Природа
<<  The Seasons 'Green' concept cars  >>
Green Computing
Green Computing
Green Computing
Green Computing
Data Centers
Data Centers
Data Centers
Data Centers
Goal
Goal
Green Computing
Green Computing
Green Computing
Green Computing
Green Computing
Green Computing
Data Center Metrics
Data Center Metrics
Future Vision
Future Vision
Current approaches
Current approaches
Current approaches
Current approaches
Current approaches
Current approaches
Data Center Product Specification Completion
Data Center Product Specification Completion
Current approaches
Current approaches
Goals for Future
Goals for Future
The Case for Energy-Proportional Computing
The Case for Energy-Proportional Computing
Intro
Intro
Intro
Intro
Laptops vs
Laptops vs
Servers
Servers
Green Computing
Green Computing
Servers
Servers
Energy Efficiency at varying utilization levels
Energy Efficiency at varying utilization levels
Green Computing
Green Computing
Toward energy-proportional machines
Toward energy-proportional machines
CPU power
CPU power
Dynamic range
Dynamic range
Green Computing
Green Computing
Disks - Inactive/active
Disks - Inactive/active
Conclusions
Conclusions
Green Introspection by K. Cameron
Green Introspection by K. Cameron
History of Green
History of Green
Gifts from the 70s
Gifts from the 70s
What happened next
What happened next
Better, but also worse
Better, but also worse
More problems
More problems
Those data centers
Those data centers
Yet another group
Yet another group
Big government
Big government
Trade-off
Trade-off

Презентация на тему: «Green Computing». Автор: Ming Lei, Susan V. Vrbsky and ZiJie Qi. Файл: «Green Computing.ppt». Размер zip-архива: 314 КБ.

Green Computing

содержание презентации «Green Computing.ppt»
СлайдТекст
1 Green Computing

Green Computing

2 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, 5MW to dissipate heat Need a system more efficient, less expensive strategy with immediate impact on energy consumption

3 Data Centers

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 reported data centers 1.5% of all electricity in US Greenhouse gas emission projected to more than double from 2007 to 2020

4 Data Centers

Data Centers

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

5 Goal

Goal

Fed. Gov. wanted data center energy consumption to be reduced by at least 10% Same as energy consumed by 1M average US households

6 Green Computing
7 Green Computing
8 Green Computing
9 Data Center Metrics

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

10 Future Vision

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

11 Current approaches

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

12 Current approaches

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 ? industry’s average amount of power How? Ultra efficient evaporative cooling (customized) Yahoo (are they back??) Data centers also carbon-neutral because of use of carbon offsets

13 Current approaches

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

14 Data Center Product Specification Completion

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

15 Current approaches

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 Greenest computer company – VirtualBoxImages What is “Greenest computer ever” ? Is MacBook air (pro) greenest?

16 Goals for Future

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??

17 The Case for Energy-Proportional Computing

The Case for Energy-Proportional Computing

Barroso and Holzle (Google)

18 Intro

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

19 Intro

Intro

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

20 Laptops vs

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

21 Servers

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

22 Green Computing
23 Servers

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

24 Energy Efficiency at varying utilization levels

Energy Efficiency at varying utilization levels

Utilization – measure of performance normalized to performance at peak loads Energy efficient server still consumes ? 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 ? at peak performance

25 Green Computing
26 Toward energy-proportional machines

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)

27 CPU power

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

28 Dynamic range

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 ? in data centers Lower peak power by 30% Energy proportional hardware reduce need for power management software

29 Green Computing
30 Disks - Inactive/active

Disks - 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

31 Conclusions

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

32 Green Introspection by K. Cameron

Green Introspection by K. Cameron

33 History of Green

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

34 Gifts from the 70s

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

35 What happened next

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

36 Better, but also worse

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

37 More problems

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

38 Those data centers

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

39 Yet another group

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

40 Big government

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

41 Trade-off

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?

«Green Computing»
http://900igr.net/prezentacija/anglijskij-jazyk/green-computing-141374.html
cсылка на страницу
Урок

Английский язык

29 тем
Слайды