leiden workshop 20/06/2007 0 presentation of the cadp toolbox cadp toolbox what is cadp ? lotos...

20/06/2007 1Leiden Workshop

Presentation of the CADP toolbox

CADP toolboxWhat is CADP ?LOTOS languageTools for functional verification

CADP extended for performance evaluationIMC formalismFrom LOTOS to Markov chainFrom LOTOS to Markov chain – example -Tools for performance evaluation


CADP:« Construction and Analysis of Distributed Processes »

Developped at INRIA Rhônes-Alpes (France) by the VASY team

Toolbox for the design of communication protocols and distributed systems.

What is CADP?


LOTOS language

LOTOS = Process Algebra (CCS & CSP) +

Abstract Data Type Algebra (ACT-ONE)caesar.adt compiler

caesar compiler


PUSH

process queue_behavior [PUSH , POP] (SMax:Nat, Q: Queue) : noexit := [getCurrentSize(Q)<SMax] -> PUSH; queue_behavior[PUSH , POP] (SMax,Push(Q)) [] [getCurrentSize(Q)>0] -> POP; queue_behavior[PUSH , POP] (SMax,Pop(Q)) endproc

POP

PUSH

POP

PUSHPOP

LOTOS language

bcg format


PUSHPOP

Physicalqueue

memory

TO_MEM FROM_MEM

memory.bcg = generation of memory.lotos;phys_queue.bcg = generation of phys_queue.lotos;

system.bcg = memory.bcg |[TO_MEM, FROM_MEM]| phys_queue.bcg;

queue.bcg = hide TO_MEM, FROM_MEM in system.bcg

LOTOS language


Tools for functional verification

Model checking on the LTSVarious temporal logics and mu-calculus (evaluator, XTL)

Equivalence checkingMinimization and comparisons modulo bisimulations relations(bcg_min, bisimulator)

Simulation & co-simulationVisual checking (bcg_edit)Step-by-step simulation (ocis)C simulator (caesar –simulator)


The behavior of a physical system can often be represented by :All the states the system may occupyHow the system move from one state to another

Functional behavior: (LTS)

action based

Timed behaviour: (CTMC)

time based

rate λ

rate μ

rate λrate μ

PUSH

POP

PUSHPOP

Performance measuresNo composition, synchronization…

↓ ↓ ↓Performance evaluation

of complex systems reserved to specialists

CompositionConcurrencySynchronization

↓ ↓ ↓Description of large

systems

&& Formal verification

IMC

IMC formalism


IMC formalism

IMC

Interactive transitionsSynchronizationComposition

Markovian transitionsRepresent delays

Hiding of Markovian transitions and minimization

LTS

Hiding of Interactive transitions and stochastic minimization

CTMC

1 model

Functional verification

Performance evaluation


Performance evaluation with LOTOS/CADP :

Introduction of Markov transitions in LOTOS models.≈ Introduction of delays in LOTOS models.

=> Generation of an Interactive Markov Chain (IMC)

Identifying the start and end of relevant timing delays in the

model1

Exposing each start and end as LOTOS

gates2

Identifying the distribution of the

delay3

Approximating the delays as CTMC

4

Embedding each delay into start/end

gates5

From LOTOS to Markov chains


Time between 2 PUSH (δ1)

Time between 2 POP (δ2)

Time needed to process a PUSH (λ)Time needed to process a POP (μ)


gates2


delays3


4


gates5


model 1

ENVIRONMENT ENVIRONMENTSYSTEM

RSP

PUSH_RQ

GENERATOR

QUEUE[ PUSH_RQ, PUSH_RSP,

POP_RQ, POP_RSP,]CONSUMER

PUSH_RSP

POP_RQ

POP_RSP

RQ RSP

RQ

PUSH

time

RQ RSP

POP

RQ RSP

RQ RSP

QUEUE[ PUSH_RQ, PUSH_RSP,

POP_RQ, POP_RSP,λ_START, λ_STOP,μ_START, μ_STOP ]

GENERATOR […] :δ1_START; δ1_STOP; PUSH_RQ !DATA; PUSH_RSP; GENERATOR […]

CONSUMER […] :δ2_START; δ2_STOP; POP_RQ; POP_RSP ?Elmt; CONSUMER […]

Proba

time

1

0

GEN_DELAY

δ1 _S

TO

P

δ1_S

TA

RT

PUSH_DELAY

λ_S

TO

P

λ_S

TA

RT

POP_DELAY

μ_S

TO

P

μ_S

TA

RT

CONS_DELAY

δ2_S

TA

RT δ

2 _STO

P

POP_DELAY […] :μ_START; μ_DELAY; μ_DELAY; μ_STOP; POP_DELAY […]

PUSH_DELAY […] :λ_START; λ_DELAY; λ_DELAY; λ_STOP; PUSH_DELAY […]

GEN_DELAY […] :δ1_START; δ1_DELAY; δ1_STOP; GEN_DELAY […]

CONS_DELAY […] :δ2_START; δ2_DELAY; δ2_STOP; CONS_DELAY […]

From LOTOS to Markov chains- example -


Performance evaluation with LOTOS/CADP :

Introduction of Markov transitions in LOTOS models.≈ Introduction of delays in LOTOS models.

=> Generation of an Interactive Markov Chain (IMC)

Hiding of the non-Markovian transition and minimisation

=> generation of a Markov Chain (CTMC)

Performance evaluation based on the analysis of these CTMC.


model1


gates2


delay3


4


gates5

From LOTOS to Markov chains


Tools for performance evaluation

Stochastic minimization (bcg_min)Based on the maximum progress rule

=> generation of a Markov Chain (CTMC)

Performance evaluation based on the analysis of a CTMC.Transient state probabilities (bcg_transient)Steady state probabilities (bcg_steady)Throughput results (-thr option)

Use of the state probabilities for more complex measuresWeighted sum of the state probabilities

Mean queue lengthMean time before failure…

API in CADP for graph exploration for this kind of computation


Summary : performance evaluationmy_model_with_start_and_stop_delay_gates.bcg =

generation of my_model_with_start_and_stop_delay_gates.lotos;my_delay1.bcg = generation of my_delay1.lotos;…my_delayN.bcg = generation of my_delayN.lotos;

my_imc.bcg =( ( ( my_model_with_start_and_stop_delay_gates.bcg

|[DL1_START, DL1_STOP]| my_delay1.bcg ) |[...]| … ) |[DLN_START, DLN_STOP]| my_delayN.bcg

);

my_mc.bcg = stochastic reduction of hide …. in my_imc.bcg

%bcg_steady –sol my_mc.sol my_mc.bcg


More information at :http://www.inrialpes.fr/vasy/cadp/

free of charge for universities and public research centers

Toolbox written in C and available for:SolarisLinuxWindowsMacOS

Questions ?

Conclusion

leiden workshop 20/06/2007 0 presentation of the cadp toolbox cadp toolbox what is cadp ? lotos...

Documents

lotos language lotos

leiden workshop

lotos language slide

lotos gates

lotos models

pop smax

push time

lotos language tools