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Green Computing

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.

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Green Computing

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  1. Green Computing

  2. 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

  3. 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

  4. Data Centers • By 2012 cost of power for data center expected to exceed cost of original capital investment

  5. 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

  6. 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

  7. 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

  8. Current approaches • Google – trying to reduce carbon footprint Carbon 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

  9. 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

  10. 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?

  11. Goals for Future • Consider energy to manufacture, operate, dispose of • Sense (?) and optimize world around us • Predict and respond to future events by modeling behavior (grown in performance) • Benefit of digital alternative to physical activities • E-newspapers, online shopping • Personal energy meter??

  12. Green Introspection by K. Cameron

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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 DCiE 1/PUE • Benchmark acceptance takes time

  20. 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

  21. 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?

  22. The Case for Energy-Proportional Computing Barroso and Holzle (Google)

  23. 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

  24. Intro • More efficient CPUs on chips based on multiprocessing has helped • But, higher performance means increased energy usage

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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)

  30. 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

  31. 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

  32. 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

  33. 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

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