In this short paper we briefly describe a tool which is based on a Markovian stochastic process algebra. The tool offers both model specification and quantitative model analysis in a compositional fashion, wrapped in a userfriendly graphical front-end

Hermanns, H.

Mertsiotakis, V.

Siegle, M.

English

University of Twente Research Information

TIPPtool: Compositional Specification and Analysis ofMarkovian Performance ModelsH. Hermanns?, V. Mertsiotakis??, and M. SiegleInformatik 7, Universität Erlangen-N¨urnberg, Erlangen, GermanyAbstract. In this short paper we briefly describe a tool which is based on aMarkovian stochastic process algebra. The tool offers both model specificationand quantitative model analysis in a compositional fashion, wrapped in a user-friendly graphical front-end.1 Compositional Performance ModellingClassical process algebras have been designed as compositional description formalismsfor concurrent systems. Instochasticprocess algebras temporal information is attachedto actions in the form of continuous random variables representing activity durations,making it possible to specify and analyse both qualitative and quantitative properties.This short paper is about theTIPPtool [5], a tool that emerged from theTIPP project whichfocussed on a basic framework supporting both functional specification and performanceevaluation in a single, process algebraic formalism [6]. The formalism is basically asuperset of LOTOS [1], including means to specify exponentially distributed delays. Ithence provides a bridge between qualitative and quantitative evaluation, the latter basedon Markov chain analysis. More precisely, the underlying semantics of the specificationlanguage gives rise to homogeneous continuous time (semi-)Markov chains that canbe analysed numerically by means of efficient techniques. Besides some support foranalysis of functional aspects, the tool offers algorithms for numerical performanceanalysis of a given process algebraic specification. Exact and approximate evaluationtechniques are provided to calculate various measures of interest. The tool also offerssemi-automatic compositional minimisation of complex models based on equivalence-preserving transformations.2 Model Specification and AnalysisThe specification language of the TIPPtool is a superset of LOTOS1. In particular,a distinguished type of prefix,(a, r); P , is supported, denoting that actiona oc-curs after a delay∆ which is exponentially distributed with rate parameterr (i.e.Prob(∆ ≤ t) = 1 − e−rt); afterwards the process behaves asP .Actions arising from ordinary prefixa;P are calledimmediateactions. They happenas soon as possible if not prevented by the environment, following themaximal progressassumption. In particular, internal (or hidden) immediate actions are assumed to happen? Current affiliation:Systems Validation Centre, University of Twente, Enschede, The Nether-lands?? Current affiliation:Development Access, Lucent Technologies, Nürnberg, Germany1 Data types are treated more liberally than in standard LOTOS; integers are a built-in data type.N. Halbwachs and D. Peled (Eds.): CAV’99, LNCS 1633, pp. 487–490, 1999.c© Springer-Verlag Berlin Heidelberg 1999488 H. Hermanns, V. Mertsiotakis, and M. Siegleimmediately when enabled. In addition to the basic language elements,process instan-tiation, parametric processesand inter-process communicationcan be used to modelcomplex dependences, such as value passing or mobility.Conservatively extending classical process algebras, a labelled transition system(LTS) is generated from the system specification using structural operational rules [6].Corresponding to timed and immediate actions there are two types of transitions betweenstates: timed transitions and immediate transitions. The LTS can hence be regardedas a semi-Markov process. Under certain conditions (checked by the tool) the semi-Markov process can be transformed into a continuous time Markov chain. Verifyingthese properties involves equivalence preserving transformations, based on a stochasticvariant of Milner’s observational congruence [6]. Since this relation is compositional,it can be applied to minimise the state space of a specification in a componentwisefashion. This minimisation abstracts from internal immediate steps and it aggregates theMarkov chain based on the concept of lumpability [10], while preserving functional andstochastic information. For a particular Markov chain, a system of ordinary differentialequations needs to be solved in order to obtain the state probabilities at a particulartime instantt (transient analysis). Alternatively, solving a linear system of equationsleads to the state probabilities in the equilibrium (stationary analysis). These limitingprobabilities (wheret → ∞) are known to exist for arbitrary finite (homogeneous,continuous time) Markov chains.3 Tool Features and StructureIn its current version 2.3, the TIPPtool provides the following functionality:– Model description by means of a LOTOS-based notation,– Reachability analysis based on the operational semantics,– Algorithms for deadlock detection and tracing to a given state,– Algorithms for checking bisimulation-style equivalences and for the minimisationof (sub-)models,– Stationary and transient analysis of the underlying Markov chain,– Functions for the calculation of performance and dependability measures,– Support of experiment series,– Output of numerical results using the toolpxgraph,– Interfacing with other tools.The tool consists of several components whose in-teraction is shown in the figure on the right. Speci-fications can be created with an editor (Edit com-ponent). TheGenerate/Aggregate component isresponsible for parsing the specification, for thegeneration of the LTS and for its minimisation ac-cording to an equivalence notion. The user maycurrently choose between four (stochastic variantsState listTransition listTOPO PXGRAPHCADP PEPPGenerateAggregate Export AnalyseSpecificationExperimentMeasuresEdit OptionsTIPPtoolGUIof) classical congruences. This minimisation is known to be particularly beneficial if itis applied to components of a larger specification in a stepwise, compositional fashion.In the TIPPtool, semi-automatic compositional minimisation is supported in an elegantway: By highlighting a certain fragment of the specification with the mouse, it is