- Email: [email protected]
Use of models and model experiments solving problems in informatics
USE OF MODELS AND MODEI., EXPEXIMENTS SOWING Gunter
PROBLEMS IN INFCRMATICS
Schwarze11
1. Prellmlnary remarks Yodelling and simulation is more and more an important methodology for the design and development of complex hierarchical systems in informatics and together tith the methodology several tools like special languages ox modelling and simulation systems have been developed, There ls a wide range of application for the design and development of hardware and/or software devices like computer systems or software systems showing also the indispensable use of it. Following this trend (eee also /l-4/) and analyzing the papers presented to this conference "Systems analysis, Modelling, and Simulation 85" established a section Wodelling and simulation of complex systems in informatics".
2. Methodology The characteristic feature of the methodology is the two phase problem solving process with the phases modelling the real problem by describing a model problem 8nd solving the described model problem by model experiments. The first phases include problem analysis and systems analysis as well 8s model validation and planning of model experiments. The second phases include the method(s) for model computation, experimental problem solving as well as ev8luation ox estimation of experiment results and integrating of human intelligence and creativeness in this problem solving process. Today somputer @.ded problem solving CAPS - using special tools like simulation systems, problem solving ayeterns, model and method base systems or calculation systems as well as special languages or parameter controlled programs are a common practice. Detailed general descriptions see /l-IO/. The model problem description after the first phase may describe a model like linked 3 submodele resp. components: - system model, which represents the essential beheviour of the real system marked off by system analysis, - input model or work load model, which represents the env$ronment influence ox the work load of the real system, - evaluation model, which determinates the model evaluation quantities using information of the system m#el or the input/work load model. '&mboldt-UniversiMt zu Berlin, Sektion Mathematik, Bereich Informationsverarbeitung, 1086 Berlin, PF 1297
125
We describe a model like a parametrization of a parametric model class. ILode modification (used for model experiments) may be realized by a new parametrization of the considered model class or exchange of models or components belonging to other model classes.
It is our aim to distinguish between model problem description and model problem solution and to formulate the model problem on a conceptual level. That means: - description like a mathematical model (in a wide sense), - problem solving without transformation in an other corresponding mathematical problem may be possible, - no loading with aspects of modelling software or methods for proble:rl salving;. The conceptual model may be described by mathematical means only or by use of a technical language. In the last case we demand that the description in the technical language implicate automatic a mathematical problem description. Essential examples of mathematical means in our sense are: - mathematical equations, - abstract automatons and linked abstract automatons, - aggregative systems, - nets like Petri nets, queuing neta and their extensions or modifications, - graphs. Special in the case of discrete models we don't have a general description base and Kalshnikov said during a Round Table (see /II, p. 242/l "Let us consider discrete simulation models as a mathematical object". The mentioned aggregative systems /12/ are a step in this direction. Furthermore we demand the separate development and (in the second phase) use of input/work load model, system model, evaluation model, and submodels or components of the system model. The model development may be bottom up or top down like use in modern software technology /13, 14/. Technical languages offer predefined parametric submodels and the user has to parametrisize selected submodel classes and to link by link rules. The basic notion of the second phase is method. We distinguish between two separate classes of methods: - CALCULhTION, that means immediate determination of the evaluation quantities direct from parameters of the input/work load model or system model. - SIMULATION, that means immediate determination of the time behaviour of the system model and use of information direct from the processes taken place in the system model or input model to determine the connection between the parameters of input or system model and the evaluation quantities. Sometimes the problem solving process may be divided into steps and for
one step we use S~~~TIO~
and fox an other step ~AL~U~TIO~*
case we use the designation HYBRID method like /15/.
