1. Confidentiality/date line: 13pt Arial Regular, white Maximum length: 1 line Information separated by vertical strokes, with two spaces on either side Disclaimer information may also be appear in this area. Place flush left, aligned at bottom, 8-10pt Arial Regular, white Indications in green = Live content Indications in white = Edit in master Indications in blue = Locked elements Indications in black = Optional elements Copyright: 10pt Arial Regular, white EE5900 Advanced Embedded System For Smart Infrastructure Computationally Efficient Smart Home Scheduling

2. Smart Home 1 Cloud Computing 2 Case Study 4 Algorithm 3 Outline 2 Conclusion 5

3. Smart Home 3 Power Line Communication Line

4. Landry machine Dish washer PHEV AC Start End …… 13:0018:00 09:0018:00 08:0018:00 17:00N/A 4

5. Home Appliance (HA) in Smart Home 5 Non-schedulable HARestrictive-schedulable HA Full-schedulable HA

6. Multiple Power Levels 6 350 W Power level 500 W 820 W 1350 W http://www.supplyairconditioner.com/1-4-9-split-wall-mounted-air-conditioner.html

7. Multiple Working Stages 7 Working cycles Prewash Washing Rinsing Spinning  Assume all stages have same working frequency for simplicity  Partition the whole task to multiple subtasks with precedence constraints Drying

8. Plug-in Hybrid Electric Vehicles (PHEV) 8 Powered by an Electric Motor and Engine Internal combustion engine uses alternative or conventional fuel Battery charged by outside electric power source, engine, or regenerative breaking During urban driving, most power comes from stored electricity. Long trips require the engine

9. Contemporary Hybrids 9 Toyota PriusToyota CamryToyota Highlander Honda Insight Lexus RX400hLexus GS450hHonda CivicHonda Accord Saturn VueChevy Silverado Ford Escape

10. Charging of PHEV 10 Level 1: 120 V, alternating current (AC) plug; dedicated circuit Level 2: 240 V, AC plug and uses the same connector on the vehicle as Level 1 Level 3: In development; faster AC charging

11. Existing Products of Battery  Accord PHEV 120-volt: less than 3 hours 240-volt: one hour  Toyata PHEV 120-volt: less than 3 hours 240-volt: 1.5 hours Quick charge to 80% needs 30 minutes. 11

12. Dynamic Pricing from Utility Company 12 https://rrtp.comed.com/live-prices/?date=20130404

13. Dynamic Voltage and Frequency Scaling (DVFS) 13 10 cents/kwh 5 cents / kwh 5 kwh 10 kwh Power Powerr Time 12 123 (a) (b) 10 cents/kwh 5 cents / kwh cost = 10 kwh * 10 cents/kwh = 100 cents cost = 5 kwh * 10 cents/kwh + 5 kwh * 5 cents/kwh = 75 cents

14. Smart Home Scheduling (SHS) 14  Given n home appliances, to schedule them for monetary cost minimization satisfying the total energy constraint and deadline constraints  Demand Side Management –when to launch a home appliance –at what frequency –The variable frequency drive (DVFS) is to control the rotational speed of an alternating current (AC) electric motor through controlling the frequency of the electrical power supplied to the motor –for how long

15. Benefit of Smart Home 15 –Reduce monetary expense –Reduce peak load

16. Smart Home Scheduling (SHS) 16 Home appliance level User level Community level

17. Smart Home Scheduling (SHS)  Home appliance level  User level  Community level 17

18. Single Home appliance Scheduling 18 Non-schedulable HA Consider the non-schedulable home appliance as fix energy consumption

19. Single Home appliance Scheduling 19 For restrictive-schedulable home appliance, set start time to be earlier than the user’s requirement. For example, in summer, user wants to come back to home at 5pm. The AC should be on before 5pm. Restrictive-schedulable HA

20. Single Home appliance Scheduling 20 For full-schedulable home appliance, one needs to schedule when to launch a home appliance at what frequency considering DVFS for how long to minimize monetary cost satisfying that the total energy is consumed. Full-schedulable HA

21. Home Appliance Definition 21

22. Dynamic Programming 22  Given a home appliance, one processes time slot one by one for all possibilities until the last time slot and choose the best solution 000 Choose the solution with total energy equal to E and minimal monetary cost