possibleTIPPtool 489to invoke compositional minimisation of that fragment. When the minimised represen-tation is computed, a new specification is generated automatically, where the selectedfragment has been replaced by its minimised representation.Via the Options, the user can specify various measures to be calculated, such asthe probability of the system being in a certain subset of states, or the throughput (i.e.the mean frequency of occurrence) of some action. An experiment description containsinformation about model parameters to be varied during analysis.A series of experimentscan be carried out automatically in an efficient manner, generating numerical results fordifferent values of a cer-tain model parameter, whilethe state space only needsto be generated once. Mo-dels can be analysed withthe Analyse module. Thismodule offers various nu-merical solution algorithmsfor the underlying stochasticprocess, among them two ap-proximate methods [9,12].After an experiment serieshas been carried out, the re-sults are presented graphi-cally with the toolpxgraphfrom UC Berkeley, cf. thescreenshot on the right. TheExport module of the tool provides interfaces to three other tools,pepp [4], topo [11],andcadp [2]. The former interface generates stochastic task graphs [8], for which thetool pepp offers a wide range of both exact and approximate analysis algorithms, some ofwhich work even for general distributions. The second interface provides support for thetranslation of specifications into a format suitable for the LOTOS toolt po.Among otherfunctionalities,topo is capable of building C-programs from LOTOS specifications. Thethird interface can be used to exploit the bisimulation equivalence algorithms of the toolaldebaran, as well as other tools (fc2, autograph), for visualisation or functionalverification purposes. Here, the interface is on the level of the state space.We used the programming languageStandard ML for implementing the parser, thesemantics, the bisimulation algorithms and for the approximate Markov chain solutionmethods. The numerical analysis part is written in C, on top of a library which providesdata structures for sparse matrices (SparseLib1.3 from Kenneth Kundert, UC Berkeley).This library has been extended by iterative solution methods for stationary and transientanalysis. The clear interface of the library makes it easy to integrate other solutionmethods into the tool. The communication with the state space generator is done viaASCII-files. For computing the measures, shell-scripts are used, which are based onstandard UNIX-tools such asgrep,awk andsed. Finally, the graphical user interface hasbeen implemented using the scripting languageTcl/Tk. The communication betweenthe GUI and the other tools is done via UNIX-pipes.490 H. Hermanns, V. Mertsiotakis, and M. Siegle4 ConclusionIn this short paper, we have presented the status quo of the TIPPtool. We have describedthe particular features of a stochastic process algebra based specification formalism, to-gether with the distinguishing components of the tool. To the best of our knowledge, theTIPPtool is the only existing tool offering compositional minimisation of Markov chainmodels. TIPPtool is available free of charge for non-commercial institutions, more de-tails can be found athttp://www7.informatik.uni-erlangen.de/tipp/. Amongothers, the tool has been applied to the study of performance and dependability aspects ofthe plain old telephony system [7], a robot control system [3], and a hospital informationsystem [13]. So far, models with up to107 states have been tackled compositionally.References1. T. Bolognesi and E. Brinksma. Introduction to the ISO Specification Language LOTOS. InThe Formal Description Technique LOTOS, pages 23–73, Amsterdam, 1989. North-Holland.2. Jean-Claude Fernandez, Hubert Garavel, Alain Kerbrat, Radu Mateescu, Laurent Mounier,and Mihaela Sighireanu. Cadp (cæsar/aldebaran development package):A protocol validationand verification toolbox. InProceedings of the 8th Conference on Computer-AidedVerification(CAV 96), Springer LNCS 1102:437–440. August 1996.3. S. Gilmore, J. Hillston, R. Holton, and M. Rettelbach. Specifications in Stochastic Pro-cess Algebra for a Robot Control Problem.International Journal of Production Research34(4):1065–1080, 1996.4. F. Hartleb and A. Quick. Performance Evaluation of Parallel Programms — Modeling andMonitoring with the Tool PEPP. InProceedings of 7th GI-ITG Fachtagung ”Messung, Model-lierung und Bewertung von Rechen- und Kommunikationssystemen“, pages 51–63. InformatikAktuell, Springer. September 1993.5. H. Hermanns, U. Herzog, U. Klehmet, V. Mertsiotakis, and M. Siegle. Compositional Per-formance Modelling with the TIPPtool. In10th International Conference on ModellingTechniques and Tools for Computer Performance Evaluation (TOOLS 98), Springer LNCS1469:51–62. September 1998.6. H. Hermanns, U. Herzog, andV. Mertsiotakis. Stochastic ProcessAlgebras – Between LOTOSand Markov Chains.Computer Networks and ISDN Systems, 30(9-10):901–924, 1998.7. H. Hermanns and J.P. Katoen. Automated Compositional Markov Chain Generation for aPlain Old Telephony System.Science of Computer Programming. to appear.8. U. Herzog. A Concept for Graph-Based Stochastic Process Algebras, Generally DistributedActivity Times and Hierarchical Modelling. InProc. of the 4th Workshop on Process Algebrasand Performance Modelling, pages 1–20. Universit´a di Torino, CLUT, 1996.9. J. Hillston and V. Mertsiotakis. A Simple Time Scale Decomposition Technique for SPAs. InThe Computer Journal, 38(7):566–577, 1995.10. J.G. Kemeny and J.L. Snell.Finite Markov Chains. Springer, 1976.11. J.A. Manas,T. de Miguel, and J. Salvachua. Tool Support to Implement LOTOS Specifications.Computer Networks and ISDN Systems, 25(7):815–839, 1993.12. V. Mertsiotakis and M. Silva. Throughput Approximation of Decision Free Processes UsingDecomposition. InProc. of the 7th Int. Workshop on Petri Nets and Performance Models,pages 174–182, St. Malo, June 1997. IEEE CS-Press.13. M. Siegle, B. Wentz, A. Klingler, and M. Simon. Neue Ans¨atze zur Planung von Klinikkom-munikationssystemen mittels stochastischer Leistungsmodellierung. In42 Jahrestagung derDeutschen Gesellschaft f¨ur Medizinische Informatik, Biometrie und Epidemiologie (GMDS),pages 188 – 192, Ulm, September 1997. MMV Medien & Medizin Verlag.

