green computing
DESCRIPTION
Green Computing. Green Computing. Current system extremely wasteful Need energy to power Need energy to cool 1000 racks, 25,000 sq ft, 10MW for computing, 5 mw to dissipate heat Need a system more efficient, less expensive strategy with immediate impact on energy consumption. Data Centers. - PowerPoint PPT PresentationTRANSCRIPT
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Green Computing
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Green Computing
• Current system extremely wasteful– Need energy to power– Need energy to cool• 1000 racks, 25,000 sq ft, 10MW for computing, 5 mw to
dissipate heat
• Need a system more efficient, less expensive strategy with immediate impact on energy consumption
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Data Centers• Focus by green computing movement on data
centers (SUVs of the tech world)• 6,000 data centers in US– Consume 61B kWh of energy in 2006– Cost: $4.5 B (more than used by all color TVs in US)– In 2007, DOE reports data centers 1.5% of all
electricity in US– Greenhouse gas emission projected to more than
double from 2007 to 2020
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Data Centers
• By 2012 cost of power for data center expected to exceed cost of original capital investment
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Goal
• Fed. Gov. wanted data center energy consumption to be reduced by at least 10% by 2011– Same as energy consumed by 1M average US
households
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Future Vision
• Sources of computing power in remote server warehouses
• Located near renewable energy sources – wind, solar
• Usage shifts across globe depending on where energy most abundant
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Current approaches
• Some “low hanging fruit” approaches– Orient racks of servers to exhaust in a uniform
direction
• Higher fruit - Microsoft– Built near hydroelectric power in WA – Built in Ireland - can air cool, 50% more energy
efficient– Countries with favorable climates: Canada,
Finland, Sweden and Switzerland
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Current approaches
• Google – trying to reduce carbon footprintCarbon footprint includes direct fuel use, purchased electricity and business travel, employee commuting, construction, server manufacturing
– According to Google, its data centers use ½ industry’s average amount of power
– How? Ultra efficient evaporative cooling (customized)
• Yahoo (what is Yahoo??)– Data centers also carbon-neutral because of use of
carbon offsets
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Current approaches• US government– EPA has phase-one of Energy Star standards for
servers– Measure server power supply efficiency and
energy consumption while idle– Must also measure energy use at peak demand
• Green Grid consortium– Dell, IBM, Sun VM-Wear AMD
• Green500 – 500 most green supercomputers
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Current approaches• Replace old computers with new more energy-
efficient• But manufacturing through day-to-day uses energy
• Dell - reducing hazardous substances in computers, OptiPlex 50% more energy efficient
• HP – “Greenest computer ever” rp5700 desktop PC– Died??
• Is MacBook air greenest?
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Goals for Future
1. Consider energy to manufacture, operate, dispose of
2. Sense (?) and optimize world around us3. Predict and respond to future events by
modeling behavior (grown in performance)4. Benefit of digital alternative to physical
activities– E-newspapers, online shopping
• Personal energy meter??
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Green Introspection by K. Cameron
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History of Green
• In the 1970s– Energy crisis– High gas prices– Fuel shortages– Pollution
• Education and action– Environmental activism– Energy awareness and conservation– Technological innovation
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Gifts from the 70s• Energy crisis subsided• In the meantime advances in computing
responsible for:– Innovation for energy-efficient buildings and cars– Identified causes and effects of global climate
change– Grassroots activism, distributing info about energy
consumption, carbon emission, etc.
• The same computing technologies pioneered by hippie geeks (???) are the problem now
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What happened next• Call to action within IT community (what about the
80s??)• In 1990s– General-purpose microprocessors built for performance– Competing processors
• ever-increasing clock rates and transistor densities • fast processing power and exponentially increasing power
consumption
– Power wall at 130 watts– Power is a design constraint
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Better, but also worse?
