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Green Computing

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1Green Computing. 24of performance normalized to performance
2Green Computing. Current system at peak loads Energy efficient server
extremely wasteful Need energy to power still consumes ? power when doing almost
Need energy to cool 1000 racks, 25,000 sq no work Power efficiency –
ft, 10MW for computing, 5MW to dissipate utilization/power value Peak energy
heat Need a system more efficient, less efficiency occurs at peak utilization and
expensive strategy with immediate impact drops as util. decreases At 20-30%
on energy consumption. utilization, efficiency drops to less than
3Data Centers. Focus by green computing ? at peak performance.
movement on data centers (SUVs of the tech 25
world) 6,000 data centers in US Consume 26Toward energy-proportional machines.
61B kWh of energy in 2006 Cost: $4.5 B Mismatch between servers’ high-energy
(more than used by all color TVs in US) In efficiency characteristics and behavior
2007, DOE reported data centers 1.5% of Designers need to address this Design
all electricity in US Greenhouse gas machines that consume energy in proportion
emission projected to more than double to amount of work performed No power when
from 2007 to 2020. idle (easy) Nearly no power when little
4Data Centers. Within a few years, cost work (harder) More as activity increases
of power for data center was expected to (even harder).
exceed cost of original capital 27CPU power. Fraction of total server
investment. power consumed by CPU changed since 2005
5Goal. Fed. Gov. wanted data center CPU no longer dominates power at peak
energy consumption to be reduced by at usage, trend will continue Even less when
least 10% Same as energy consumed by 1M idle Processors close to
average US households. energy-proportional Consume < 1/3 power
6 at low activity (70% of peak) Power range
7 less for other components < 50% for
8 DRAM, 25% for disk drives, 15% for network
9Data Center Metrics. Metrics switches.
SPECPowerjbb benchmark and DCiE from Green 28Dynamic range. Processors can run at
Grid Green Grid – group of IT lower voltage frequency mode without
professionals Power Usage Effectiveness impacting performance No other components
PUE PUE = Total facility power/IT with such modes Only inactive modes in
equipment power Data Center infrastructure DRAM and disks Inactive to active mode
Efficiency metric DCiE 1/PUE. transition penalty (even it only idle to
10Future Vision. Sources of computing submilliseconds) Servers with 90% dynamic
power in remote server warehouses Located range could cut energy by ? in data
near renewable energy sources – wind, centers Lower peak power by 30% Energy
solar Usage shifts across globe depending proportional hardware reduce need for
on where energy most abundant. power management software.
11Current approaches. Some “low hanging 29
fruit” approaches Orient racks of servers 30Disks - Inactive/active. Penalty for
to exhaust in a uniform direction Higher transition to active from inactive state
fruit - Microsoft Built near hydroelectric makes it less useful Disk penalty 1000
power in WA Built in Ireland - can air higher for spin up than regular access
cool, 50% more energy efficient Countries latency Only useful if idle for several
with favorable climates: Canada, Finland, minutes (rarely occurs) More beneficial to
Sweden and Switzerland. have smaller penalty even if higher energy
12Current approaches. Google – trying to levels Active energy savings schemes are
reduce carbon footprint Carbon footprint useful even if higher penalty to
includes direct fuel use, purchased transition because in low energy mode for
electricity and business travel, employee longer periods.
commuting, construction, server 31Conclusions. CPUS already exhibit
manufacturing According to Google, its energy proportional profiles, other
data centers use ? industry’s average components less so Need significant
amount of power How? Ultra efficient improvements in memory and disk subsystems
evaporative cooling (customized) Yahoo Such systems responsible for larger
(are they back??) Data centers also fraction of energy usage Need energy
carbon-neutral because of use of carbon efficient benchmark developers to report
offsets. measurements at nonpeak levels for
13Current approaches. US government EPA complete picture.
has phase-one of Energy Star standards for 32Green Introspection by K. Cameron.
servers Measure server power supply 33History of Green. In the 1970s Energy
efficiency and energy consumption while crisis High gas prices Fuel shortages
idle Must also measure energy use at peak Pollution Education and action
demand Green Grid consortium Dell, IBM, Environmental activism Energy awareness
Sun VM-Wear AMD Green500 – 500 most green and conservation Technological innovation.
