The document discusses the urgent need for energy-efficient wireless internet access and the implications of energy consumption in information and communication technologies (ICT). It highlights that ICT is responsible for a significant portion of global energy usage and greenhouse gas emissions, while also detailing various strategies to reduce energy consumption in networks, such as optimizing data centers and implementing dynamic network planning. The overarching emphasis is on the integration of 'green networking' practices to mitigate climate change and improve energy efficiency in technology-related sectors.

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?

4. Energy is becoming the issue of our future We depend on energy which is becoming scarce Energy consumption is causing dramatic climate changes We must cope with this and reduce energy consumption in all sectors, ICT and networking included The problem

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

7. The main global warming culprit is carbon dioxide, CO 2 Gases that react to form smog Fine particles such as black carbon 80% of the increase of CO 2 in the air in the last century is due to fossil fuel burning (20% deforestation) Who is the culprit?

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

9. Electricity = 30% of energy 1 W of electrical energy ≈ 2.1 W of primary energy Source: Energy Information Administration (EIA), International Energy – Annual Energy Outlook 2009

10. Information and Communication Technologies play a positive role for energy saving: moving bits instead of atoms teleworking and telecommuting e-commerce intelligent transport systems electronic billing sensors to monitor and manage environment What about ICT?

11. The ICT sector is a heavy consumer! … but “ ICT alone is responsible of a percentage which vary from 2% to 10% of the world power consumption.” “ Electricity demand of ICT is almost 11% of the overall final electricity consumption in Germany.” “ The ICT sector produces some 2 to 3% of total emissions of greenhouse gases.”

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.

14. Life Cycle Assessment (LCA) refers to the quantitative characterization of the environmental impacts of products and services and includes Manufacture Operation Disposal A life cycle perspective can lead to a better understanding of environmental management This is particularly true for IT products Life cycle matters

15. Example: 2-gram memory chip requires at least 1,200 grams of fossil fuels 72 grams of chemicals Fossil fuels for production are some 600 times the weight of the chip (the total fossil fuel to produce a car is 1-2 times its weight) Purification to semiconductor grade materials is energy intensive Due to its extremely low-entropy, organized structure, the materials intensity of a microchip is orders of magnitude higher than that of “traditional” goods. Electronics

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

20. In 2006, U.S. data centers used 61 TWh of electricity, corresponding to 1.5% of national consumption Double the amount consumed in 2000 Based on current trends, energy consumption will continue to grow 12% per year, due to increasing demand for the services they provide Data centers

21. Algorithms to free up servers and put them into sleep mode or to manage load on the servers in a more energy-efficient way Sensors identify which servers would be best to shut down, based on environmental conditions Use more efficient components Reduce cooling needs (cooling consumes as much as 40% of the operating costs) through specific physical layouts Current solutions

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

32. Cellular networks Base stations are responsible for about 80% of energy consumed by a cellular network A typical BS consumes from 500W to 3KW, with an average consumption per year of 35 MWh (as much as 10 families) In Italy 60,000 BSs, leading to 2.1 TWh/year, about 0.7 % of total Italian consumption of electricity 300 M€ electricity bill for the operators About 1,2 Mton of emitted CO 2 equivalent per year

33. Base station consumption

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

35. Dynamic network planning Networks are planned based on the peak hour traffic Due to natural traffic variability (i.e., typical day/night traffic profile) the network results over-dimensioned during long periods of time Switch off portions of the network when traffic is low

36. Traffic profiles

37. Assume that a fraction x of the base stations (cells) are switched off The BSs that remain on are in charge of the traffic of the cells that are off (the desired QoS must still be guaranteed) the radio coverage (transmission power might be increased to guarantee coverage) Switch-off scheme

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

41. Assume that, for each cell remaining on, x cells can be switched off. In the cells that remain on: New traffic is  ’=( x +1)  New cell radius is R’=KR ( K depends on geometry) Looking for a switch-off scheme

42. Find the low traffic threshold and compute the night zone (period in which the switch-off scheme can be applied), based on day/night traffic pattern QoS constraint (i.e., blocking probability < 1%) Looking for a switch-off scheme

