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The Datacentre Energy Waterfall Opportunities to Improve Energy Efficiency in Datacentres from the Utility to the Processor National Energy Efficiency Conference Singapore - October 2013 Ed Ansett i3 Solutions Group
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1. The Energy Waterfall
2. Holistic View
3. Facilities – M&E / Building
4. IT Infrastructure
5. Platform Software and Applications
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Datacentre Energy Waterfall
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Green Datacentre Metrics Today
• PUE is the ubiquitous facility metric to measure energy efficiency
• CUE and WUE are also useful metrics
• Resource consumption (e.g. energy) is not addressed
Facilities
Technology Infrastructure
• Two metrics DPPE and nIT measure technology infrastructure energy
efficiency
• No industry consensus on use of these metrics
• Resource consumption is not addressed by these metrics
Software
• No metrics to measure software energy efficiency
• No metrics to measure resource consumption (e.g. energy)
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1. The Energy Waterfall
2. Holistic View
3. Facilities – M&E / Building
4. IT Infrastructure
5. Platform Software and Applications
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Application Chain x
Application Chain y
Application Chain z
Technology MEP Infrastructure Infrastructure a e.g. PDU 5 & 6
Technology MEP Infrastructure Infrastructure b e.g. PDU 7 & 8
Technology MEP Infrastructure
Infrastructure a+b e.g. PDU 5,6,7 & 8
Across Multiple Business Processes
Business Processes: Application Chain:
IT Infrastructure:
MEP Infrastructure:
Functional elements Applications , Application Management
Compute, Network, Storage, VMs and Ancillaries
Power, Cooling and Ancillaries
Business Process 3
Business Process 2
Business Process 1
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Reference IT Stack
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Energy is
wasted at
every level
of the IT
Stack
Datacentre Energy Groups
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• Eemb = energy used to manufacture the datacentre including the embedded energy used in the
materials to construct the facility and the physical IT infrastructure
• Eops = energy consumed during the operation of the datacentre
• Eops,ren = renewable energy generated on-site to offset or augment utility energy
• Enet = effective net energy used during the lifetime of the datacentre
Datacentre Embedded Energy
The total energy used by any datacentre:
Enet = Eemb + Eops – Eops,ren
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1. The Energy Waterfall
2. Holistic View
3. Facilities – M&E / Building
4. IT Infrastructure
5. Platform Software and Applications
6. Examples of Likely Technology Successes in
Singapore
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Facility and IT Infrastructure Energy Losses
Utility Power In
Facility Power Losses Hardware Power Losses
Power Used by Software Code
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Datacentre Environmental Standards and
Metrics are Gaining Acceptance
• EU Code of Conduct for Datacentres
• Singapore Standard SS 564: 2010 and Greenmark
• LEED 4 (Due now end 2013)
• CEEDA
Power : Cooling : IT Power Ratio
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Facilities
• Embedded energy is not considered
• Datacentres operating at too low temperatures
• Power and cooling over-capacity is the norm
• Typical PUE is higher than 2.0
• M&E topology design largely misaligned with IT
• Air management systems are the exception rather than the rule
• UPS battery autonomy is excessive
• Material recycling / disposal requires incentives
• Selection of sustainable materials requires incentives e.g. flywheels instead of
batteries
• Datacentre designs waste energy; caused by excessive design margins and
topologies
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Facilities
• Direct current power at LV (not ELV) is viable – improves EE and is more
reliable; fewer components
• Datacentre loads have historically been relatively flat (+/- 10%); this will
change due to:
• Dynamic resource allocation (e.g. V-Motion) • Cloud elasticity (on demand services ) driven by remote user activity
• Cooling systems will have need to respond faster to load changes
• Densification drives demand for larger power and cooling systems
• Variable densities waste energy due to over-cooling the datacentre to deal
with localized hot spots
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ASHRAE Operating Ranges for Class A1 IT equipment
ASHRAE’s X Factor Failure Rates
Recommended Allowable
Dry Bulb
Temperature
27oC 32oC
Dew Point 15oC Not
specified
Relative
Humidity
60% 80%
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Inlet Dry Bulb
(oC)
Increase in IT Failure
rate
( %)
20 0%
22 13%
25 24%
27.5 34%
30 42%
32.5 48%
Most physical IT equipment used in the datacentre is cooled using a combination
cooled air fed into the computer room by perimeter air-conditioning units, that
passes over heat sinks that increase the surface area of the component that
dissipates heat using internal and fans to increase the rate of heat removal from
the IT equipment. The difficulty with this method of heat removal is the poor
thermal capacity of air with respect to other fluids such as water, refrigerants and
inert hydrocarbon oils.
