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 Multiple-User Smart Home

2. Multiple Users in a Community 2

3. Multiple Users  Pricing at 10:00am is cheap, so how about scheduling everything at that time? 3 Energy Accumlation 10:00am

4. Game Theory Based Scheduling 4

5. 5  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 chooses from the set of strategies in order to maximize its payoff.  When no player can increase its payoff without decreasing other users’ payoff, Nash Equilibrium is reached. Game Theory Based Scheduling

6. Game Formulation in Community Level 6 Players: All users in the community Strategy: Choose power levels and launch time to maximize payoff while satisfying constraints

7. Community Size 7  Small community: Less than 100 users  Medium community: 100 ~5,000 users  Large community: More than 5,000 users

8. Small Community: Fully Distributed Architecture 8 In fully distributed architecture, each customer uses own smart home scheduler to communicate with other users for information exchange and computes smart home scheduling solution.

9. 9 Communication/Synchronization …… Equilibrium/Schedule … Embedded Processor User 2 Embedded Processor User n Embedded Processor User 1 Communication/Synchronization … Embedded Processor User 2 Embedded Processor User n Embedded Processor User 1 Iteration 1 Iteration 2 Algorithmic Illustration For Small Community

10. Algorithmic Flow For Small Community 10 Each user schedules their own appliances separately to maximize payoff using dynamic programming All users share information with each other Each user reschedules their own appliances separately by dynamic programming Schedule Equilibrium Yes No Determine scheduling appliances order Schedule current appliance by dynamic programming Schedule Appliances All appliances scheduled Yes No

11. Problem With Fully Distributed Architecture 11 Communication/synchronization problem: Assume that there are 100 iterations needed for the game theory based algorithm. Communication/synchronization needs to be performed at the end of every iteration. It is not realistic for big community to deploy fully distributed due to the complexity of synchronizes among a large number of users. Each user performs the game theory based algorithm at their own side and communicates with all other users after every single iteration.

12. Medium Community: Fully Centralized Architecture 12 Users only communicate with computer cluster twice, at the beginning and end. Communication/synchronization is not needed any more among users. Communication/synchronization within computers or CPU cores is much easier and faster. Each user sends the scheduling tasks to a computer cluster which compute the scheduling solutions of all users.

13. Algorithmic Illustration For Medium Community 13 Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User 1 Interface User 2 Interface User n Interface User 1 Interface User 2 Interface User n …

14. Solve the continuous fashion problem combinatorially Discretize the continuous solution Flag all computers to be available Assign task fractionally to the available computer with lowest ratio of / Sort all computers increasingly by ratio of / Runtime of computer is reaching T C Flag the computer to be unavailable Yes No Each computer runs tasks of users in parallel All computers share information with each other to synchronize Each computer reruns the tasks of users given the information of other users Schedule Equilibrium Run iteratively Users send tasks to computer s Schedule tasks of users to computersGame theory based algorithm Computers send back the results to users # iterati ons = k ϒ Yes No Yes … User 1 User 2 User 3 User n … User 1 User 2 User 3 User n Algorithmic Flow For Medium Community

15. Problem With Fully Centralized Architecture 15 Cannot handle large community Communication delay Limited computation power and high maintenance cost Security concerns

16. Large Community: Hierarchical Architecture 16 There are 10 million users in a big community. It can be partitioned into 2k smaller groups, in which the number of users is 5k. The communication overhead within each group is acceptable. There is no flooding packets problem.

17. Algorithmic Flow For Intra-Community Optimization 17 Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User 1 Interface User 2 Interface User x1 Continue to Inter-community optimization

18. Algorithmic Flow For Inter-Community Optimization 18 Energy consumption summation of Intra- community optimization Pick k time intervals with the largest total energy consumption Reduce the k energy consumption by δ Continue to Intra-community optimization/Schedule Pick k time intervals with the smallest total energy consumption Increase the k energy consumption by δ

19. Algorithmic Illustration For Large Community 19 Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User 1 Interface User 2 Interface User x1 Continue to Inter-community optimization Energy consumption summation of Intra- community optimization Pick k time intervals with the largest total energy consumption Reduce the k energy consumption by δ Continue to Intra-community optimization/Schedule Pick k time intervals with the smallest total energy consumption Increase the k energy consumption by δ

20. 20 Thanks