There
is
In this a wide
hybrid range between simulation and calculation (see F. Burghardt in this section /I&'), In our sense hybrid is not
an attribut of model but
of model problem solving. Further methods belong to the realization of planned experiments, evaluation or estimation of experiment results up to report generation (see /5, 6, 17, 18/). That means we use method in the sense of method base systems. Models in informatics belong to problem descriptions connected with - user software (application software), - software neighbouxing hardware (operating systems for instance), computer installation, - hardware. Today we demand the use of models not only before or during the design of software but also during the implementation, that means the target larguage of the program representing the model must agree with the implementation language. In this case we use systems with components being models and others being originals. These systems are models in an extended sense (see Griitzner /19/). Generally speaking methodology means modelling In a wide sense including model problem description, for instance study of special model behaviour or solving nearly an optimization problem including even a sensitivity study, and discovery a model problem solution (if solvable) with sufficient or adequate accuracy using model experiments in a wide sense. An effective problem solution calls for human intelligence and proper tools for experimental computer aided problem solving.
3. Tools and applications Referring to the aim of this conference we get out the user's point of view. Tools have to help the problem solving, That means we need languages assisting the user or user oriented software systems Oriented to problem classes being typical of informatics. We have to take into account distributed problem solving systems marked by software modules being ~plemented on different computer systems f5l. The multi microprocessors allow the development of new simulators (special computexs) as an aid for problem solving for a wide range of model problem classes, fox instance stochastic discret event models /20/.
High level program languages lilreILu)OL,PORTRAN and PI.& or more modern one like SIMUIA, PASCAL, MODlILA2 are in u8e for writing special parametric programs belonging to one special parametric class of model problems or development of CAPS-software systems like modelling and simulation systems respectively program systems basing on analytical models. In special situations only the user may decide to write an own simulation program using only a high level programming language. For this has to be also a programmer for this language. In both situations the question arise: "Which language gives help for modelling and experimentation with models?" Only SIMULA - referring to the mentioned languages - gives good help and therefore we used in our simulation group SIMULA as a target language and developed also a programming technology for this purpose /I/. There exist extensions of programming languages like SIMPL/l, SIMPASCAL, A68SIM /22/ being a help for modelling and experimentation with discrete models. Most of the extension have been built by adding software modules which imitate essential aspects of SIMULA. PASCAL and MODULA 2 are languages used in informatics special for software being near the hardware. They are also available for small computers. For that colleagues in our group are working in extension of MODULE 2 using the modern concept of this language and also our experiences with SIMUIA. I refer to the contribution /23/ in this section. Special languages have been developed for special informatical problems but not related to a complete CAPS-system. Concerning this matter we refer to CDJJda computer design language for multilevel simulation with the structure description level, behaviour description level and the specification level (see /24/j.
3.2. Software systems Software systems assist the user on very different model description levels for more or less well defined model probelm classes. An extension of the usefulness relative to model classes, methods or linkage with additional software may be realized by use of the target language of the system. We can find software systems only with the method simulation or only with the method calculation but also with hybrid methods. On the other side may exist an exact defined language but also only rules for use of the program package.
Selected software systems useful for study of the behaviour or evaluation of computer systems, computer installations or program development together with the type of methods, the target or internal used language and corresponding literature are tabled in the following: name
tYP
SIMULATION CALCULATION MAOS SIMULATION CALCULATION MARS SIMULATION (CALCULATION) MS&SIM SIMULATION OASIS SIMULATION SIBUR SIMULATION TBRMAL! SIMULATION CALCULATION TOCS SIMULATION PS Computer system simulation SIMULATION PS Calculation of queuing CALCULATION networks BORIS BNBT
language
literature
PASCAL PL/l SIMULA-extension
/13,14/ /25,26/ /15/
PASCAL
/19/
FORTRAN SIMULA SINULA SIMULA
/27/ /28/ /29/ /5,30/
SIMULA
/31/
MODULA-2
/32/
SIMULA
/33/
Extended or modified Petri-networks are the base for model problem description in several software systems. Used selected extended or modified Petri-networks are Place/Transactor-Nets /34/, buffer nets (B-Nets) (SIBUN /29/j, generalized nets /35/, evaluation-net-oriented system PORCASD /36/ and M-nets /19/. There exist other using great software systems with high level user assistance. One representative is the block-oriented interactive simulation system for dicrete systems BORIS /13, 14/. This system assists modelling (system level) and simulation (observation level). Concepts of date, model and method as well as information systems are integrated. The model components are agencies and models are nets of agencies. BORIS may be used for program or computing system development. The system TBRMAL /30/ is an example for an "open distributes experimental useful computer aided problem solving system" and shall be presented in this section.
