Virtualization for efficiency: by Kathrin Winkler, The green grid

VIRTUALIZATION for
EFFICIENCY
Kathrin Winkler
Director, The Green Grid
VP,EMC Corporation

Copyright © 2010, The Green Grid
About The Green Grid
• A not-for-profit global consortium focused on driving
energy efficiency in the computing ecosystem
• Developing meaningful and user-centric metrics to
help IT and Facilities better manage their computing
resources
• Developing and promoting standards, measurement
methods, best practices and technologies that
support these metrics
2
2

x86 Servers are Driving Power Consumption
 Servers = 40% of total data center electricity use
 Volume servers = 85% of all server electricity use
– Up from 70% in 2000 at 17% CAGR (compound annual growth rate)
 Servers drive other IT and site infrastructure consumption
 Start by solving the problem “upstream” at the source!
Courtesy of
VMware
3

Processor Capacity Trends
MHz Capacity Requirements +10% YOY
MHz per server
0
1000
2000
3000
4000
5000
6000
Y2006
Y2006
Y2006
Y2006
Y2007
Y2007
Y2007
Y2007
Y2008
Y2008
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Total MHz
Used Mhz
Use
5%
Courtesy of VMware
4

Power Consumption vs Utilization
Courtesy of
VMware

Sample with 500 x86
Servers
Category Innovation Production
Mission
Critical Total
Server Count 250 175 75 500
Utilization 3% 6% 10% 5%
Watts (161 per
server) 40250 28175 12075 80500
Consolidation
Ratio 15:01 10:01 5:01 ~10:1
Remaining Servers 17 18 15 50
Post Consolidation
Utilization 50% 50% 50% ~50%
Post Consolidation
Watts (230) 3910 4140 3450 11500
Energy Savings -
Watts 36340 24035 8625 69000
6

Determine the Opportunity
for
Consolidation of Servers Using
Virtualization
Determine the Opportunity
for
Consolidation of Servers Using Virtualization
7

Gathering Intelligence
• Collect information on all servers
 Agent-less tools are available
 Discover, inventory, and collect performance
 Group servers and make consolidation
recommendations
• Characterize workloads
 Resource consumption over time
 Workload volatility
 Potential risk
8

Minimum Information
• Configuration & Utilization
 Make, model
 Processor speed, core count, etc.
 Memory
 Network
 Local storage
 OS and patch level
 Software
• Risk Items
 Ownership
 Service level requirements
 Security, privacy, regulatory or political issues
9

% Processor Time
(not normalized)
0
5
10
15
20
25
30
35
40
45
50
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Single
1.26
GHz
(2) 2.8
GHz
Utilization
Hour
Courtesy of
VMware
10

Normalized Processor MHz Load
Single
1.26
GHz
(2) 2.8
GHz
Time
Courtesy of
VMware11

Load Characteristics
Courtesy of VMware12

Determine Workloads that Can Coexist
• Exclude workloads which must reside on
their own servers
• Identify workloads with above average or
volatile workloads
• Group workloads that can co-exist
 Same Location
 Same Service Level
 Same Environment (Innovation, Production,
Mission Critical)
13

Implementation Strategy
• Phase 1
 Low resource requirements
 Low volatility in resource consumption
 Low risk
• Phase 2
 Low utilization & volatility of resources
 Medium risk
• Phase 3
 Everything else that can be virtualized
14

Consolidation Hurdles
• Awareness
 Infrastructure knowledge
 Virtualization knowledge
• Technical
 Data center preparation
 Application restrictions
• Financial
 Servers not fully depreciated
 Budget freeze
• Political
 Perceived loss of control
15

Strategies to Clear the Hurdles
• Conduct a Complete Assessment
 Include all servers, all locations
• Develop Data Center Model
 Based upon post consolidation
 Include growth estimates
• Develop Transition Plan
 Full consolidation takes more than a year in most cases
 Infrastructure needs will step down with server reduction
• Market Internally
• Develop Rewards
 More independence
 Lower cost
16

Virtualization – Two Steps
WP #19 USING VIRTUALIZATION TO IMPROVE DATA CENTER EFFICIENCY
Data Center
P
O
W
E
R
C
O
O
L
I
N
G
Data Center
P
O
W
E
R
C
O
O
L
I
N
G
Data Center
Servers
P
O
W
E
R
C
O
O
L
I
N
G
Storage
Network
#27 Impact of Virtualization on Data
Center Physical Infrastructure
Servers
Storage
Network
Servers
Storage
Network
17

Physical Infrastructure
Loss Characteristics
No
Load
Electrical
Loss in kW
(Waste due to
inefficiency)
Equipment Loading
Full
Load
50%10% 30% 90%70%
NONO--LOADLOAD loss
PROPORTIONALPROPORTIONAL loss
SQUARESQUARE--LAWLAW
loss
Powered on without
IT Load
Losses that
occur at higher
loads (ex.
switching,
conduction)
18

