Quantifying the Environmental Advantages of Large-Scale Computing Vlasia Anagnostopoulou...
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Transcript of 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 ([email protected]), 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: [email protected]: http://www.cs.ucsb.edu/~vlasia
ArchLab: http://www.cs.ucsb.edu/~archlabComputer Science Dept.: http://www.cs.ucsb.edu