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Octopus: document handling

Document handling:three modeling approaches

Marc Voorhoeve

Venkatesh Kannan

TUE

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Octopus project

Broker: EESI

Industry partner: Océ

Researchers:NijmegenTUE - Electrical EngineeringTUE - Computer Science

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Octopus: document handlingData path:concurrent job execution

scan

control

print

retrieve

ImageProcessing

store

IP1 IP2

resources

user

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Engineering decisions:- resource capacity- scheduling rules

Octopus aim:Engineering support

Frameworks:- TA (Timed Automata)- SDF (Synchronous Data Flow)- CPN (Colored Petri nets)

SimulationState spaceexploration

Approaches

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Timed automata:labeled transition system with clock variables,that can be set and tested.

states,actions (maybe silent),clock assignments,clock conditions.

idle busy

recover

start?

0:x0:x

etxrtx

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Kind of merge with synchronization of clocks.

idle busy

recover

start?

0:x

0:xetxrtx

s0 s1start! s2start!

0:y

(idle,s0) (busy,s1) (recover,s1) (idle,s1) (busy,s1) (recover,s1) (idle,s2)

0,0 yx ety re tty )(2 re tty

Temporal predicates (CTL) can be assessed,e.g. existence of execution path with duration D.

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CPN (colored Petri nets):Petri net with arc inscriptions, firing conditionsand subnet hierarchy.Eager firing.

idle busy

recover

start

etx @

xx

xx

( )

rt@)(

start s0

s1

s2

a

b

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SDF:action-based,Petri net subclass(marked graph)

Firing delaysfast optimalschedule computation

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Special challenge: modeling USB storage/retrieval.

unidirectional capacity: H, bidirectional capacity: Lcapacities L,H in MB/sec satisfying L < H < 2L.

Suppose 3H upstream; after 1 sec L downstreamtiming diagram:

0 1 2 3 3+(H-L)/H

HL

2L

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USB model:hybrid system.

Discrete approximation in TA possible.Exact CPN model by data manipulation.SDF model is still under construction.

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Experiences so far:- coordination via regular meetings- agreement on parameters of common model- mutual validation has been achieved (despite several difficulties)

Next stages:- validation with current project - exploration of engineering alternatives- adaptivity?

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CPN tradeoff: structure in net vs. color

more structurevisual modellow-levelless flexible

structure in coloropaquehigh-levelflexibility

TA tradeoff: model vs. predicate

constraints in modelsmaller state spacemore models

constraints in predicatelarger state spaceless models (flexibility)

Some difficulties were caused by the solutionsthat the various frameworks allow.

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CPN model therefore opts for flexibility.

Other difficulties caused by unstable requirements.

color set JOB: sequence of tasks.

a

b

[a,b]

x != [ ]

x tail(x)

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jobQ

JOB

scanner scanIP IP1 IP2 USB printIP

startedJob

JOB

schedulerJobGenerator

newJob

JOB

memory

INT

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start do

free

jobQ

JOB

startedJob

JOB

compInfo

paperInfo

end

parameters

structure of scanner, scanIP, IP1, IP2, printIP

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job in join

trigger

USBjobList

updatejobout

Job Rate LastUpdateTime

JOB JOB

structure of USB subnet

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0 1 2 3 3+x

HL

2L

Depicted USB scenario:

0 U H 0 31 U L 1 3+x

D L 1 22 U H 23 U H 33+x -

time job rate last_update_time trigger

x=(H-L)/H

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TA, SDF:find "optimal" schedulewithin state spacefor a given job sequence

CPN:nondeterministiceager execution

Mutual validation of models:- remove freedom- model within intersection- compare generated schedules (traces)

Mutual validation: do the models match?

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Modeling power vs. analysis options:

CPN: most power, least analyzable.SDF: least power, analysis limited but fastTA: wide range of analysis options danger of state space explosion

Possible developments for Océ:- competition- cooperation

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Conclusions:

Combination of formal frameworksinteresting, but time-consuming.

More time needed for assessment.

Questions?

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