Datacentre Cooing Fluids currently include: • Air • Water • Dielectric Oil • Refrigerants/Phase Change Fluids
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Datacentre Cooling Media
The potential advantages for the use of these systems in Singapore are:
• Reduce the vulnerability of fuel supply lines.
• Increase government resilience to energy price and supply volatility
• Contribute to national goals, such as reducing reliance on fossil fuels and
cutting greenhouse gas emissions.
• Reduce vulnerability; fixed installations currently depend on the local electric
grid to power mission-critical systems and equipment.
• Scalable technology can supply base-load power for distributed power
production grids.
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Alternative and Renewable Energy Sources
1. The Energy Waterfall
2. Holistic View
3. Facilities – M&E / Building
4. IT Infrastructure
5. Platform Software and Applications
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IT Infrastructure Energy Losses
Hardware Power Losses
Power Used by Software
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70.00%
20.00%
10.00%
Servers
Storage
Network
Technology Infrastructure Energy Ratios
Server Idle Power
Zero Power at Zero Utilization
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Hardware Energy Efficiency
80 PLUS Certification – Power Supply Energy Efficiency
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80 Plus is great but incomplete
because doesn’t take into
account:
• Internal fan energy loss
• Internal dc-dc converter loss
Technology Infrastructure Today
Some success primarily with switched mode power i.e. Energy Star, 80+ program
Metrics not implemented
Infrastructure is overprovisioned
Virtualized servers only 20% - 30%
Storage virtualization gathering momentum
Server vendors offering power capping and power throttling
Idle state power has not effectively been addressed
Generally nothing has been implemented in networks – research is lagging
Storage slightly further ahead due to increased usage of flash/SSD technology
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Compute
• Power Supplies – Energy Star, 80+ program
• Increasing power density
• Direct current power supplies
• Virtualization
• Dynamic resource allocation.. Hyper V, V Motion
• Eliminating Server Idle Power, sleep states
• Power capping and throttling
• Fans and conversion loss energy overhead
• Internal heat-pipe to external heat sink
• Inert liquid server cooling
• 3D Stack DRAM
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Storage
• Storage Virtualization
• De-duplication
• Compression
• Tiering
• Auto-provisioning
• Phase change memory and memristor
• Spin down
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Network
• Energy aware dynamic workload allocation
• Energy aware routing using cognitive packet switching
• Reduced energy consumption via sleeping and rate adaptation
1. The Energy Waterfall
2. Holistic View
3. Facilities – M&E / Building
4. IT Infrastructure
5. Platform Software and Applications
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Software Environment Today
• Energy aware applications will provide significant EC savings
• Lack of awareness by end users regarding software energy consumption
• Energy consumption awareness exists mainly in mobile platforms
• Load handling in multi-core processors is key to EE and EC software
• Compilers not written to optimize EC
• Main energy consumers are CPU, GPU, memory and IO
• Many instances of non-virtualized applications
• Multi-threading reduces EC when multiple-cores execute faster than A lesser
number of cores
• Balanced / synchronized threads improve EC
• Deploy low power processors where workload permits
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Software
• Pre-fetching data and batched sequencing reduces disk calls and improves EC
• Where possible replace periodic timers event-driven or interrupt-based code
• Algorithms and data structures focus on speed of task completion as a means of
reducing EC
• Minimize extensive and / or recursive loops
• Use of drivers that are idle friendly
• Optimizing applications for speed can improve EC; by maximizing idle periods
• New compiler options should be written that prioritize EE/EC
• Emerging context aware applications utilizing sleep states
• Power limiting via clock speed or peak power
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Key Areas for Improvement
1. Energy efficient applications and platform software
2. Automated sleep state adoption
3. Dynamic optimization of application workload allocation
4. Dynamic voltage and frequency scaling
5. Dynamic power management
6. Energy and workload aware dynamic virtualization
7. Energy efficient hardware
8. Thermal performance
9. Power system efficiency
10. Renewable energy sources
11. Embedded energy optimization
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Thank You
Ed Ansett
i3 Solutions Group