GREEN COMPUTING

CS 595

LECTURE 14

4/10/2015

DATA CENTERS• Focus by green computing movement on data centers (SUVs of the

tech world)

• 6,000 data centers in US

• Cost: $4.5 B (more than used by all color TVs in US)

• In 2007, DOE reported data centers 1.5% of all electricity in US

• Greenhouse gas emission projected to more than double from 2007 to 2020

DATA CENTERS

• Within a few years, cost of power for data center is expected to exceed cost of original capital investment

DATA CENTER METRICS

• 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

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 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 (are they back??)

• Data centers also carbon-neutral because 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

DATA CENTER PRODUCT SPECIFICATION COMPLETION• 2009

• Servers v1.0

• 2011

• Data Center Buildings Program

• 2012

• UPS v1.0 (uninterruptable power supply)

• 2013

• Servers 2.0

• Storage v1.0

• 2014

• Large Network Equipment v1.0

• 2015

• Data Center Cooling Equipment v1.0

CURRENT APPROACHES• Replace old computers with new more energy-efficient computers

• But manufacturing uses energy

• Dell - reducing hazardous substances in computers, OptiPlex 50% more energy efficient

• Greenest computer company – VirtualBoxImages

• What is “Greenest computer ever” ?

• Is MacBook air (pro) greenest?

• https://www.apple.com/macbook-pro/environment/

GOALS FOR FUTURE

1. Consider energy to manufacture, operate, dispose of wastes

2. Sense and optimize world around us

3. 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??

THE CASE FOR ENERGY-PROPORTIONAL COMPUTINGBARROSO 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 supplies

• Use power saving features already in equipment

INTRO

• More efficient CPUs with architectures based on multiprocessing has helped

• But, higher performance means increased energy usage

• Really?

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 energy

• Difficult to idle subset of servers

• Servers need to be available

• Perform background tasks

• Move data around

• Help with crash recovery

• Applications can be restructured to create idle intervals

• Difficult, hard to maintain

• Devices with highest energy savings usually have the highest wake-up penalty

• 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 utilization 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 if only idle to active)

• 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• How to build energy proportional characteristics into different hardware components?

DISKS - INACTIVE/ACTIVE• Penalty for transition to active from inactive state

makes it less useful

• Disk penalty 1000x higher for spin up than regular access latency

• Only useful if idle for several minutes (rarely occurs)

• More beneficial to have smaller penalty even at 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

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

• Increase 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 all factors from CPU to heating/cooling of building

• Need metrics

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:

• 130K computers thrown away daily

• 274K cell phones thrown away daily

•Recycle e-waste (good luck)

•Use computers as long as possible?