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Chapter x: Green Datacenter Infrastructures in the Cloud Computing Era

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS. Chapter x: Green Datacenter Infrastructures in the Cloud Computing Era. 1 Sergio Ricciardi, 2 Francesco Palmieri, 3 Jordi Torres- Viñals , 2 Beniamino Di Martino, 1 Germán Santos-Boada, 1 Josep Solé-Pareta

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Chapter x: Green Datacenter Infrastructures in the Cloud Computing Era

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  1. HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS 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, Spain 2Second University of Naples 3Barcelona Supercomputing Center (BSC),

  2. 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

  3. 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]

  4. 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

  5. BSC MareNostrum “It is not the most powerful supercomputer in the world, but it is the most beautiful” (Fortune, Sept. 2006) Power consumption: 1.2 MW ~ 1,200 houses 1.100.000 €/year ~ 10000 Servers

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

  7. Energy distribution within the DC Server/Storage 47% Cooling 34% Conversion 7% Network 10% Lighting 2% Where energy goes? • The ICT Vicious cycle Watt  Heat  Cooling • PowerUsageEffectiveness (PUE): ~ 2 [38]

  8. What we can gain? Improving energy efficiency in distributed Data Centers can: • Reduce business risk • Lower utility bills • • Become more socially • responsible • “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

  9. 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

  10. Virtualization & decentralization • 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)

  11. Consolidation Example 200 25 Server Virtualization Virtual server Physical servers Storage Dollar Savings Energy Savings $49,000/yr 980,000 kWh/yr Source: BC Hydro

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

  13. Sleep mode: “doing nothing well” Much of the time our systems are idle but on • What we seek is the ability to do nothing well… Source: [40]

  14. Sleep mode: “doing nothing well” Consumption is driven by on-times, not by usage PC savings potential is most of current consumption Sources: [41][42]

  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 DPM WN1 SE1 ... ... WNn SEm

  16. The Facilities • Power on procedure executed on SE1

  17. The Facilities • Power off procedure executed on SE1

  18. Performance AnalysisResults • In multicore servers job aggregation is possible: • Best-fit vsFirst-fit, Workload scheduler: 1 job  1 core

  19. Energy-aware data centercontrol plane Capacity-demand mismatch leads to resource and energy wastes [8] Overprovisioning Traffic fluctuations IDEA: exploit traffic fluctuations to aggregate jobs on a subset of servers and turn-off the idle ones

  20. Energy-aware data center control plane Theoretical provisioning elasticity concept Ideal case Safety Margin d IDEA: exploit traffic fluctuations to aggregate jobs on a subset of servers and turn-off the idle ones

  21. Energy-aware data center control plane Service-demand matching algorithm Real case Theoretical energy savings upper-bound: Actual energy saving:

  22. Energy-aware data center control plane Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of jobs duration Server energy model: the power consumption varies linearly with the CPU load.

  23. Energy-aware data center control plane Energy consumption of day one with and without the service-demand matching algorithm.

  24. Energy-aware data center control plane Energy consumption of day n with and without the service-demand matching Algorithm and queued jobs that have to wait due to a peak in the traffic load.

  25. Performance AnalysisResults • Energy, CO2 emissions and Costs with varying d values (large data center)

  26. 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

  27. References • 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. • An inefficient Truth by the Global Action Plan, http://www.globalactionplan.org.uk/upload/resource/Full-report.pdf. • SMART 2020: Enabling the low carbon economy in the information age, The climate group, 2008. • The Green Grid, “The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE,” Technical Committee White Paper, 2008. • 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. • 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 • 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. • 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. • L.A. Barroso, L. A., Hölzle, U., “The Case for Energy-Proportional Computing”, IEEE Computer, vol. 40, 33-37, 2007.

  28. Thanks for your attention!

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