# based on Unfoldings

Networking and Communications

Oct 24, 2013 (4 years and 7 months ago)

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Modular Processings

based on Unfoldings

DistribCom Team

Irisa/Inria

UFO workshop
-

June 26, 2007

Assembling Petri nets

products, pullbacks, unfoldings and trellises

Modular computations

on a constraint graph : an abstract viewpoint

Application 1: modular diagnosis

or modular computation of a minimal product covering

Application 2: modular prefixes

or how to compute a FCP directly in factorized form

Conclusion

Outline

Nets as Products of Automata

Caution

: in this talk, for simplicity

we limit ourselves to safe Petri nets,

although most results extend to ½ weighted nets

we represent safe nets in “complemented” form,

i.e. their number of tokens remains constant

Building bloc: a
site

or
variable V

= labeled automaton

labeling of transitions

V
= (
S
,
T
,s
0
,

,
¸
,
¤
)

¸
: T

¤

Nets as Products of Automata
(2)

Composition of variables by product :

disjoint union

of places

transitions with shared labels are “glued”

transitions with private labels don’t change

S =
V
1
£

V
2

£

V
3

This product yields a safe (labeled) nets,

and extends to safe nets

Interest of Product Forms

The 1
st

interests are

a natural construction method starting from modules

the compactness of the product form

on this example, the expanded product contains
m*n

m+n

in the factorized form

Composition by Pullback

Generalizes the product

allows interactions of nets by an interface (sub
-
net)

outside the interface, interactions are still by shared labels

S =
V
1
£

V
2
£

V
3

= (
V
1
£
V
2
)
Æ

(
V
2
£
V
3
)

Main property

Graph of a Product Net

Interaction graph of a net

shared labels define the local interactions…

… but it’s better to re
-
express interactions under the form of
shared variables (or sub
-
nets).

S =
V
1
£

£

V
n

V
1
£

V
2
£

V
3

= (
V
1
£
V
2
)
Æ

(
V
2
£
V
3
)

= S
1

Æ
S
2

Translation in terms of
pullbacks

define
components

S
i

in order to “cover” the shared labels

Unfoldings in Factorized Form

The key =
Universal Property

of the unfolding of S

Let denote the unfolding of S,

and its associated folding (labeling)

8
O,
8
Á
:O

S,
9
!
Ã
:O

U
(S),
Á
= f
S

±

Ã

f
S
:
U
(S)

S

U
(S)

Consequences:

and thus preserves products, pullbacks, …

U

U
(S) =
U
(S
1
)
£
O

£
O

U
(S
n
)

S = S
1
£

£

S
n

)

U
(S) =
U
(S
1
)
Æ
O

Æ
O

U
(S
m
)

S = S
1

Æ

Æ

S
m

)

Unfoldings in Factorized Form
(2)

Example:

U
(S) =
U
(
V
1
)
£
O

U
(
V
2
)
£
O

U
(
V
3
)

S =
V
1
£
V
2
£
V
3

)

Important properties

The category theory approach naturally provides

an expression for operators (and )

recursive procedures

to compute them (as for unfoldings)

notions of
projections

associated to products/pullbacks:

£
O

Æ
O

¦
S
i
:
U
(S)

U
(S
i
)

Important properties

The category theory approach naturally provides

an expression for operators (and )

recursive procedures

to compute them (as for unfoldings)

notions of
projections

associated to products/pullbacks:

£
O

Æ
O

¦
S
i
:
U
(S)

U
(S
i
)

Important properties

The category theory approach naturally provides

an expression for operators (and )

recursive procedures

to compute them (as for unfoldings)

notions of
projections

associated to products/pullbacks:

£
O

Æ
O

¦
S
i
:
U
(S)

U
(S
i
)

Important properties

The category theory approach naturally provides

an expression for operators (and )

recursive procedures

to compute them (as for unfoldings)

notions of
projections

associated to products/pullbacks:

