the datacentre energy waterfall opportunities to improve energy … · 2018-10-02 · the...

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