quantifying the environmental advantages of large-scale computing vlasia anagnostopoulou...

Quantifying Quantifying the Environmental Advantages the Environmental Advantages

of Large-Scale Computingof Large-Scale Computing

Vlasia Anagnostopoulou (vlasia@cs.ucsb.edu), Heba Saadeldeen, and Frederic T. Chong

Department of Computer Science,University of California Santa Barbara

E-Business datacenter dilemma

• In addition, manufacturing and use phase of datacenters burden environment

Datacenter environmental implications• Manufacturing:

– A desktop computer requires 260 kg of fossil fuel + 6400 MJ of energy (Williams)

– An average cooling unit: 527 kg of primary materials

– An average power unit: 8 ton

• Use phase (24/7) : – As of 2006, U.S. datacenters consumed 10% of

total U.S. energy consumption, projected to double in 5 years

– For lower growth projection: aggressive efficiencyTherefore, environmental cost is significant!

• Besides deployment costs, what are the environmental and operational costs with datacenter scale?

• Trade-offs among them?

Rest of presentation

• Overview of datacenter operation and characterization

• Datacenter and business models• Model cost analysis

– Environmental– Capital (CAPEX)– Operational (OPEX)

• Lessons• Conclusion• Future work

Overview of datacenter operation + characterization

Datacenter power distribution

Tier classification • Tier-I: no redundancy • Tier-II: redundancy N+1• Tier-III, Tier-IV, …

Cooling Operation

Datacenter-in-a-container

• Standard-sized container• Very efficient air-flow

– Better PUE (Power Usage Efficiency = Total Power/ IT-Power)

• External cooling and power loops are same

Datacenter and business models

Datacenter and Business Model• Datacenter case:• Various datacenter sizes

– Comp. room (1-2 racks), Small (20) , Medium (50), Large (100)– Based on vendor’s classification

• Building /container installation• Cooling and power provisioned w/o redundancy (tier-I) and w.

N+1 redundancy (tier-II) • In case of comp. room, assume existing chilled-water loop

• Business case:• Two representative types of business apps

– E-commerce– Financial

• Simulated by TPC council’s TPC-C and TPC-H benchmarks (in respect)

Putting it all together

Size # of businesses

# of racks # of containers

Local Comp.Room

(TPC-H)

1 ¾ N/A

Comp.Room

(TPC-C)

1 1.5 N/A

Remote S 18 20 1

M 47 50 2

L 95 100 5

Model provisioning

• Strategy: capacity matching• Not as precise as detailed model, but it is

uniform and it does happen in practice(!)• Server provisioning

– the state-of-the-art system from TPC council

• Cooling provisioning – from vendor’s specs, to match server heat load

• Power provisioning– to match server heat load + cooling load

Model cost analysis

Environmental cost -> Methodology

• For each size configuration:• From vendor’s specs, add weights of power &

cooling components • Calculate amount of materials • Use material breakdown tables to come up

with amount of metals, plastic, and glass/ceramic

• Normalize over large configuration for comparison

Environmental cost -> Results

Environmental cost -> Explanation

• Material scaling dis-proportionality• (Same trend for UPS)

CAPEX -> Methodology• Here: Cooling and Power CAPEX (part of TCO)• Assumptions:

– Loan with interest rate: 8%– Cooling & Power provisioning: 2x– Application-requirements double every 2 years – Small facility upgrade period is 4 years– Large facility upgrade is 6 months, except for

Chiller– Life-cycle of 10 years

CAPEX -> Methodology

CAPEX-> Results

• Total capital costs:

Size CAPEX [Million $]

Comp. Room 6.1

S 6.7

M 5.4

L 5.1

CAPEX->Results

OPEX -> Methodology

• Calculated PUE based on:– Active-power*work-hours + Idle-power*idle-hours– Power based on inefficiency related to size

• For container, used PUE from specs (same for all sizes)

Size PUE

Comp. Room 2.00

S 1.76

M 1.71

L 1.69

Container (All sizes) 1.25

OPEX->Results

• Total energy consumption:

Size Energy in MWh

Norm. Comp. R. 687,400

Norm. S 243,500

Norm. M 238,000

Norm. L 223,900

Lessons, Conclusions and Future Work

Lesson #1: Material efficiency• Large (tier-I) installations are up to 53% more

efficient

• Tier-II (w. N+1 redundancy) up to 75%

• Preferring a large installation can save up to:– 95 tons of materials, from which:

i. Primary metals: 62 tons

ii. Plastics: 27 tons

iii. Glass/Ceramic: 7 tons

• Because of disproportional use of materials in power and cooling manufacturing + effect of redundancy

Lesson #2: Operational efficiency • Large (building) installations can have up to

16% better PUE

• Their operational energy consumption can be up to 67% less

• Containers can have up to 38% better PUE– (however, data from different sources)

• Because the larger the datacenter, the less inefficiencies in power and cooling

• Although we don’t evaluate here, large datacenter have better practices and more staff resources

Lesson #3: Cost advantage

• A large installation can be up to 24% cheaper– Because of faster outpayment of loans

• However, a small datacenter installation is 10% more expensive compared to an equivalent # of comp. rooms– Because we assume that in the case of a

comp. room deployment, the building’s chilled-water loop is used

Conclusion• Quantified material, price, and operation

efficiency with datacenter scale– Up to 75% material efficiency, 67%

operational efficiency, and 24% in capital cost– Up to 95 tons less materials

• Container datacenters are even more efficient in their operation

• Exception is the deployment of a computer room, if it is to be hooked to the building’s chilled-water loop

Future workWe plan to:

• Include more factors:– degradation of land– price of land– Operational savings because of VM migration

• Add staff and software expenses to OPEX

• Complete Life-Cycle Assessment: – Use LCA tools over manufacturing and use

phases (e.g. GHG emissions, water pollution)– Evaluate retirement options

The End

Thanks for listening!

Questions?

Contact: vlasia@cs.ucsb.eduURL: http://www.cs.ucsb.edu/~vlasia

ArchLab: http://www.cs.ucsb.edu/~archlabComputer Science Dept.: http://www.cs.ucsb.edu