£
O

Æ
O

¦
S
i
:
U
(S)

U
(S
i
)

Important properties
(2)

Thm

let O
i

be an occ. net of component S
i
,

then is an occ. net of

define then

and this is the
minimal product covering

of O

O=O
1
£
O

£
O

O
n

S=S
1
£

£
S
n

O’
i

=
¦
S
i
(O)
v

O
i

O=O’
1
£
O

£
O

O’
n

The
reduced

occurrence nets

represent the behaviors of component S
i

that remain

once S
i

is inserted in the global system S

or the local view in each component S
i

of the behaviors of the
global system S

are interesting objects !

O’
i

v

O
i

Factorized forms of unfoldings are often more compact…

…but they can however contain useless parts.

Trellises in Factorized Form

The trellis of net S is

obtained by merging conditions of with identical height

a close cousin of merged processes
(Khomenko
et al.,

2005)

T
(S)

U
(S)

time is counted

independently in each V
i

for S = V
1
£

£

V
n

Trellises in Factorized Form

The trellis of net S is

obtained by merging conditions of with identical height

a close cousin of merged processes
(Khomenko
et al.,

2005)

enjoys exactly the same factorization properties as unfoldings

T
(S) =
T
(S
1
)
£
T

£
T

T
(S
n
)

S = S
1
£

£

S
n

)

T
(S) =
T
(S
1
)
Æ
T

Æ
T

T
(S
m
)

S = S
1

Æ

Æ

S
m

)

T
(S)

U
(S)

Assembling Petri nets

products, pullbacks, unfoldings and trellises

Modular computations

on a constraint graph : an abstract viewpoint

Application 1: modular diagnosis

or modular computation of a minimal product covering

Application 2: modular prefixes

or how to compute a FCP directly in factorized form

Conclusion

Outline

S
2

S
3

S
4

“Abstract” Constraint Reduction

Ingredients :

variables

“systems” or “components” S
i

defined by (local) constraints on

V
max

= {
V
1
,
V
2
,…}

V
i

µ

{
V
1
,…,V
n
}

S
1

V
1

V
5

V
3

V
2

V
7

V
6

V
4

V
8

S = S
1
Æ
S
2

a composition operator (conjunction)

“Abstract” Constraint Reduction
(2)

Reductions:

for , reduces constraints of S to variables V

reductions are projections

V
µ
V
max

¦
V
(S)

¦
V
1

±

¦
V
2

=
¦
V
1
Å
V
2

Central axiom
:

S
1

operates on V
1
, S
2

operates on V
2

let then

i.e.

all interactions go through shared variables

V
3

V
1
Å
V
2

¦
V
3
(S
1
Æ
S
2
) =
¦
V
3
(S
1
)
Æ
¦
V
3
(S
2
)

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

Modular reduction algorithms

Problem :

Given where S
i

operates on V
i

compute the reduced components

i.e.
how does S
i

change once inserted into the global S ?

S = S
1
Æ

Æ

S
n

S’
i

=
¦
V
i
(S)

This can be solved by
Message Passing Algorithms

(MPA)

always converges

only involves local computations

exact if the graph of S is a (hyper
-
) tree

What about systems with loops ?

Message passing algorithms

converge to a unique fix point
(independent of message scheduling)

that gives an upper approximation:

How good are their results ?

Local extendibility to any tree around each component.

¦
V
i
(S)
v

S’
i

v

S
i

What about systems with loops ?

Message passing algorithms

converge to a unique fix point
(independent of message scheduling)

that gives an upper approximation:

How good are their results ?

Local extendibility to any tree around each component.