23. Characterizing 23  For a solution in time slot i, energy consumption e and cost c uniquely characterize its state Time slot iTime slot i+1 (e i, c i )(e i+1, c i+1 )

24. Pruning 24  For one time interval, (e 1, c 1 ) will dominate solution (e 2, c 2 ), if e 1 >= e 2 and c 1 <= c 2 Time slot i (15, 20) (15, 25) (11, 22)

25. Algorithmic Flow of Dynamic Programming 25 Calculate all possible (e, c) Prune all dominated (e, c) Choose the result (e, c) which e = E and c is minimal Schedule Start time t = T s Yes Next time slot t = t + 1 End time t = T e No No Schedule e < E

26. Dynamic Programming based Appliance Optimization 26 (1,2) (2,4) (3,6) (1,1) (2,2) (3,3) 0 t1 t2 (6, 9) (5, 8) (4, 7) (5, 7) (4, 6) (3, 5) (4, 5) (3, 4) (2, 3) (0,0) (3, 3) (2, 2) (1, 1) –# of distinct power levels = k –# time slots = m Runtime : Price Time Dynamic Programming returns optimal solution Power level: {1, 2, 3}

27. Smart Home Scheduling (SHS)  Home appliance level  User level  Community level 27

28. Scheduling Among Multiple Appliances for One User 28 Determine Scheduling Appliances Order Schedule Current Task Update Upper Bound of Each Time Interval An appliance Schedule Appliances Not all the appliance(s) processed All appliance process

29. Smart Home Scheduling (SHS)  Home appliance level  User level  Community level 29

30. Game Approach User 1 User 2 User m............. A game approach is deployed where each customer acts as a player. 30

31. Game Theory 31  For every player in a game, there is a set of strategies and a payoff function, which is the profit of the player.  Each player choose actions from the set of strategies in order to maximize its payoff.  When no player can increase its payoff without changing the actions of others, Nash Equilibrium is reached.

32. Game Formulation in Community Level 32 Players: All the customers in the community Strategy: Choose power levels and launch time to maximize payoff while the constraint conditions can be satisfied

33. Algorithmic Flow in Community Level 33 Each user schedules their own appliances separately All users share information with each other Each user reschedules their own appliances separately Schedule Equilibrium Yes No

34. Multiple Customer Scheduling 34 u1u1 u2u2 u3u3 r1r1 r2r2 r3r3 Communication FPGA First iteration Communication FPGA u1u1 u2u2 u3u3 Second iteration FPGA …… Schedule Low frequency High cost Hard to maintain Equilibrium ……

35. Cloud Computing 35  In Cloud Computing, a new class of network based computing takes place over the Internet  It is a collection/group of integrated and networked hardware, software and Internet infrastructure

36. Why Cloud Computing 36  Advantages –Low cost –High availability, flexibility, elasticity –You can increase or decrease capacity within minutes, not hours or days; –You can commission one, hundreds or even thousands of server instances simultaneously. –Your application can automatically scale itself up and down depending on its needs. –Free of maintenance –Security

37. Service models Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Google App Engine SalesForce CRM LotusLive 37

38. Cloud Taxonomy 38

39. Some Commercial Cloud Offerings 39

40. Amazon EC2 40  Amazon EC2 is one large complex web service.  EC2 provided an API for instantiating computing instances with any of the operating systems supported.  It can facilitate computations through Amazon Machine Images (AMIs) for various other models.

41. Google App Engine 41  This is more a web interface for a development environment that offers a one stop facility for design, development and deployment Java and Python- based applications in Java and Python.  Google offers the same reliability, availability and scalability at par with Google’s own applications  Interface is software programming based

42. Windows Azure 42  Enterprise-level on-demand capacity builder  Fabric of cycles and storage available on-request for a cost  You have to use Azure API to work with the infrastructure offered by Microsoft

43. In Home vs. Cloud Computing Scheduling 43  Cost –High performance FPGA vs. Low performance FPGA + Cloud –Low performance FPGA vs. Low performance FPGA + Cloud  Upgrade –Upgrade FPGA vs. Cloud service  Maintenance –Broken FPGA –Cloud is free of maintenance  Runtime –In Home vs. Cloud Computing

44. Estimation of Computation Time of Low Performance FPGA  FPGA in smart home: 250 MHz –1000 users with 1000 FPGA –Runtime is approximately 10 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (10+0.04)*100 = 1004 sec = 16.73 min  Since the pricing policy is updated each 15 minutes by most utilities, 16.73 minutes are unacceptable.  Why not using some quite high performance machines in each home? 44

