Il consumo energetico è la questione del futuro. Dipendiamo sempre più da fonti di energia che scarseggiano. D'altro canto il consumo di energia ha influenze drammatiche sui cambiamenti climatici. E' necessario affrontare la questione della riduzione dei consumi, soprattuto nel settore delle comunicazioni. Qui presentati e analizzati i consumi della telefonia mobile e del network.

1. Energy Efficient Wireless Internet Access Marco Ajmone Marsan, Michela Meo Politecnico di Torino

2. WIA & MtCO 2 e Marco Ajmone Marsan, Michela Meo Politecnico di Torino

3. What’s all this “green networking” about?

5. Climate change Source: Hansen, J., et al. (2006) "Global temperature change". Proc. Natl. Acad. Sci. 103: 14288-14293.

6. Climate change Source: A.P. Sokolov et al, “Probabilistic Forecast for 21st Century Climate Based on Uncertainties in Emissions (without Policy) and Climate Parameters” , Report 169, Jan 2009 2003 Model 2009 Model

8. Source: Energy Information Administration (EIA), International Energy – Annual Energy Outlook 2009 TW

12. Which ICT? Source: M. Pickavet et al, “Worldwide Energy Needs for ICT: the Rise of Power-Aware Networking,” in IEEE ANTS Conference, Bombay, India, Dec. 2008.

13. Consumption might double in the next decade Source: M. Pickavet et al, “Worldwide Energy Needs for ICT: the Rise of Power-Aware Networking,” in IEEE ANTS Conference, Bombay, India, Dec. 2008.

16. PCs Source: Peter James and Lisa Hopkinson, “ Energy and Environmental Impacts of Personal Computing -- A Best Practice Review prepared for the Joint Information Services Committee (JISC)” , May 2009.

17. Williams, E., 2004. Energy Intensity of Computer Manufacturing: Hybrid Assessment Combining Process and Economic Input-Output Methods. Environ. Sci. Technol., 2004, 38 , 6166-6174. Lawrence Berkeley National Laboratory, 2005. Optimization of Product Life Cycles to reduce Greenhouse Gases in California. Report for California Energy Commission. CEC-500-2005-110-F. IVF Industrial Research and Development Corporation, 2007. Lot 3: Personal Computers (desktops and laptops) and Computer Monitors. Final Report for the European Commission, August 2007.

18. Operation Equipment Consumption Desktop PC 100-150W Laptop PC 20W Server 700 W – 10KW Router 5-10 W per Gbps GSM BS 700W UMTS BS 800W WIMAX BS 400W

19. Data centers Source: “Report to Congress on Server and Data Center Energy Efficiency” Public Law 109-431. U.S. Environmental Protection Agency ENERGY STAR Program , August 2007

22. Data centers Source: “Fact Sheet on National Data Center Energy Efficiency Information Program“, U.S. Department of Energy (DOE) and U.S. Environmental Protection Agency (EPA), March 19, 2008

23. Networks Internet Core Backbone Metro Feeder

25. Typical network Source: J. Baliga, K. Hinton and R. Tucker, “Energy consumption of the Internet”, in COIN - ACOFT 2007, June 2007, Melbourme, Australia factor 4

26. Routers Source: R. Tucker et al., “Energy consumption in IP networks”, in European Conference on Optical Communication ECOC’2008, Brussels, Sept. 2008.

28. Fixed operators 70% of power consumption 30% of power consumption

29. Mobile operators 10% of power consumption 90% of power consumption Order of the OPEX!

30. Which business model?

31. Fast Slow Intermediate

33. Base station consumption

34. An immediate solution for mobile operators Start by reducing consumption at the access network with current technologies

36. Traffic profiles

38. A NodeB controls 2 microcells

39. Switch off half of the NodeB, x=1/2

40. Switch off half of the NodeB, x=1/2

43. 8:00 16:00 24:00 8:00 16:00 24:00 8:00 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 time lambda day/night traffic pattern for one cell Total traffic in x+1 cells night zone Low traffic threshold: QoS is guaranteed

44. 8:00 16:00 24:00 8:00 16:00 24:00 8:00 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 time lambda night zone traffic pattern for cells remaining on

46. 8:00 16:00 24:00 8:00 16:00 24:00 8:00 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Time lambda VOICE VIDEOCALL DATA Switch off 1 Node-B for about 9 hours Energy saving= 37.5%

47. Hierarchical scenario 7 μ Cells with: R_ μ cells=100m P TX _ μ cells=2 W Umbrella (Macro) Cell: R_Mcell≈265m P TX _Mcell=3.4 W

48. 8:00 16:00 24:00 8:00 16:00 24:00 8:00 0 0.01 0.02 0.03 0.04 0.05 0.06 Time lambda VOICE VIDEOCALL DATA λ night ->0: Good for office scenario

50. Possible configurations Manhattan configurations (linear) (1,2) (2,3)

51. Possible configurations Hexagonal configurations (squared) (3,4) (8,9)

52. Switching off more does not always mean saving more! Switch off scheme geometry (1,2) linear (2,3) linear (3,4) squared (8,9) squared Load ratio 2 3 4 9 Cell radius 2x 3x 2x 3x PB[W] 5 18 5 18 Night zone 16h30m 14h40m 12h20m 7h NodeB saving [%] 68.7 61.1 50.4 29.1 Network saving 34.3 40.7 37.8 25.9

56. f M T/2 T=24h t f M /(1+  ) f M  /(1+  ) Switch off time for B Switch off A  B  A

62. Real traffic pattern

63. Constant cost model: Total saving Saving can be huge! 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 Energy saving [cost/day] Energy saving [%] Traffic ratio,  Roaming Saving Switching Max

64. Constant cost model: Roaming balance 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 Energy saving [cost/day] Energy saving [%] Traffic ratio,  Total A B

65. Constant cost model: Switching balance 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 Energy saving [cost/day] Energy saving [%] Traffic ratio,  Total A B

66. Variable cost model: Total saving Different cost models lead to different policies 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Energy saving [cost/day] Traffic ratio,  Roaming Saving Switching Max

67. Variable cost model: Total saving Different cost models lead to different policies 5 10 15 20 25 30 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Energy saving [%] Traffic ratio,  Roaming Saving Switching Max

69. 2 Operators: Different QoS 0.4 0.5 0.6 0.7 0.8 0.9 1  =0.25  =0.50  =0.75  =1.00 0 5 10 15 20 25 0.1 0.2 0.3 Saving [%] QoS traffic reduction factor, 

70. 2 Operators: Different QoS  =0.25  =0.50  =0.75  =1.00 6 7 8 0.2 0.4 0.6 0.8 QoS traffic reduction factor,  Off-On  =0.25  =0.50  =0.75  =1.00 1 20 21 22 23 24 0.2 0.4 0.6 0.8 1 Switching time QoS traffic reduction factor,  On-Off

73. 4 Operators: Increasing Pattern Under same cost, increasing pattern is optimal 20 25 30 35 40 0 0.2 0.4 0.6 0.8 1 Saving [%] Network unbalance, a same cost - All var cost - All same cost - One var cost - One Roaming to all is more effective

74. 4 Operators: Decreasing Pattern 20 25 30 35 40 0 0.2 0.4 0.6 0.8 1 Saving [%] Network unbalance, a same cost - All var cost - All same cost - One var cost - One

75. 4 Operators: Increasing Pattern 7 8 9 10 11 12 13 14 15 0 0.2 0.4 0.6 0.8 1 Off time Network unbalance, a oper. 1 - All oper. 2 - All oper. 3 - All oper. 1 - One oper. 2 - One oper. 3 - One

80. Thank you!

81. Questions?