Challenges in Physical Modeling for Adaptation of Cyber-Physical Systems
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Transcript of Challenges in Physical Modeling for Adaptation of Cyber-Physical Systems
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Ivan Ruchkin, Selva Samuel, Bradley Schmerl, Amanda Rico, and David Garlan
Institute for Software Research, Carnegie Mellon University
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CPS operate in uncertain contexts
Need to adapt to unanticipated situations
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System & environment under adaptation
Adaptation with models
Phenomena
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System & environment under adaptation
Adaptation with physical models
Physical phenomena
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NUC(computer)
Kinect(sensor)
Base(actuator & battery)
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Abstractions of physical objects and interactions
Beyond simple discrete models
Objects may be in the system, in the environment, or on the border
Example: power model for TurtleBot
How much does each task consume?
How much power is left given current voltage?
How long does it take to charge?
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Software models guide state-of-the-art adaptive systems
Physical models are often implicit or assumed
In CPS, we need both software and physical models!
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1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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Many formalisms and tools are available for modeling CPS
Differential equations, signal flow graphs, automata
Position: no single formalism is enough to model adaptive CPS; we need to embrace their multiplicity
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Evaluate individual formalisms Expressiveness▪ Linear/non-linear, continuous/discrete, classes of
functions (polynomials, transcendental functions, etc.)
Types of analyses supported▪ Trade-off between expressiveness and computing cost
Engineering expertise▪ Novices: higher effort and lower quality
We need approaches to integrate formalisms! Difficult problem, outside of talk’s scope
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We chose a linear real-valued regression model
Continuous changes in parameters
Easily embeddable into other models
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P(v, t) = Av + Bt + C
15time (s)
po
wer
(wh
)
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1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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Goal: maximize value of each model
Analytical power: strength of predictions and explanations
Fragility: amount of rework to accommodate future changes
Computational cost: amount of processing needed for analysis
Position: the way we build physical models affects their value. We need more guidance!
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Theory-driven
Physical theory dictates first principles
Calibrate with data
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Data-driven
Collect data first
Then create abstractions from it
time (s)
po
wer
(wh
)
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We chose to use data-driven approach
Low expertise with theory-driven models
Ok with low-precision far-horizon predictions
The model is fragile: hard to change
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1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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Software models in adaptation are used for:
State estimation and prediction
Triggering adaptive changes
Choosing adaptive strategy
+ Continuous improvement of models themselves
Position: physical models should also be treated as first-class entities in adaptation
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Clear representation
Either separate models or explicit embedding
Easier change and reuse
Coordinated use with cyber models
Estimation, prediction, choice
Models themselves should be adapted
Model value & cost should be the guiding factors
Need to reason about model value at run time!
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Physical models in adaptive CPS are important and difficult to build
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Challenge Position
Selecting modeling formalism
Embrace multiplicity; use formalisms based on expressiveness, analyses, and expertise.
Obtaining physical models Model value should the guiding factor. More guidance is needed to connect model- building and model value.
Using physical models in adaptation
Physical models should be treated as first-class entities and adapted based on their value at run time.