45. Cloud Based Distributed Algorithm 45 u1u1 u2u2 u3u3 r1r1 r2r2 r3r3 Communication FPGA First iteration Communication FPGA …… Schedule Equilibrium …… FPGA r1r1 r2r2 r3r3 u1u1 u2u2 u3u3 Cloud

46. Monetary Cost Aware Scheduling Problem  There are different types of machines in cloud with different monetary cost, frequencies and storage  One is required to schedule those users’ tasks to appropriate machines to minimize the monetary cost of the distributed algorithm satisfying the timing constraints 46

47. Runtime (s) u1u1 u2u2 u3u3 u4u4 FPGA12141015 2 GHz1.51.751.251.88 3 GHz11.170.831.25 An example I 47  FPGA: 250 MHz  CPU in cloud: 2 GHz with $0.02/hour, 3 GHz with $0.06/hour  Timing constraints T c = 5 The monetary cost C = 1.25 / 3600 * 0.02 + (1+1.17+1.25) / 3600 * 0.06 = $6.39 * 10 -5. If one schedules tasks of user 3 to CPU with 2 GHz and schedules tasks of user 1, 2 and 4 to CPU with 3 GHz, then The runtime T = max{1.25, 1+1.17+1.25} = 3.42 < T c.

48. An example II 48  FPGA: 250 MHz  CPU in cloud: 2 GHz with $0.02/hour, 3 GHz with $0.06/hour  Timing constraints T c = 5 u1u1 u2u2 u3u3 u4u4 Runtime (s)12141015 2 GHz (s)1.51.751.251.88 3 GHz (s)11.170.831.25 The monetary cost C = (1.5 + 1.75) / 3600 * 0.02 + (0.83 + 1.25) / 3600 * 0.06 = $5.27 * 10 -5. If one schedules tasks of user 1 and 2 to CPU with 2 GHz and schedules tasks of user 3 and 4 to CPU with 3 GHz, then The runtime T = max{1.5 + 1.75, 0.83 + 1.25} = 3.25 < T c.

49. Problem Formulation 49

50. Monetary Cost Problem Formulation 50

51. Linear Programming With Rounding 51

52. Algorithmic Flow 52 Solve the continuous fashion problem combinatorially Discretize the continuous solution Flag all machine to be available Assign task fractionally to the available machine with highest ratio of / Sort all machines increasingly by by ratio of / Runtime of machine is reaching T C Flag the machine to be unavailabe Yes No

53. Combinatorial solving …… f1f1 f2f2 f m-1 fmfm TCTC …… 53 T c – Timing constraints f i - Frequency of cloud machines

54. Discretization 54 f1f1 f2f2 TCTC 1 2 3 f1f1 f2f2 TCTC 1 2 3 T’ (a) (b) 3

55. Theorem 55

56. High Level Algorithm 56 The distributed algorithm needs multiple iterations to achieve the equilibrium, thus the scheduling algorithm needs to handle all the iterations repeatedly. u1u1 u2u2 u3u3 r1r1 r2r2 r3r3 Communication FPGA First iteration Communication FPGA …… Schedule Equilibrium …… FPGA r1r1 r2r2 r3r3 u1u1 u2u2 u3u3 Cloud

57. FPGA & Amazon EC2  Low performance FPGA in smart home: 250 MHz  Computer in cloud: –1 core with 1 ECU (approx.. 1.7 GHz, $0.034 per hour) –1 core with 2 ECU (approx.. 3.5 GHz, $0.068 per hour) –2 cores with 2 ECU (approx.. 3.5 GHz, $0.136 per hour) –4 cores with 2 ECU (approx.. 3.5 GHz, $0.271 per hour)  Communication time: 10kb/250kb/s=0.04s  Observing that there are machines with multiple cores, we can schedule multiple tasks to one machine with multiple cores at the same time 57

58. Comparison for 1000 users  W/o cloud –1000 users with 1000 FPGA –Runtime is approximately 14 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (10+0.04)*100 = 1004 sec = 16.73 min  W/ cloud of 1 core with 1 ECU –1000 computers in cloud –Runtime is approximately 2 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (2+0.04)*100 = 3.4 min (4.92X) 58