129
3.3.
Simulatora
Simulators are apeoial devices only for simulation.The developmentof multi microprocessors is also a natural base for the development of discrete event-oriented simulators like special computers only for this aim. For instance on the base of queuing network models they may be used also for problems belonging to infozmatics. In /20/ the development and use of the stochastic discret event simulatorDESC is described. DESC has been used for the simulation of the performance of DESC using a queuing network model. The result are suggestions for performance improvement. If we compare the use of DESC with the use of a universal computer with a discrete simulation system solving one simulation problem, DESC needs very lower costs.
4. Terminating remarks The further development of experimental CAPS-systems has to take into account that more and more essential and realizable parts of human intelligence - artificial intelligence - may be introduced these systems and also fully exhausting the possibilities of parallel computers. Other important aspects are backtracking, formula manipulator, expert systems and fuzzy systems. See also /l-4/. The results of to-day shall be now presented by the following speakers.
5. Literatur
/I/ /2/
/3/ /4/
/5/
/6/ /7/ /8/ /9/
Goller, P. (Ed.): Simulationstechnik. 1. Symposium Simulationstechnik Erlangen, 1982, Proceedings. Springer-Verla Berlin - Heidelberg - New York 1982 (Informatik-Fachberichte 5% j Ameling, W. (Ed.): First European Simulation Congress ESC83. Aachen 1983, Proceedi s. Springer-Verlag Berlin - Heidelberg New York - Tokyo 1983"iInformatik-Wchberichte 71) J&vor, A. (Ed.): Simulation in Research and Development. IhUCS European Simulation Meeting, Eger 1984 (Preprints) Breitenecker, F.; Kleibsrt, Vi. (Eds.): Simulationstechnik. 2, Sym+xx&.~ Simulationstechnik, lien 1984 Proceedings. Springer-Verlag, - Heidelberg - Rew York 1984 (Informatik-Fachberichte 85) Carl. D.: Schwarze. G.: Exuerimental Use of Models Connected with Computer.Aided Problem Sol&n(: - Problems Connected with Informatics. In /3/ pp. 365-374 Schwarze, G.: Systemsimulation. In Handbuch der Systemtheorie (Hrsg.: Wunsch, G.). Akademie-Verlag 1985 Vansteenkiste, G.C.: Simulation Methodology for Improved Process Interaction. In /2/ pp. 32-43 Sol, H.G.: Simulation Based Inquiry Systems. In /2/ pp. 58-62 Schone, A.: Simulation Techniques and Design in Engineering Foundations, Nethods and Possibilities. In /2/ pp. 77-81
/IO/ /II/
/12/ /13/ /14/
/15/
/16/ /17/
/18/
/19/
/20/
/21/
/22/ /23/
/24/
Molti, I.: Some Problems in Rese&h of General Simulation Systems. In /2/ pp. 88-92 JBvor, J. (Ed.): Proceedings of the IMACS Simulation Meeting on Discrete Simulation and Releated Fields. North-Holland Publishing Company 1982 Kalashnikov, V.V.; Nemchiniv, B.V.: The Organization of the Research Process by Aggregative Simulation Systems. Syt. Anal. Model. Simul. l(1984) 2, 101-111 Maierhofer, J.; Schmitt, H.; Trosch, S.: Software Simulation with BORIS. In /2/ pp. 239-244 Decker, H.; Zobel, A.: Simulation and Verification - a Combined Approach to the Development of Software Systems. In /3/ pp. 135-149 Jobmann, M.: ILMAOS - eine Sprache zur Formulierung von Rechnersystemen. Univ. Hamburg, Fachbereich Inforrnatik,Bericht IFI-HHB-91/82, Hamburg 