¦
V
i
(S)
v

S’
i

v

S
i

Assembling Petri nets

products, pullbacks, unfoldings and trellises

Modular computations

on a constraint graph : an abstract viewpoint

Application 1: modular diagnosis

or modular computation of a minimal product covering

Application 2: modular prefixes

or how to compute a FCP directly in factorized form

Conclusion

Outline

centralized supervizor

Distributed system monitoring…

ab c

b b

a b

c

aa

distributed supervision

Consider the net

and move to trajectory sets
(unfolding or trellis)

In the category of occurrence nets
(for ex.),

we have

a composition operator, the pullback

trajectories of S are in factorized form

we have projection operators on occ. nets,

where V
i

are the variables of S
i

Thm:
projections and pullback satisfy the central axiom

(here we cheat a little however…)

We are already equipped for that !

Æ
O

S = S
1

Æ

Æ

S
m

U
(S) =
U
(S
1
)
Æ
O

Æ
O

U
(S
m
)

¦
V
i

A computation example

A computation example

A computation example

A computation example

A computation example

A computation example

Assembling Petri nets

products, pullbacks, unfoldings and trellises

Modular computations

on a constraint graph : an abstract viewpoint

Application 1: modular diagnosis

or modular computation of a minimal product covering

Application 2: modular prefixes

or how to compute a FCP directly in factorized form

Conclusion

Outline

Objective

Given

compute a finite complete prefix of in factorized form

Obvious solution:

compute a FCP of

then compute its minimal pullback covering

where

S = S
1

Æ

Æ

S
m

U
(S)

U
s
(S)

U
(S)

U
s
(S)
v

U
’(S
1
)
Æ

Æ U
’(S
m
)

U
’(S
i
) =
¦
V
i
(
U
s
(S))

but this imposes to work on the global unfolding…

… we rather want to
obtain directly the factorized form

Local canonical prefixes don’t work

Canonical prefix

defined by a
cutting context

Θ = ( ~ ,

, {
κ
e
}
e

E

)

~
equivalence relation on
Conf

set of reachable markings

Conf

partial order on
Conf

refining inclusion

{
κ
e
}
e

E
a subset of
Conf
,

configurations used for cut
-
off identification

cut
-
off

event

Extended canonical prefix

Toy example :

two components, elementary interface (=automaton)

S =
A
£
C
£
B

= (
A
£
C
)
Æ
(
C
£
B
)

= S
A

Æ

S
B

interface

Extended canonical prefix
(2)

extended prefix of w.r.t. its interface
C

restriction of the cutting context
Θ
C

= (~,

, {
κ
e
}
e

E

)

to

particular configurations
κ
e

e cut
-
off event, corresponding event e’ :

κ
e
~
κ
e’

and
κ
e’

κ
e

where usually
κ
e
=[e]

if e

is a

private event, then
P
C

(
κ
e

κ
e’
)=Ø

if e is an interface event, then e’ is also an interface event

S
A

where

is the symmetric set difference

Extended cut
-
off event

e : extended cut
-
off

e’ : interface event

Summary net

Summary net =

behaviors allowed by an extended prefix on the interface:

obtained by projecting the extended prefix on the interface,

and refolding matching markings

merge

Distributed computations

augmented

prefixes

Distributed computations

extract

summary nets

Distributed computations

exchange

summary nets

Distributed computations

build

pullbacks

Distributed computations

construct

prefixes

Distributed computations

Killed in the pullback

Local factors are a little too conservative

(not the minimal pullback covering of the FCP)

Assembling Petri nets

products, pullbacks, unfoldings and trellises

Modular computations

on a constraint graph : an abstract viewpoint

Application 1: modular diagnosis

or modular computation of a minimal product covering

Application 2: modular prefixes

or how to compute a FCP directly in factorized form

Conclusion

Outline

A few lessons…

Factorized forms of unfoldings are generally more compact.

One can work directly on them, in an efficient modular
manner, without ever having to compute anything global.

Optimal when component graphs are trees.

Sub
-
optimal, but provide “good” upper approximations
otherwise.

…and some questions

Finite complete prefixes in factorized form:

we need to understand better how to compute them,

and provide complexity results.

Can this be useful for model checking?

Can this be useful for distributed optimal planning?

(see last talk today)