59. Comparison for 1000 users  W/ cloud of 1 core with 2 ECU –1000 computers in cloud –Runtime is approximately 1 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (1+0.04)*100 = 1.7 min (9.84X)  W/ cloud of 4 core with 2 ECU (Parallel in four cores) –250 computers in cloud –Runtime is approximately 1 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (1+0.04)*100 = 1.73 min (9.67X) 59

60. Case Study Setup 60  Low performance FPGA in smart home: 250 MHz, $200  High performance FPGA in smart home: 1250 MHz, $2000  Computer in cloud: –1 core with 1 ECU (approx.. 1.7 GHz, $61/yr upfront, $0.034/hr) –1 core with 2 ECU (approx.. 3.5 GHz, $122/yr upfront, $0.068/hr) –2 cores with 2 ECU (approx.. 3.5 GHz, $243/yr upfront, $0.136/hr) –4 cores with 2 ECU (approx.. 3.5 GHz, $486/yr upfront, $0.271/hr) http://www.xilinx.com/support/documentation/data_sheets/ds160.pdf http://www.amazon.com/C3-DRK-Digital-Radio- Kit/dp/B001KBPIOQ/ref=sr_1_8?s=pc&ie=UTF8&qid=1365106998&sr=1-8&keywords=fpga http://aws.amazon.com/ec2/pricing/

61. Case Study Setup (Cont.) 61  Home appliances category –Restrictive-schedulable –Full-schedulable –Non-schedulable Frequency level: 20Hz, 40Hz, 60Hz, 80Hz Start time: 16:00 End time: 18:00 Start time: 0:00 End time: 23:59 Start time: 9:00 Frequency level: 20Hz, 40Hz, 60Hz, 80Hz End time: 18:00

62. Case Study Setup (Cont.) 62  200 to 1000 users in one community  Each user could have 10 – 30 home appliance –30% of restrictive-schedulable home appliance –50% of full-schedulable home appliance –20% of non-schedulable home appliance

63. An Example – One User 63 HAStart timeEnd timeTotal energy (kW.h) Power levels (W) AC17:0020:008{400, 600, 800, 1000, 3000} Washer & Dryer 09:0018:0051000 Dish Washer09:0018:0031000 PHEV18:0007:0012{1900, 3000, 20k, 240k} Refrigerator00:0023:591.250 http://www ​.mpoweruk. ​ com/electr ​ icity_dema ​ nd.htm

64. 64 Total Bill – Monthly Dollars

65. 65 Runtime Minutes

66. 66 High Performance FPGA  FPGA in smart home: 1250 MHz, $2000  Runtime –1000 users with 1000 FPGA –Runtime is approximately 2 seconds in one iteration –Communication time: 10kb/250kb/s=0.04s –100 iterations: (2+0.04)*100 = 204 sec = 3.4 min –No real time issue

67. 67 Total Bill – First Year Dollars

68. 68 Total Bill – Ten Years Dollars

69. 69 Total Bill – Ten Years Cloud computing service cost reduction Dollars  Cloud computing service cost reduction rate: 10%/yr

70. 70 Total Bill – Ten Years FPGA Maintenance Dollars  FPGA maintenance cost: $50/yr

71. 71 Total Bill – Ten Years FPGA Broken Dollars  FPGA broken rate: 2.8% http://homepages.cae.wisc.edu/~aminf/FCCM09%20- %20FPGA%20Design%20Analysis%20of%20the%20Clustering%20Algorithm%20for%20the%20CERN%20Large %20Hadron%20Collider.pdf

72. 72 Total Bill – Ten Years US Dollars Inflation  Inflation rate of US dollars: 2%/yr http://www.usinflationcalculator.com/inflation/historical-inflation-rates/ Dollars

73. Conclusion 73  According to case study, our approach by use of cloud can make several times speed up comparing to low performance FPGA based algorithms such that the timing constraints could be satisfied and archive 18.95% monetary cost reduction on average  If high performance FPGA is chosen, user needs to pay 58.3% on average more than bill without SHS in first year of buying FPGA; user will pay higher than cloud based scheme considering cost reduction of cloud computing, maintenance and broken of FPGA in first ten years  Overall, cloud computing is better than both low performance FPGA and high performance FPGA

74. Further Study 74  Design an algorithm to decide the number of machines in cloud to minimize the reservation cost  More case study will be conducted to generalize my conclusion

75. Thanks 75