gap


Predicting the Impact of Change314

213314

213

Will this tip right or left?Will this tip right or left?


Predicting the Impact of Change314

213Tipping force: 4 x 2 = 8Tipping force: (1x3) + (3x1) = 6Tipping force: 4 x 2 = 8Tipping force: (3x3) + (1x1) = 10314

213


A data center is much more complicated. We can not calculate it by hand, but it is still predictable using engineering simulation:

3D Modelling to represent the data center.PSS (Power System Simulation).CFD (Computational Fluid Dynamics) to predict cooling.

Together, these provide a safe, off-line environment to try out any proposal and remove the fear of change.Predicting the Impact of Change Engineering Simulation


2 layouts, one works... one failsPredicting the Impact of Change Engineering Simulation


Fluid Data Center Case Studies


One investment bank acquires another. Need to consolidate data centers.

Different IT hardware strategies.

DCIM told them they had the space, power and cooling

Fear: Would it work?

Can we add the extra unplanned IT load to the left side without the risk of downtime?

?Fluid Data Center Case Study 1 - Avoiding Risk


The migration should not go ahead. 19% of the IT hardware would be at risk.

Fluid Data Center Case Study 1 - Avoiding RiskThe Fluid Data Center bridges the Engineering Gap and predicts the outcome:


The data center operator used engineering simulation again to find out how to make it work. Will any of our proprosed ideas work?1) Upgrade fans in cooling units2) Install additional cooling3) Migrate the applications

Fluid Data Center Case Study 1 - Avoiding Risk


Engineering simulation gave the operator the confidence to find a solution within their fear lines.

Upgrading CRAH FansInstalling In-Row CoolingInstalling Equivalent Rack-Mount Servers

Fluid Data Center Case Study 1 - Avoiding Risk


Design PUE = 1.6 Operational PUE = 2.1

A colocation data center had a design PUE of 1.6.

DCIM showed operational PUE was 2.05

Best practice suggested that three air conditioning units should be turned off.

Fear: Will we lose resilience?

How much can we reduce the DC infrastructure energy cost without losing resilience?

?Fluid Data Center Case Study 2 Reducing PUE


Using engineering simulation, the DC manager could predict the impact of relying on best practise:

PUE would reduce from 2.05 to 1.90.

No IT hardware would overheat.

No SLA would be broken.

However, the data center had to be able to cope with another single cooling unit failure

turned off turned off turned off Fluid Data Center Case Study 2 Reducing PUE


Engineering simulation showed:

If a fourth unit failed, rack temperatures would exceed the SLA and IT would overheat.

The best practice guidelines were wrong - three cooling units should not be turned off to save energy.turned off turned off turned off failed

Fluid Data Center Case Study 2 Reducing PUE


Further simulations showed that two units could be turned off, reducing the PUE to 1.95 without introducing any risk.

The best balance between facility cooling and IT demand was found: the Fluid Data Center became a reality.turned off turned off failed

Fluid Data Center Case Study 2 Reducing PUE


The Journey


Green Grid New Performance Indicator metricThe Green Grid has developed a new multi-metric view that provides a broader understanding of data center cooling performance than PUE alone

Efficiency is important but so is the performance of a DCs cooling system and its resiliency.

All three Efficiency, Conformance and Resilience are inextricably linked. You can improve one to the detriment of the other two.

The new Performance Indicator (PI) allows facility operators to better predict the outcome of potential changes. Simulation is used to predict the effects of amending any of the three core parts of the DC

Target RangeCurrent RangeFuture 100% Load


Users of the performance indicator can make the choice to select the areas in which they maximise outputs and where they have risk toleranceHyperscale Business DriversHigh EfficiencyVirtulised IT allows for failureEnterprise Business DriversNo Risk of DowntimeEfficiency Sacrificed

Colo Business DriversCompetitive edge on PUErSacrifice Conformance to maintain SLAs

Making Choices for Your Business


Level of AssessmentCurrent StateFuture Simulated StatesLevel 1Basic(Measured)N/ALevel 2Advanced(Measured)N/ALevel 3Basic/Advanced(Simulated or Measured)BasicLevel 4Advanced(Simulated or Measured)Advanced

PI and Simulation


Thank You!


Technology

The need for a fluid data centre, what that actually means, and the hurdles in achieving this goal