© Technical University of Catalonia - Second University of Naples - Barcelona Supercomputing Center...

© Technical University of Catalonia - Second University of Naples - Barcelona Supercomputing Center (BSC) 1

Chapter x: Green Datacenter Infrastructures

in the Cloud Computing Era

1Sergio Ricciardi, 2Francesco Palmieri, 3Jordi Torres-Viñals, 2Beniamino Di Martino, 1Germán Santos-Boada, 1Josep Solé-

Pareta

1Technical University of Catalonia, Spain2Second University of Naples

3Barcelona Supercomputing Center (BSC),

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS


Introduction ICT energy consumption 7% worldwide produced electrical energy (ICT industry has the

same energy demand of the aviation industry) [2]

Demand Source: 20% from manufacturing, 80% equipment use [3]

ICT energy consumption growth rate will double in 2020


Introduction Human’s activities have severe impacts on the environment

Resource exploitation: Energy-consumption Pollution: GHG emissions, global warming & climate changes

Human ecological footprint measures the humanity’s demand on the biosphere 1,4 planet Earths [2006]

Carbon footprint Measures the total set of GHG emissions

Three dimensions Energy consumption (Wh) GHG emissions (kg CO2)

Cost (€)

Source: [1]


Data Centers energy consumption

Standard computer servers can consume between 1,200 to 8,600 kWh annually.

Annual source energy use of a 2MW data center is equal to the amount of energy consumed by 4,600 typical cars in one year.

=

• 4,600 typical cars• 1 million vehicles

• A single 2MW data center• All the US data center


BSC MareNostrum

Power consumption: 1.2 MW

~ 1,200 houses

1.100.000 €/year

~ 10000 Servers

“It is not the most powerful supercomputer in the world, but it is the most beautiful”(Fortune, Sept. 2006)


Data center energy use doubled to more than 120 billion kWh from 2006 to 2011, equal to annual electricity costs of $7.4

billion.

Data Centers energy costs


Where energy goes?

Cooling 34%

Server/Storage 47%

Conversion 7%

Network 10%

Lighting 2%

Energy distribution within the DC

The ICT Vicious cycle Watt Heat Cooling

Power Usage Effectiveness (PUE): ~ 2 [38]


Improving energy

efficiency in distributed

Data Centers can:

What we can gain?

“If we do nothing to change our data center consumption, 10 more power plants need to be built (over the next four years) to the tune of $2 billion to $6 billion each and their

cost is ultimately going to get passed on to IT through increased utility bills.”

-Ken Brill, Forbes Magazine

• Reduce business risk

• Become more socially

responsible

• Lower utility bills


What can we do? Global warming: great challenge

sensibilize People sensibilize Governments sensibilize Industries sensibilize Energy Providers sensibilize Academia sensibilize Internet Service Providers

Avoid wastes not increasing the offer but decreasing the demand

Develop energy-efficient architectures, energy-aware algorithms & protocols, use renewable energy


Replace physical servers with virtual servers that allow consolidation and resource sharing

Use thin clients, mobile phones or other low energy devices

Transfer network presence to a proxy and use wake on LAN

Not bringing the electrical power to data centers (power losses) but moving the data centers to the source of the green power and connect them with long reach fiber optic cables (ICT industry is the only business sector that has this inherent capability)

attenuation(light) < impedance(electric)

Virtualization & decentralization


Consolidation

Example200

Server Virtualization

$49,000/yr

DollarSavings

Energy Savings

980,000 kWh/yr

Physicalservers

Virtual server

25

Storage

Source: BC Hydro


Follow-the-sun/wind/… in complex clouds

12


Much of the time our systems are idle but on

What we seek is the ability to do nothing well…

13

Sleep mode: “doing nothing well”

Source: [40]


14

Sleep mode: “doing nothing well”

Sources: [41][42]

Consumption is driven by on-times, not by usageConsumption is driven by on-times, not by usage

PC savings potential is most of current consumption


15

Sleep mode Generic devices

Load balancing Time consuming Start-up & configuration problem + peak in power usage Lifetime (MTBF) Economic CAPEX & OPEX Per-interface sleep mode / Adaptive rate / Low Power Idle[39] / STOP-START

Energy proportional computing / Downclocking

Grid sites / data centers / Clouds Modular structure with hierarchical devices

CE

WN1 WNn...

