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Smart Systems Engineering > Vorlesung 11 > Flashcards

Vorlesung 11 Flashcards

1
Q

What does “System model” describe?

A

Describes how the system works

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2
Q

What does “Architecture” describe?

A

Describes how the system has to be implemented

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3
Q

What is a system?

A

A system is a combination of components that act together to perform a function not possible with any of the individual parts.

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4
Q

What is a model?

A

A model is a formal description of the system, which covers selected information.

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5
Q

Perception vs. reality

A

What we perceive and how we interpret it depends on the frame through which we view the world around us.

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6
Q

What are the characteristics of system models?

A

!Homogeneous or heterogeneous

!Concurrency/parallelism

!Hierarchy

!Communication

!Synchronization models

_Q_Q_Q_Q_Q hier war noch was hintendran zu den einzelnen Charakteristika, aber kA ob wichtig Q:Q_Q_Q_Q_Q

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7
Q

What are some exemplary system models and what attributes do they have?

A

Attributes: Determinism / Concurrent / Time

Finite State Machines (FSM) (yes, no, no)

Petri Net (no, yes, no)

Kahn Process Networks (KPN) (yes, yes, no)

Data Flow (yes, yes, no)

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8
Q

What are Finite State Machines (FSM)?

A

A FSM is a model for deterministic automata, which consists of states and transitions. The current state is changed as a reaction to input signals. Thus, the state holds information about the past. FSMs are ideal for modelling control flow dominant applications such as controllers.

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9
Q

What are examples for FSMs?

A

Mealy automata, Moore automata

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10
Q

What’s the difference between Mealy and Moore automata

A

Mealy generates an output signal with every transition, Moore with every state they reach.
Mealy’s output only depends on inputs. Moore’s output only depends on states.

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11
Q

What are modelled system characteristics of FSMs?

A

not concurrent, deterministic, no explicit notion of time

control-flow-oriented

input-output-relation

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12
Q

Pros / cons about FSMs

A

pro: comprehensive
con: data-flow / calculations are not modelled

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13
Q

What are Petri Nets?

A

Petri Nets are a very general model for concurrent asynchronous systems. It consists of places holding tokens and transitions which are connected to each other. In modelling, places represent conditions and transitions represent events.

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14
Q

When do transitions fire?

A

Transitions may fire, if all input places hold a token. Firing a transition removes one token from each input place of the transition and adds one token on each output place.

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15
Q

What are modelled system characteristics of Petri Nets?

A

asynchronous

concurrent, non-deterministic, no notion of time

distributed

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16
Q

pros / cons of Petri Nets

A

+ concurrent
+ control-flow
+ highly flexible

  • high complexity, can be transformed into FSM —> state explosion
  • dataflow not modelled
  • lack of composition and scalability
17
Q

What is a Kahn Process Network (KPN)?

A

deterministic model for distributed systems
directed graph
nodes = processes
arcs = FIFO queues

_____________________________________________

KPN is a deterministic model for distributed systems. A KPN is a directed graph where nodes represent processes and arcs represent FIFO queues connecting processes.

Processes are deterministic and can perform non-blocking write and blokcing (empty input queue) read operations

18
Q

Modelled system characteristics of KPNs

A

distributed
deterministic
concurrent

19
Q

pros / cons of KPNs

A

+ synchronisation through blocking reads
+ deterministic behaviour independent of execution order
+ control flow can be hidden in node

  • FIFOs in reality have bounded size
  • predictability of termination and boundedness of queues are not given
20
Q

What is special about Data Flow Models

A

Each node must produce and consume a fixed amount of tokens per execution on each of its input/output arcs/fifos

21
Q

pros / cons of Data Flow Models

A

PROS

comprehensive
hierarchy can be modelled using extended models

CONS

Control flow is not modelled
less expressive than KPN

22
Q

modelled system characteristics Data Flow Models

A

deterministic
dataflow
inherent concurrency

23
Q

continuous time model - modelled system characteristics

A

concurrency, timing, deterministic

usually used together with a discrete event model

24
Q

pros/cons of continuous time models

A

+ synchronisation possible
+ hierarchy and scalability

  • restricted to a limited set of applications
  • simulation requires discretization
  • low performance of simulation
25
Q

example of continuous time model tool

A

simulink

Smart Systems Engineering (12 decks)

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