EE5900: Cyber-Physical Systems
Multiple-User Smart Home System
Multiple Users in a Community
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Multiple Users
 Pricing at 10:00am is cheap, so how about scheduling everything at
that time?
Energy Accumlation
10:00am
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Game Theory Based Scheduling
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Game Theory Based Scheduling
 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.
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Game Formulation in Community Level
Players: All users in the community
Strategy: Choose power levels and launch time to maximize
payoff while satisfying constraints
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Community Size
Small community: Less than 100 users
Medium community: 100 ~5,000 users
Large community: More than 5,000 users
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Small Community: Fully Distributed Architecture
In the fully distributed architecture, each
customer uses own smart home scheduler to
communicate with other users for information
exchange and computes smart home
scheduling solution.
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Algorithmic Illustration For Small Community
Iteration 1
User1
User2
Embedded
Processor
Embedded
Processor
…
Usern
Embedded
Processor
Communication/Synchronization
Iteration 2
User1
User2
Embedded
Processor
Embedded
Processor
…
Usern
Embedded
Processor
Communication/Synchronization
……
Equilibrium/Schedule
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Algorithmic Flow For Small Community
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
No
Equilibrium
Yes
Schedule
Appliances
Determine scheduling
appliances order
Schedule current appliance
by dynamic programming
All appliances
scheduled
No
Yes
Schedule
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Problem With The Fully Distributed Architecture
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
the fully distributed architecture due to the
complexity of synchronization 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.
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Medium Community: Fully Centralized Architecture
• 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.
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Algorithmic Illustration For Medium Community
User1
User2
Interface
Interface
Parallel
Computing
…
Usern
Interface
Each core schedules
assigned tasks of users in
parallel
Run iteratively until convergence
All cores share information
with each other to
synchronize
Each core reschedules the
assigned tasks given the
information of other users
Equilibrium
Schedule
Interface
Interface
User1
User2
…
Interface
Usern
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Algorithmic Flow For Medium Community
Schedule tasks of users to computers
Sort all
computers
increasingly by
ratio of 𝒄/𝒇
User1
Solve the continuous fashion problem
combinatorially
User2
Flag all computers to
be available
Assign task
fractionally to the
available computer
with lowest ratio of 𝒄/
𝒇
Run iteratively
Discretize the
continuous
solution
No
#
iterati
ons =
kϒ
User3
Computers
send back
the results
to users
Each computer
reruns the tasks
of users given the
information of
other users
Equilibrium
Flag the computer to
be unavailable
User2
Yes
Yes
Usern
User1
…
…
…
…
No
Runtime of
computer is
reaching TC
Each computer
runs tasks of
users in parallel
All computers
share information
with each other to
synchronize
User3
Users
send
tasks to
computer
s
Game theory based algorithm
No
Usern
Yes
Schedule
Problem With The Fully Centralized Architecture
Cannot handle large community
• Communication delay
• Limited computation power and
high maintenance cost
• Security concerns
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Large Community: Hierarchical Architecture
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.
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Algorithmic Flow For Intra-Community Optimization
User1
User2
Interface
Interface
Parallel
Computing
…
Userx1
Interface
Each core schedules
assigned tasks of users in
parallel
Run iteratively until convergence
All cores share information
with each other to
synchronize
Each core reschedules the
assigned tasks given the
information of other users
Equilibrium
Schedule
Continue to Inter-community optimization
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Algorithmic Flow For Inter-Community Optimization
Energy consumption
summation of Intracommunity optimization
Pick k time intervals with the largest
total energy consumption
Reduce the k energy consumption
by δ
Pick k time intervals with the
smallest total energy consumption
Increase the k energy consumption
by δ
Continue to Intra-community
optimization/Schedule
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Algorithmic Illustration For Large Community
User1
User2
Interface
Interface
Parallel
Computing
…
Energy
Userx1 consumption
summation of IntraInterface
community optimization
Each
core schedules
Pick
k time intervals with the largest
assigned tasks of users in
total energy consumption
parallel
Run iteratively until convergence
All cores share information
the k energy
withReduce
each other to
synchronize
by δ
consumption
Each core reschedules the
assigned tasks given the
time
information Pick
of otherkusers
intervals with the
smallest total energy consumption
Equilibrium
Increase
the k energy consumption
Schedule
by δ
Continue to Intra-community
optimization/Schedule
Continue to Inter-community optimization
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Thanks
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