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

45. Check the maximum cell radius, R MAX If R’< R MAX  DONE else increase transmission power during night zone OR reduce the night zone Looking for a switch-off scheme

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

49. -1 Time Lambda µ M M µ µ The Umbrella cell is always ON (day+night) - Switch off 2 Node-B (7µcells) for about 4 hours Energy saving= 17% 8:00 16:00 24:00 8:00 16:00 24:00 8:00 10 -20 10 -15 10 -10 10 -5 10 0 Time Blocking Probability VOICE VIDEOCALL DATA 8:00 16:00 24:00 8:00 16:00 24:00 8:00 10 -4 10 -3 10 -2 10

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

53. But, we have multiple operators Several competing mobile operators cover the same area with their equipment Networks are dimensioned over the peak hour traffic During low traffic periods the resources of one operator are sufficient to carry all the traffic Make operators cooperate to reduce energy consumption

54. In turn, Switch off the network of one operator, when traffic is low and the active operators can carry all the traffic Let users roam to other operators Balance costs Cooperation

55. Two operators: A and B No. of users N A and N B , with N B =  N A and  <1 Daily traffic profile f A (t) and f B (t), f B (t)=  f A (t) Example: 2 operators f M T/2 T=24h t f A (t) f B (t)  f M

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

57. Let p A and p B be the frequency with which A and B switch off Different strategies can be adopted for choosing the switching frequency Switch-off policies

58. Switch off the networks every other day, alternatively, p A = p B Balanced switch-off frequency

59. Make the two networks carry the same roaming traffic (on average) Balanced roaming cost traffic carried by B when A is off traffic carried by A when B is off

60. Make the two networks achieve the same energy saving Balanced energy saving switch off time for A energy cost for A

61. Two cost models Constant : the fixed costs dominate, the networks have the same energy cost regardless the number of subscribers C B =C A Variable : the network energy cost is proportional to the number of subscribers C B =  C A Balanced energy saving

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

68. Different QoS When operators guarantee different QoS levels, the network with the best QoS switches off only when the other operator can guarantee similar QoS This translates into a traffic reduction factor Same QoS Different QoS: QoS of A is tighter

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

71. Multiple Operators With more than 2 operators, the space of possible switch-off patterns explodes Different roaming schemes are possible, during the switch-off phase: Roaming-to-One : Roaming traffic goes to the operator which remains on all the time Roaming-to-All : Roaming traffic is distributed to active operators

72. Example: 4 Operators Let the number of users for operator i be proportional to  i , with a is the network unbalance a=0: the networks carry the same traffic a=1: network 1 has ¼ of the traffic of network 4

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

76. Energy issues are crucial, even for networking Design criteria must be changed Energy consumption/wastage is a variable to be taken into account in design and performance evaluation Future Internet design will have to cope with it Virtual operators appear to be an interesting option Lessons

77. A new attitude is needed Consumers: awareness of the cost of Turn over of devices Uncontrolled use of energy Manifacturers Life cycle assessment Operators Careful management of resources Architectures Lessons

78. Governments and institutions will have to play a role in Inducing new attitudes (e.g., education to an aware use of resources) Forcing new production models based on products life cycle (e.g., responsibility for disposal, incentives to long lasting devices) Providing incentives for cooperation Lessons

79. M.Ajmone Marsan, L.Chiaraviglio, D.Ciullo, M.Meo, “Energy-Aware UMTS Access Networks” , W-GREEN 2008 - First International Workshop on Green Wireless, Lapland, Finland, September 2008 M.Ajmone Marsan, L.Chiaraviglio. D. Ciullo, M.Meo, “Optimal Energy Savings in Cellular Access Networks” , GreenComm'09 - First International Workshop on Green Communications, Dresden, Germany, June 2009 M.Ajmone Marsan, L.Chiaraviglio, D.Ciullo, M.Meo, “Energy-Efficient Management of UMTS Access Networks” , 21st International Teletraffic Congress (ITC 21), Paris, France, September 2009 M. Ajmone Marsan, M. Meo, ”Energy Efficient Management of two Cellular Access Networks” , GreenMetrics 2009 Workshop, Seattle, WA, USA, June 2009 References

80. Thank you!

81. Questions?