Improving Data Centers Energy Reliability & Efficiency. December 2015

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Improving Data Centers’

Energy Reliability & Efficiency

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 Background  The challenges  Alternative solutions  SATEC approach  Advantages  Cases  Summary

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Data Centers Energy Challenges

 Increased demand for On-line Clouding services

 Data center energy consumption per m² is ×15-20 times higher compared with typical office

 Expected increase in Power consumption

While

 Worldwide demand for cleaner power generation solutions  Reducing power consumptions by increasing efficiency (PUE)

70%

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Type of data centers

 Corporate:

 _{In-House Data Center}

 Collocation:

 _{Hardware (servers), belong to customer}

 _{Facility and internal systems maintenance – by provider}

 Managed/Hosting (“cloud”):

 _{Hardware may be owned by the customer or the provider}  _{Hardware can be separated (at rack/ server levels) or shared}  _{Provider operates storage, engineering, security, electricity etc.}

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Tier 4 Most Common Tier 3 Tier 2 Tier 1

Data centers

Reliability & Availability

Increased outsourcing services

Enterprise Corporation 99.995% Uptime

23.3 Minutes Downtime (Y) 2N+1 Fully redundant

96 Hour Power Outage protection

Large Businesses 99.982% Uptime

1.6 Hours Downtime (Y) N+1 Fully redundant

72 Hour Power Outage protection

Medium-size Businesses 99.749% Uptime

22 Hours Downtime (Y)

Partial redundancy in Power and Cooling Small Businesses

99.671% Uptime

28.8 Hours Downtime (Y) No Redundancy

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Data Center – Common Metrics

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Data Centers Energy Challenges

“Worldwide Data is set to grow 10-fold by 2020 from 4.4 zettabytes to 44ZB” (IDC)

 Reliability

 Reducing investments  Density

 Efficiency (PUE)

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 Improving Reliability

 _{Load Measurement and notifications – down to rack level}  _{Power Quality Measurements and alerts, at critical}

locations

 _{“what if” testing procedures for measurement of loading}

in case of failures

 Improving Efficiency

 _{Measurement down to servers racks for accurate PUE}

segmentation

 _{Measurement and Control of HVAC systme including}

temperature and humidity levels

Improving DC Reliability

and Efficiency

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 Limited energy information – energy metering at

MCCBs only

 Price of Smart PDU prevents wide deployment

 Lack of power quality monitoring

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SATEC suggested approach

Balancing between expensive Smart PDU and lack of information of board measurement level only

EM720 High performance revenue meter + PQ & Transient PM180 High performance analyzer (PQ) BFM II Energy + Rev. meter

Main Input Backup

ATS UPS UPS UPS HVAC Distribution Boards UPS Switch Gear

~ ~

[N+1] Generators

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 Real time PDU level load monitoring for loading and

PUE segmentation

 High Accuracy energy monitoring

(utility check-meter, collocutors billing, losses)

 Cost effectiveness

 Power Quality analysis

 Leakage current alerts

 HVAC optimization based on actual data at rack

level

 What-if analysis

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Distribution Switchboard

Metering Scheme

Up to 200m

Temperature & humidity (Analog Inputs)

Up to 54 channels (27 racks)

Split/Solid Core High Accuracy Current Sensor

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 PDU’s

* BFM unit price based on 54 channel BFM II

Price comparison (CAPEX)

PDU Type Av. Unit _price Av Rack price _(A-B) _{standard PDU)}Delta (from

Regular PDU 200 400 -

Metered PDU 700 1,400 1,000

Smart PDU 1,100 2,200 1,800

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Error of Measurement (example)

Meter PDU SATEC BFM

Error for single metering point 2% 0.5% The mean square error of measurement

for 1 rack

(2 metering points)

2.8% 0.7% The mean square error of measurement

for 1 cage

(60 metering points = 30 racks)

15.5% 3.87% Total Power Reading Error

5kW×30 racks×40% loading×%error ±9.3kW ±2.3kW Billing error

(one cage, one year, US$ 0.15/kWh): US$ 12,220 US$ 3,022 Delta charging per cage US$ 9,198

) ( ) ( ) ( ) (x y z U2 x U2 y U2 z U + + = + +

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In advance info – to prevent shutdown and downtime

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 Standard 6 pulse (2 pulse for 1~) rectifiers (UPS,

server PS) cause harmonics. Modern AFE rectifiers reduce harmonics, but increase transients

 Transient frequency are high and can pass-through

UPS system

 “clean network” is not 100% “clean”

 High end Power Quality systems upstream and

downstream of UPS allow measurement of PQ

 With advanced thresholds and logs, prevention

action can be conducted and save $$$

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Downtime

 DC in Russia – lost 20% of servers

 Finding – Automatic operation of servers at specific

time (acceleration of computing power) lead to over loading of transformation currents

 Solution – constant measurement of racks levels +

logs + thresholds could provide in-advance notification and save

 _$$$

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PQ and Safety Issue

 Investments for new DC

=3000-5000 USD/m2

 DC (1500 m2)= 3-5M$

PQ+ Fault Recorder+ High Accuracy Billing Meter= 3000 USD

 Complete System for Power Quality Control needs 10-12k$ Investments  4-5 measured points

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