TIPPtool: Compositional Specification and Analysis of Markovian Performance Models

A Concept for Graph-Based Stochastic Process Algebras, Generally Distributed ActivityTimesandHierarchicalModelling. InProc.ofthe4thWorkshoponProcessAlgebras and Performance Modelling, pages 1–20. Universit´ a di

A Simple Time Scale Decomposition Technique for SPAs.

Automated Compositional Markov Chain Generation for a Plain Old Telephony System. Science of Computer Programming.

Compositional Performance Modelling with the TIPPtool.

Finite Markov Chains.

Hubert Garavel, Alain Kerbrat, Radu Mateescu, Laurent Mounier, andMihaelaSighireanu.

Introduction to the ISO Speciﬁcation Language LOTOS.

NeueAns¨ atze zur Planung von Klinikkommunikationssystemen mittels stochastischer Leistungsmodellierung.

Performance Evaluation of Parallel Programms — Modeling and MonitoringwiththeToolPEPP. InProceedingsof7thGI-ITGFachtagung”Messung,ModellierungundBewertungvonRechen-undKommunikationssystemen“,pages51–63.Informatik Aktuell,

Speciﬁcations in Stochastic Process Algebra for a Robot Control Problem.

ThroughputApproximation of Decision Free Processes Using Decomposition.

http://purl.utwente.nl/publications/66281

TIPPtool: Compositional Specification and Analysis of Markovian Performance Models

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