Performance Evaluation and Model Checking of Probabilistic Real-time Actors Ph.D. Dissertation

138 Pages·2016·5.75 MB·English

Performance Evaluation and Model Checking
of Probabilistic Real-time Actors
Ali Jafari
Doctor of Philosophy
April 2016
School of Computer Science
Reykjavík University
Ph.D. Dissertation
ii
Performance Evaluation and Model Checking of Probabilistic
Real-time Actors
by
Ali Jafari
Dissertation submitted to the School of Computer Science
at Reykjav(cid:237)k University in partial ful(cid:28)llment
of the requirements for the degree of
Doctor of Philosophy
April 2016
Thesis Committee:
Marjan Sirjani, Supervisor
Professor, Reykjav(cid:237)k University, Iceland
Holger Hermanns,
Professor, Saarland University, Germany
Carolyn Talcott,
Professor, SRI International, USA
Wan Fokkink, Examiner
Professor, VU University Amsterdam, Netherlands
Copyright
Ali Jafari
April 2016
iv

vi
The undersigned hereby grants permission to the Reykjav(cid:237)k University Library to
reproduce single copies of this Dissertation entitled Performance Evaluation and
Model Checking of Probabilistic Real-time Actors and to lend or sell such
copies for private, scholarly or scienti(cid:28)c research purposes only.
The author reserves all other publication and other rights in association with the
copyright in the Dissertation, and except as herein before provided, neither the Dis-
sertation nor any substantial portion thereof may be printed or otherwise reproduced
in any material form whatsoever without the author’s prior written permission.
date
Ali Jafari
Doctor of Philosophy
viii
Performance Evaluation and Model Checking of Probabilistic
Real-time Actors
Ali Jafari
April 2016
Abstract
This dissertation is composed of two parts. In the (cid:28)rst part, performance evalua-
tion and veri(cid:28)cation of safety properties are provided for real-time actors. Recently,
the actor-based language, Timed Rebeca, was introduced to model distributed and
asynchronous systems with timing constraints and message passing communication.
A toolset was developed for automated translation of Timed Rebeca models to Er-
lang [1]. The translated code can be executed using a timed extension of McErlang
for model checking and simulation. In the (cid:28)rst part of this dissertation, we induce a
new toolset that provides statistical model checking of Timed Rebeca models. Using
statistical model checking, we are now able to verify larger models against safety prop-
erties comparing to McErlang model checking. We examine the typical case studies
of elevators and ticket service to show the e(cid:30)ciency of statistical model checking and
applicability of our toolset.
In the second part of this dissertation, we enhance our modeling ability and cover
more properties by performance evaluation and model checking of probabilistic real-
time actors. Distributed systems exhibit probabilistic and nondeterministic behaviors
and may have time constraints. Probabilistic Timed Rebeca (PTRebeca) is introduced
as a timed and probabilistic actor-based language for modeling distributed real-time
systems with asynchronous message passing. The semantics of PTRebeca is a Timed
Markov Decision Process (TMDP). We provide SOS rules for PTRebeca, and develop
two toolsets for analyzing PTRebeca models. The (cid:28)rst toolset automatically gener-
ates a TMDP model from a PTRebeca model in the form of the input language of the
PRISM model checker. We use PRISM for performance analysis of PTRebeca models
against expected reachability and probabilistic reachability properties. Additionally,