DPM

SE1 SEm...


The Facilities

Power on procedure executed on SE1


The Facilities

Power off procedure executed on SE1


Performance Analysis Results

Time t1:

core 1 core 2 core 3 core 4server 1

server 2

server 3

server 4 x

server 5 x

server 6 x

Time t2: 9 incoming jobs:core 1 core 2 core 3 core 4

server 1

server 2

server 3

server 4 x

server 5 x

server 6 x

=> first-fit uses up to 2x more servers than best-fit

• In multicore servers job aggregation is possible: • Best-fit vs First-fit, Workload scheduler: 1 job 1 core


server 1 x x x x <= +4 first-fit


server 3 x <= +1 first-fit

server 4 x

server 5 x

server 6 x




server 3 x <= +1 first-fit

server 4 x x x x <= +3 best-fit




19

Energy-aware data center control plane

Capacity-demand mismatch leads to resource and energy wastes [8]

Traffic fluctuation

s

Overprovisioning

IDEA: exploit traffic fluctuations to aggregate jobs on a subset of servers and turn-off the idle ones


20

Theoretical provisioning elasticity concept

Safety Margin d

Ideal case

IDEA: exploit traffic fluctuations to aggregate jobs on a subset of servers and turn-off the idle ones



21

Service-demand matching algorithm

2

1

)()(t

t

dttltp

Theoretical energy savings upper-bound: Actual energy saving:

iisipn

i

1

)()(

Real case



22


Server energy model: the power consumption varies linearly with the

CPU load.

Probability Density Functions (PDFs) and Cumulative Distribution

Functions (CDFs) of jobs duration


23


Energy consumption of day one with and without the service-demand matchingalgorithm.


24


Energy consumption of day n with and without the service-demand matchingAlgorithm and queued jobs that have to wait due to a peak in the traffic load.


Performance Analysis Results

Energy, CO2 emissions and Costs with varying d values (large data center)


Conclusions Farms usually over-provisioned

+ Fluctuations in the traffic load

Job aggregation and sleep mode to save Energy, CO2 and €

service-demand matching algorithm job aggregation capabilities respects both the demand requirements and the logical and physical dependencies

Resource allocation efficiency : 20% ~ 68%

Significant energy, cost and emissions savings


References1. BONE project, 2009, “WP 21 Topical Project Green Optical Networks: Report on year 1 and updated plan

for activities”, NoE, FP7-ICT-2007-1 216863 BONE project, Dec. 2009.

2. An inefficient Truth by the Global Action Plan, http://www.globalactionplan.org.uk/upload/resource/Full-report.pdf.

3. SMART 2020: Enabling the low carbon economy in the information age, The climate group, 2008.

4. The Green Grid, “The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE,” Technical Committee White Paper, 2008.

5. Jordi Torres, “Green Computing: the next wave in computing”, Ed. UPCommons, Technical University of Catalonia (UPC). February 2010. Ref. http://hdl.handle.net/2099.3/33669.

6. Sergio Ricciardi, Alessandra Doria, Gianpaolo Carlino, Salvatore Iengo, Leonardo Merola, Maria Carla Staffa, “Powerfarm: a power and emergency management thread-based software tool for the ATLAS Napoli Tier2”, proceedings of Computing in High Energy Phisics (CHEP) 21 - 27 March 2009, Prague, Czech Republic, Journal of Physics: Conference Series (JPCS), IOP Publishing

7. Sergio Ricciardi, Davide Careglio, Ugo Fiore, Francesco Palmieri, Germán Santos-Boada, Josep Solé-Pareta, "Saving Energy in Data Center Infrastructures", submitted to e-Energy 2011, New York, U.S., 21/1/2011.

8. B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, N. McKeown, “Elastictree: Saving energy in data center networks”, in Proceedings of the 7th USENIX Symposium on Networked System Design and Implementation (NSDI), pages 249--264. ACM, 2010.

9. L.A. Barroso, L. A., Hölzle, U., “The Case for Energy-Proportional Computing”, IEEE Computer, vol. 40, 33-37, 2007.


Thanks for your attention!

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