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Distinguished Engineer, Chief Architect
Global Foundation Services
Microsoft Corporation
200+ CLOUD SERVICES
59 markets and
36 languages
76 markets and
48 languages
ChicagoQuincy
Dublin
Amsterdam
Hong Kong
Singapore
Japan
"Datacentershavebecomeasvitaltothe
functioningofsocietyaspowerstations."
TheEconomist
San Antonio
Multiple global CDN locations
Microsoft has more than 10 and less than 100 DCs worldwide
Boydton
Des Moines
Quincy, Washington 27MW 100% Hydro power
San Antonio, Texas 27MW Recycled water for cooling
Chicago, Illinois Up to 60MW Water side economization, Containers
Dublin, Ireland Up to 50MW Outside air cooling, PODs
Generators Substation Evaporators
Chiller Tower Battery Room
Electrical
Distribution
Pumps/Pipes
Operations
http://www.youtube.com/user/MSGFSTeam#p/a/u/0/hOxA1l1pQIw
Monolithic design and
construction effort
Typical large datacenter = 11 football fields
20 to 50 Megawatts
Huge $$$ Typical construction costs = $10M to $15M per Megawatt
Long lead time 18 to 24 months from design to online
Land
Core / Shell
Mech /Elec
Arch
Where the costs are:
>80% scale with power
<10% scale with space
2010+20081989-2005 2007
Reduced Carbon, Rightsized
1.05-1.15 PUE
Faster Time to Market
1.25 PUE
!
Quincy, WA Modular Datacenter
Tent City
ITPAC Proof of Concept Unit
AHU
IT
Mixing -
Evap
Drive change in the industry
through strong partnerships
http://www.eia.doe.gov/cneaf/electricity/epm/table5_3.html
Building Load
Demand from Grid IT Load
Demand from Servers, Storage,
Telecom Equipment, etc.
PUE = Total Facility Power/IT Equipment Power
Power (Switch Gear, UPS, Battery Backup, etc.)
Cooling (Chillers, CRACs, etc.)
Your mileage may vary!
3 Year TCO
Total Equipment Cost per server
Energy Usage Cost per server
Datacenter Capital Cost per server
Datacenter Operating Cost per server
$-
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
6 M$/MW 8 M$/MW 10 M$/MW 12 M$/MW 15M$/MW
AnnualCost
Capital Expenditure for Datacenter Capacity
Cap Ex Depreciation
&Energy @6c, PUE=1.2
&Energy @8c, PUE=1.5
&Energy @10c, PUE=1.8
Your mileage may vary!
Strategic
Use of New
Technologies
Drive Higher
Efficiency
Remove unnecessary components
Limit the platform expandability: # Slots, CPU Power
Right-size the power supply
Optimize the
Configuration
Application manages Data locality
Hardware manages Data locality, Transparent to Application
System bottlenecks are likely to be in I/O subsystem


www.globalfoundationservices.com
http://blogs.technet.com/msdatacenters
GFS Videos:
http://www.youtube.com/user/MSGFSTeam
Energy Efficiency Considerations in Large Datacenters

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