we develop another toolset to automatically generate a Markov Automaton from a
PTRebeca model in the form of the input language of the Interactive Markov Chain
Analyzer (IMCA). The IMCA can be used as the back-end model checker for perfor-
mance analysis of PTRebeca models against expected reachability and probabilistic
reachability properties. We present the needed time for the analysis of di(cid:27)erent case
studies using PRISM-based and IMCA-based approaches. The IMCA-based approach
needs considerably less time, and so has the ability of analyzing signi(cid:28)cantly larger
models. We show the applicability of both approaches and the e(cid:30)ciency of our tools
by analyzing a few case studies and experimental results.
Mat Æ frammist(cid:246)(cid:240)u og athugun Æ l(cid:237)k(cid:246)num (cid:237) l(cid:237)kindafr(cid:230)(cid:240)ilegum
raunt(cid:237)ma leikurum
Ali Jafari
apr(cid:237)l 2016
(cid:218)tdrÆttur
(cid:222)essi ritger(cid:240) er tv(cid:237)skipt. ˝ fyrri hlutanum er fari(cid:240) (cid:237) mat og sannpr(cid:243)fun Æ eiginleikum
(cid:246)ryggis (cid:237) raunt(cid:237)mal(cid:237)k(cid:246)num. Fyrir stuttu s(cid:237)(cid:240)an var leikendabygg(cid:240)a mÆli(cid:240), Timed Re-
beca, nota(cid:240) vi(cid:240) l(cid:237)kana drei(cid:28)ngu og (cid:243)samstillt ker(cid:28) me(cid:240) t(cid:237)mastillingu og samskipti (cid:237)
skilabo(cid:240)um. Bœi(cid:240) var til verkf(cid:230)rasett fyrir sjÆlfvirka (cid:254)(cid:253)(cid:240)ingu Æ Timed Rebeca l(cid:237)k(cid:246)n
y(cid:28)r (cid:237) Erlang. H(cid:230)gt er a(cid:240) nota (cid:254)(cid:253)dda k(cid:243)(cid:240)ann me(cid:240) (cid:254)v(cid:237) a(cid:240) nota t(cid:237)mastillta framlengingu
af McErlang fyrir l(cid:237)kanapr(cid:243)fun og hermun. ˝ fyrri hluta (cid:254)essarar ritger(cid:240)ar, (cid:230)tlum vi(cid:240)
a(cid:240) kynna verkf(cid:230)rasetti(cid:240) sem veitir t(cid:246)lfr(cid:230)(cid:240)ilega pr(cid:243)fun Æ l(cid:237)k(cid:246)n Æ Timed Rebeca l(cid:237)k(cid:246)n.
Me(cid:240) (cid:254)v(cid:237) a(cid:240) nota t(cid:246)lfr(cid:230)(cid:240)ileg pr(cid:243)f Æ l(cid:237)k(cid:246)n er nœna h(cid:230)gt a(cid:240) sannreyna st(cid:230)rri l(cid:237)k(cid:246)n eins
og (cid:237) (cid:246)ryggiskr(cid:246)fum McErlang. Vi(cid:240) ranns(cid:246)kum d(cid:230)miger(cid:240)ar ferilsathuganir af lyftum og
mi(cid:240)as(cid:246)lu til a(cid:240) s(cid:253)na fram Æ skilvirkni t(cid:246)lfr(cid:230)(cid:240)ilegra l(cid:237)kana og beitingu verkf(cid:230)rasettsins
okkar.
˝ seinni hluta (cid:254)essarar ritger(cid:240)ar aukum vi(cid:240) vi(cid:240) getu l(cid:237)kanager(cid:240)arinnar og vi(cid:240) nÆum
y(cid:28)r (cid:29)eiri eiginleika me(cid:240) mati Æ framkv(cid:230)md og pr(cid:243)funum Æ l(cid:237)k(cid:246)num Æ l(cid:237)kinda raunt(cid:237)ma
leikara. Dreif(cid:240) ker(cid:28) s(cid:253)na l(cid:237)kindi og brig(cid:240)genga heg(cid:240)un sem kunna a(cid:240) hafa t(cid:237)mam(cid:246)rk.
Probabilistic Timed Rebeca (PTRebeca) er kynnt sem t(cid:237)mastillt og l(cid:237)kinda leikara-
byggt mÆl l(cid:237)kindadreif(cid:240)ra raunt(cid:237)makerfa me(cid:240) (cid:243)samstillta sendingu skilabo(cid:240)a. Merk-
ingarfr(cid:230)(cid:240)i PTRebeca er Timed Markov Decision Process (TMDP). Vi(cid:240) ver(cid:240)um me(cid:240)
SOS reglur fyrir PTRebeca, og (cid:254)r(cid:243)um tv(cid:246) verkf(cid:230)rasett til a(cid:240) greina PTRebeca l(cid:237)k(cid:246)n.
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