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Green Cloud: Reducing Energy Consumption in Cloud systems

Green Cloud: Reducing Energy Consumption in Cloud systems. Danilo Ardagna Dipartimento di Elettronica e Informazione, Politecnico di Milano ardagna@ elet.polimi.it Rome , 17th September 2010. Green Cloud Academic and Industrial Partners. Alta Scuola Politecnica Program:

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Green Cloud: Reducing Energy Consumption in Cloud systems

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  1. Green Cloud: Reducing Energy Consumption in Cloud systems Danilo Ardagna Dipartimento di Elettronica e Informazione, Politecnico di Milano ardagna@elet.polimi.it Rome, 17th September2010

  2. Green Cloud Academic and Industrial Partners Alta ScuolaPolitecnica Program: Politecnico di Milano: Software Engineering, Telecommunication, Operations Research groups Politecnico di Torino: Telecommunication, Operations Research groups Industrial partners: IBM Alcatel Lucent Lutech

  3. Data Center power consumption: an environmental problem... About 0.5% of global electricpowerconsumptionis due to DC In developedcountry: UK: 2.2-3.3% USA: 1.5% From the environmentalpointofview: 2% of global CO2emissions Source: EU Commission

  4. Data Center power consumption: …but first an economic one 12% 35% Number of Servers (M units) 28% IT costs 25% New Servers costs Energy and cooling costs Source: EU Commission Source: Microsoft Research

  5. DC Inefficiencies • Energy consumption reduction through server consolidation • Maintenance costs economies of scale • Efficient cooling systems solutions Courtesy of IBM

  6. Some Data Centers around the world • Microsoft (Quincy, WA) • 43.600 m2 (10 football fields) • 4,8 km chiller piping • 965 km electrical wire • Yahoo (Quincy, WA) • 13.000 m2 • Google (Dalles, OR) • 6.380 m2 servers area • 1.900 m2 administration offices • 1.500 m2apartments • 1.700 m2 cooling towers

  7. What about the network? • Inefficiencies: networks, too, are designed according to the peak traffic value

  8. Additional issues Workloads change during the day Energy costs spatial and time variability

  9. Green Cloud reference framework

  10. Green Cloud goals Design novel resource allocation policies for energy-aware Clouds Dynamic allocation of computing resources Joint management of network and data centers energy consumption Define business models for green clouds: scenarios, drivers, role of regulators

  11. Green Cloud Data Center autonomic resource management Application1 Application2 Free Server Pool Application3 Internet

  12. Network layer management: Power aware routing

  13. Network layer management: Power aware routing

  14. Energy management modeled as Optimization Problems • DC Autonomic Resource Management: • Mixed Integer Non Linear Problem, solution based on Local Search • Network Power Aware Routing Problem: • Mixed Integer Linear Problem, solution based on commercial solvers

  15. DC autonomic resource management – Preliminary results Scenario 2:customersfromtwodifferenttimezones Scenario 1:customersfrom the sametime zone • Averagesavingsscenario 1: 15% • Averagesavingsscenario 2: 25% Comparisonwith a DC running at full capacity

  16. DC autonomic resource management – Preliminary results IBM Tivoli Comparison Oursolution Oursolution IBM Tivoli IBM Tivoli

  17. Network power aware routing – Preliminary results Power savings (W/h) • Powerawarefixedrouting vs. Nominalconsumption • Powerawarevariablerouting vs. Nominalconsumption Time Power (W/h) Time

  18. Current work • Integrationof DC and network layerpolicies • Smart grid and green energysourcesmodelling • Validation in industrial testbeds

  19. Acknowledgements • Ahmed Allam, RiccardoChiodaroli, Francesco Lunetta, Stefano Viganò, Stefano Ziller • (ASP students GreenCloud team) • Antonio Capone, Bernardetta Addis, GiulianaCarello, Marco Lovera, Mara Tanelli, Alessandro Barenghi (Politecnico di Milano) • Federico Della Croce, Marco Mellia, MichelaMeo (Politecnico di Torino) • Massimo Leoni (IBM Italy) • Carlo Spinelli, Giorgio Parladori (Alcatel Lucent) • Fabrizio Leone (Lutech) • Li Zhang (IBM Research) • BrunildeSansò (ÉcolePolytechnique de Montréal) • Paolo Costa (KushagraVaid, Microsoft Research) • Klaus Lange (Hplabs)

  20. Thanks!Anyquestions?

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