EE5900 Advanced Embedded System For Smart Infrastructure

1. EE5900 Advanced Embedded System For Smart Infrastructure Computationally Efficient Smart Home Scheduling

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

3. Smart Home Power Line Communication Line 3

4. End Start Dish washer 13:00 18:00 Landry machine 09:00 18:00 PHEV 18:00 08:00 AC 17:00 N/A …… 4

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

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

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

8. Plug-in Hybrid Electric Vehicles (PHEV) 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 8

9. Contemporary Hybrids Honda Insight Toyota Camry Toyota Prius Toyota Highlander Honda Civic Honda Accord Lexus RX400h Lexus GS450h Saturn Vue Chevy Silverado Ford Escape 9

10. Charging of PHEV 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 10

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 https://rrtp.comed.com/live-prices/?date=20130404 12

13. Dynamic Voltage and Frequency Scaling (DVFS) Powerr Power 5 cents / kwh 10 cents/kwh 5 cents / kwh 10 cents/kwh 10 kwh 5 kwh 1 2 3 1 2 Time Time (b) (a) cost = 5 kwh * 10 cents/kwh + 5 kwh * 5 cents/kwh = 75 cents cost = 10 kwh * 10 cents/kwh = 100 cents 13

14. Smart Home Scheduling (SHS) • 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 14

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

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

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

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

19. Single Home appliance Scheduling Restrictive-schedulable HA 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. 19

20. Single Home appliance Scheduling Full-schedulable HA • 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. 20

21. Home Appliance Definition • Ts: Start time • Te: End time • Pi: Power level • E: Total required energy • : Unit price of time slot t 21

22. Dynamic Programming • Given a home appliance, one processes time slot one by one for all possibilities until the last time slot and choose the best solution 0 0 0 Choose the solution with total energy equal to E and minimal monetary cost 22

23. Characterizing • For a solution in time slot i, energy consumption e and cost c uniquely characterize its state 23

24. Pruning • For one time interval, (e1, c1) will dominate solution (e2, c2), if e1>= e2 and c1<= c2 24

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

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

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

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

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 • 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. 31

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

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

34. Multiple Customer Scheduling u1 u2 u3 FPGA FPGA FPGA First iteration r1 r2 r3 • Low frequency • High cost • Hard to maintain Communication Communication u1 u2 u3 Second iteration FPGA FPGA FPGA …… …… Equilibrium Schedule 34

35. Cloud Computing • 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 35

36. Why Cloud Computing • 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 36

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

38. Cloud Taxonomy 38

39. Some Commercial Cloud Offerings 39

40. Amazon EC2 • 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. 40

41. Google App Engine • 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 41

42. Windows Azure • 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 42

43. In Home vs. Cloud Computing Scheduling • 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 43

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 u1 u2 u3 FPGA FPGA FPGA First iteration r1 r2 r3 Communication Communication Cloud r1 r2 r3 …… …… Equilibrium Schedule FPGA FPGA FPGA u1 u2 u3 45

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. An example I • FPGA: 250 MHz • CPU in cloud: 2 GHz with $0.02/hour, 3 GHz with $0.06/hour • Timing constraints Tc = 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 monetary cost C = 1.25 / 3600 * 0.02 + (1+1.17+1.25) / 3600 * 0.06 = $6.39 * 10 -5. The runtime T = max{1.25, 1+1.17+1.25} = 3.42 < Tc. 47

48. An example II • FPGA: 250 MHz • CPU in cloud: 2 GHz with $0.02/hour, 3 GHz with $0.06/hour • Timing constraints Tc = 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 monetary cost C = (1.5 + 1.75) / 3600 * 0.02 + (0.83 + 1.25) / 3600 * 0.06 = $5.27 * 10 -5. The runtime T = max{1.5 + 1.75, 0.83 + 1.25} = 3.25 < Tc. 48

49. Problem Formulation • Given users in smart home scheduling problems with runtime running in local machine with frequency , types of machines in cloud with frequency and monetary cost , one needs to schedule these users’ tasks to machines such that the total monetary cost is minimized and maximum runtime over all the machines satisfies the timing constraints. 49

50. Monetary Cost Problem Formulation 50