Data Center Power Efficiency Metrics:
PUE and DCiE
19
PUE = Power Usage Effectiveness =
Total Facility Power
IT Equipment Power
1
PUE
IT Equipment Power
Total Facility Power
=
Data Center
Infrastructure Efficiency
DCiE = =

Efficiency Curve
(not considering redundancy)
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
6.00
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
% IT Load
PUE
Reducing IT Load
Right-Size
Power-Cooling Equip
20

Which Density Strategy?
 New or existing data center?
 How many high density servers?
 How much bulk power and cooling?
 Can IT devices be moved around?
High-density racks with
virtualized servers
Low-density racks with no
virtualized servers
Low-density racks with a mix of
virtualized and non-virtualized
servers
Spread the
load
Consolidate the
load
Spectrum of post-virtualization
densities
Supplemental
Cooling
21

Spread the Load Strategy
• Easy…
• No new power and cooling equipment required
• But…
• Takes up valuable floor space - lots of white space
• No power/cooling efficiency benefit
• New configuration could compromise existing cooling
strategy/architecture
• Host servers inherit same availability as less critical servers
22

When to Spread the Load
• Post-consolidation average kW/rack < 3 kW
• Placement of IT equipment is flexible
• IT equipment power and cooling redundancy
requirements could limit mobility of IT equipment
• Additional power drops available only in certain
locations
• Cooling redundancy may be based on physical
location
• Open U-space is available in existing racks
23

Supplemental Cooling Strategy
• Low Capital Expense
• But…
• Effective only for the lower range of high density
• Temporary solution for cooling of hot spots
• Supplemental Cooling Examples
• Installation of fans to boost the cool air supply from the
CRAC to an enclosure
• Variable speed drives (VFD) on CRAC units
• Better hot isle containment such as special racks
• Installation of row-based air conditioners to address
localized hot spots
• A rear-door heat exchanger shortens the air path to back
of rack from CRAC
24

When to Deploy Supplemental
Cooling
• Post-consolidation average kW/rack is
3 kW – 5 kW
• No flexibility over the placement of IT
equipment
• There is limited capital budget
• Options:
 Specialty (grate-type) floor tiles or fans
 Portable spot air conditioning units
 Special racks or rack mounted cooling devices
 Row-based air conditioners between racks
25

Consolidate to High and Low
Density Areas Strategy
• Takes more planning…
• Needs advance planning
• Perhaps retrofit only high
density area
• But has big
advantages…
• Predictable high-density
cooling
• Enables higher-efficiency
cooling
• Better utilization of floor
space
• Enables targeted
availability/redundancy
Local cooling for
hi-
powered, dense
equipment
26

When to Designate High & Low
Density Areas
• Post-consolidation average kW/rack >5 kW
• Subsection of data center is available for
pod(s)
• Desire to improve overall data center
efficiency (PUE)
• Use high density area to focus on cooling
efficiency :
 Improve hot air containment
 Reduce air path length, e.g., top of rack cooling,
row cooling, or rear door heat exchanger
27

Characteristics of Dynamic Loads
Hot spot
Hot spot
1 2 3
Row CRACs sense elevated temperature and
increase fan speed to remove extra heat
from hot aisle
When temperature decreases, row CRACs
decrease fan speed to conserve energy
Row CRACs added
Room CRACs
removed
CRACCRAC
CRAC CRAC CRAC
CRAC
Caution:
• Loads constantly shift as data center experiences higher/lower activity
• IT movement (e.g., vMotion) is typically unaware of
cooling limits in any physical space
Row based cooling, CRAC VFD are methods to adjust to variable IT loads
28

Other Considerations When Moving
to High Density
• Typically complicates cable management
 Leftover cables when moving from low to high density
 Additional cables because of additional IT equipment
• Requires higher rates of air flow to support heat
load
• Increased weight of racks due to additional IT in
rack
• Impact on power infrastructure (breakers, panels,
etc.)
29

Summary
• Surveying “Things To Consider” before /during /
after IT Virtualization
• The virtualization savings entitlement can be
multiplied if power and cooling infrastructure is
modified or upgraded
• Spreading the load, supplemental cooling, and
designation of high density and low density areas
can help further enhance data center efficiency
performance
• No one size fits all
30

For More Information
• www.thegreengrid.org
for whitepapers, training,
and content
• For membership, please contact us:
+1 503-619-0653 or
admin@lists.thegreengrid.org
31
Thank you to our members for your
continued dedication and involvement in
ICT energy efficiency efforts!

Thank you!
For more information please go to
www.thegreengrid.org

Virtualization for efficiency: by Kathrin Winkler, The green grid

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