supercomputers. 34Gifts from the 70s. Energy crisis
14Data Center Product Specification subsided In the meantime advances in
Completion. 2009 Servers v1.0 2011 Data computing responsible for: Innovation for
Center Buildings Program 2012 UPS v1.0 energy-efficient buildings and cars
(uninterruptable power supply) 2013 Identified causes and effects of global
Servers 2.0 Storage v1.0 2014 Large climate change Grassroots activism,
Network Equipment v1.0 2015 Data Center distributing info about energy
Cooling Equipment v1.0. consumption, carbon emission, etc. The
15Current approaches. Replace old same computing technologies pioneered by
computers with new more energy-efficient hippie geeks (???) are the problem now.
But manufacturing through day-to-day uses 35What happened next. Call to action
energy Dell - reducing hazardous within IT community (what about the 80s??)
substances in computers, OptiPlex 50% more In 1990s General-purpose microprocessors
energy efficient Greenest computer company built for performance Competing processors
– VirtualBoxImages What is “Greenest ever-increasing clock rates and transistor
computer ever” ? Is MacBook air (pro) densities fast processing power and
greenest? exponentially increasing power consumption
16Goals for Future. Consider energy to Power wall at 130 watts Power is a design
manufacture, operate, dispose of Sense and constraint.
optimize world around us Predict and 36Better, but also worse? To reduce
respond to future events by modeling power consumption Multicore architectures
behavior (grown in performance) Benefit of – higher performance, lower power budgets
digital alternative to physical activities But Users expect performance doubling
E-newspapers, online shopping Personal every 2 years Developers must harness
energy meter?? parallelism of multicore architectures
17The Case for Energy-Proportional Power problems ubiquitous – energy-aware
Computing. Barroso and Holzle (Google). design needed at all levels.
18Intro. Energy proportional computing 37More problems. Memory architectures
primary design goal for servers Cooling consume significant amounts of power Need
and provisioning proportional to average energy-aware design at systems level
energy servers consume Energy efficiency Disks, boards, fans switches, peripherals
benefits all components Computer energy Maintain quality of computing devices,
consumption lowered if: Adopt decrease environmental footprint Can’t
high-efficiency power supplies Use power rely on nonrenewable resources or toxic
saving features already in equipment. ingredients.
19Intro. More efficient CPUs on chips 38Those data centers. IT helping in data
based on multiprocessing has helped But, centers Reducing energy with
higher performance means increased energy virtualization and consolidation Need to
usage. address chip level device to
20Laptops vs. Servers. Mobile device heating/cooling of building Need metrics.
techniques Multiple voltage planes, energy 39Yet another group. Metrics
efficient circuit techniques, clock SPECPowerjbb benchmark and DCiE from Green
gating, dynamic voltage frequency scaling Grid Green Grid – group of IT
Mobile high performance, short time professionals Power Usage Effectiveness
followed by long idle interval High energy PUE PUE = Total facility power/IT
efficiency at peak performance, low energy equipment power Data Center infrastructure
inactive states. Efficiency metric DCiE 1/PUE Benchmark
21Servers. Servers Rarely completely acceptance takes time.
idle Seldom operate at maximum 10-50% of 40Big government. US EPA Energy Star
max utilization levels 100% utilization specification for servers Will have impact
not acceptable for meeting throughput, US gov. procurements required to purchase
etc. – no slack time. energy star machines (already true of
22 monitors0 May be further gov. regulations
23Servers. Completely idle server waste (with Dems in power ??) EU implemented
of capital Difficult to idle subset of carbon cap and trade scheme, US to follow.
servers Servers need to be available 41Trade-off. How often to replace aging
Perform background tasks Move data around systems? 2% of solid waste comes from
Can help recovery of crash Applications consumer electronic components E-waste
can be restructured to create idle fastest growing component of waste stream
intervals Difficult, hard to maintain In US 130,000 computers thrown away daily
Devices with highest energy savings, and 100 M cell phones annually Recycle
highest wake-up penalty, e.g disk spin up. e-waste (good luck) Use computers as long
24Energy Efficiency at varying as possible?
utilization levels. Utilization – measure
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