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• 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

Data Centers

• By 2012 cost of power for data center expected to exceed cost of original capital investment

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

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

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

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

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

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?

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??

Green Introspection by K. Cameron

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

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

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

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

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

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

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

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

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?

The Case for Energy-Proportional Computing

Barroso and Holzle (Google)

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

Intro

• More efficient CPUs on chips based on multiprocessing has helped

• But, higher performance means increased energy usage

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

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

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

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

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)

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

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

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

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