• To reduce power consumption– Multicore architectures – higher performance,
lower power budgets
• But– Users expect performance doubling every 2 years– Developers must harness parallelism of multicore
architectures– Power problems ubiquitous – energy-aware design
needed at all levels
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More problems
– Memory architectures consume significant amounts of power
– Need energy-aware design at systems level• Disks, boards, fans switches, peripherals
– Maintain quality of computing devices, decrease environmental footprint
– Can’t rely on nonrenewable resources or toxic ingredients
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Those data centers
• IT helping in data centers – Reducing energy with virtualization and
consolidation– Need to address chip level device to
heating/cooling of building
• Need metrics
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Yet another group
• Metrics– SPECPowerjbb benchmark and DCiE from Green
Grid
• Green Grid – group of IT professionals– Power Usage Effectiveness PUE
PUE = Total facility power/IT equipment power
– Data Center infrastructure Efficiency metric DCiE1/PUE
• Benchmark acceptance takes time
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Big government
• US EPA Energy Star specification for servers• Will have impact– US gov. procurements required to purchase energy
star machines (already true of monitors0
• May be further gov. regulations (with Dems in power ??)
• EU implemented carbon cap and trade scheme, US to follow
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Trade-off
• How often to replace aging systems?– 2% of solid waste comes from consumer
electronic components – E-waste fastest growing component of waste
stream– In US 130,000 computers thrown away daily and
100 M cell phones annually
• Recycle e-waste (good luck)• Use computers as long as possible?
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The Case for Energy-Proportional Computing
Barroso and Holzle (Google)
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Intro
• Energy proportional computing primary design goal for servers
• Cooling and provisioning proportional to average energy servers consume
• Energy efficiency benefits all components• Computer energy consumption lowered if:– Adopt high-efficiency power supplied– Use power saving features already in equipment
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Intro
• More efficient CPUs on chips based on multiprocessing has helped
• But, higher performance means increased energy usage
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Laptops vs. Servers
• Mobile device techniques– Multiple voltage planes, energy efficient circuit
techniques, clock gating, dynamic voltage frequency scaling
– Mobile high performance, short time followed by long idle interval
– High energy efficiency at peak performance, low energy inactive states
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Servers
• Servers– Rarely completely idle– Seldom operate at maximum– 10-50% of max utilization levels– 100% utilization not acceptable for meeting
throughput, etc. – no slack time
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Servers
– Completely idle server waste of capital• Difficult to idle subset of servers
– Servers need to be available• Perform background tasks• Move data around• Can help recovery of crash
– Applications can be restructured to create idle intervals• Difficult, hard to maintain
– Devices with highest energy savings, highest wake-up penalty, e.g disk spin up
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Energy Efficiency at varying utilization levels
• Utilization – measure of performance normalized to performance at peak loads
• Energy efficient server still consumes ½ power when doing almost no work
• Power efficiency – utilization/power value• Peak energy efficiency occurs at peak utilization
and drops as util. decreases• At 20-30% utilization, efficiency drops to less
than ½ at peak performance
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Toward energy-proportional machines
• Mismatch between servers’ high-energy efficiency characteristics and behavior
• Designers need to address this• Design machines that consume energy in
proportion to amount of work performed– No power when idle (easy)– Nearly no power when little work (harder)– More as activity increases (even harder)
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CPU power• Fraction of total server power consumed by
CPU changed since 2005• CPU no longer dominates power at peak
usage, trend will continue– Even less when idle
• Processors close to energy-proportional– Consume < 1/3 power at low activity (70% of
peak)– Power range less for other components• < 50% for DRAM, 25% for disk drives, 15% for network
switches
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Dynamic range• Processors can run at lower voltage frequency mode
without impacting performance• No other components with such modes– Only inactive modes in DRAM and disks
• Inactive to active mode transition penalty (even it only idle to submilliseconds)
• Servers with 90% dynamic range could cut energy by ½ in data centers
• Lower peak power by 30%• Energy proportional hardware reduce need for
power management software
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Inactive/active• Penalty for transition to active from inactive
state makes it less useful– Disk penalty 1000 higher for spin up than regular
access latency– Only useful if idle for several minutes (rarely
occurs)– More beneficial to have smaller penalty even if
higher energy levels– Active energy savings schemes are useful even if
higher penalty to transition because in low energy mode for longer periods
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Conclusions
• CPUS already exhibit energy proportional profiles, other components less so
• Need significant improvements in memory and disk subsystems– Such systems responsible for larger fraction of
energy usage
• Need energy efficient benchmark developers to report measurements at nonpeak levels for complete picture