1982 Burghardt, K.: Hybrid Simulation of Queueing Networks. In this proceedings. Carl, D.: Eine Methodik zur Leistungsanalyse komplexer Rechnersysteme. Akad. d, Wiss. d. DDR, ZfR-Information-82.11. Berlin 1982 Carl, D.: RechnergestiitzteVerhaltensmodellierung und -bewertung komplexer Rechnersysteme. Akad. d. Wiss. d. DDR, ZfR-Information84.03, Berlin 1984 Griitzner,R.: Konzeptionelle Grundlagen der Modellierung und Simulation von Software auf der Basis modifizierter Petri-Netze. Akad. d. Wiss. d. DDR, ZfR-Information-84.06, Berlin 1984 Barel, M.: Ein Multimikroprozessorsystem fur die stochastische ereignisorientierte Simulation. Diss. TH Aachen, Fak. f. Elektrotechnik, Aachen 1983 Fischer, J.: Modellierung und Simulation paralleler diskreter, kontinuierlicher und kombinierter Prozesse in SIMULA. Akad. d. Wiss. d. DDR, ZfR-Informationen-82.20, Berlin 1982 Buschmann, H.: A68SIM - Discrete Event Simulation in ALGOL 68. In /2/ pp. 301-306 Ahrens, K.; Burghardt, F.; Fischer, J.: Beitrage zur Modellierung und Simulation in der Informatik. Humboldt-Univ. z. Berlin, Sekt. Math. Seminarbericht (in print) Hahn, W.: Computer Design Language - Version Munich (CDLM) A Multi-Level Simulation Tool. In /2/ pp. 207-212
/25/ Irmscher, K.: Programmsystem BNET (Bedienungsnetze der TU Dresden) - Analyseverfahren geschlossener Bedienungsnetze zur Leistungsbewertung von Rechnersystemen. TU-Informationen, Preprint 08-01-84, TU Dresden, Sektion Informationsveru.r~beFtlung, Dresden 1984 /26/ Bergholz, G.; Irmscher, K.: Perfomance Modelling of Computer Systems. In this proceedings /27/ Schmidt, B.: Rechnermodelle - die Simulation von Rechenanlagen mit GPSS-FORTRAN. R. Oldenbourg Verlag, Miinchen- Wien 1978 /28/ Unger, B.W.: OASIS 3.0 Reference Manual. Univ. Calgary, 1980 /29/ Bladko, R.: Simulation of Parallel Asynchronous Systems Described by B-Nets. In /3/ pp. 59-68 /30/ Carl, D.: Software Tool for Computer System Simulation. In this proceedings /31/ Muhlenbein, H.: TOCS - Ein Programmsystem zur Simulation von Rechensystemen. GMB Bonn, Jahresbericht 1980/81 K.: Computer System Simulation with MODlILA-2.Humboldt1321 E;ns, , zu Berlin, Sekt. Math. preprint 1985 (in print)
131
/33/ Burghardt,I?.:Ein Programmeystemzur Untersuchungvon Bedienunganetzwerkenin SIMUL4. Humboldt-UniversitIt zu Berlin, Sekt. Math. preprint in working out, Berlin 1985 In /3/ /34/ Fuss, IL: ImprovingSimulationswith Place/Transactor-Nets. pp* 57-58 /35/ Dahmen, N.: FORCASD - an EvaluationNet Oriented Program System for Modelling and Simulation.In /2/ pp. 267-272
132
PROBLEMS IN INFCRMATICS
Schwarze11
1. Prellmlnary remarks Yodelling and simulation is more and more an important methodology for the design and development of complex hierarchical systems in informatics and together tith the methodology several tools like special languages ox modelling and simulation systems have been developed, There ls a wide range of application for the design and development of hardware and/or software devices like computer systems or software systems showing also the indispensable use of it. Following this trend (eee also /l-4/) and analyzing the papers presented to this conference "Systems analysis, Modelling, and Simulation 85" established a section Wodelling and simulation of complex systems in informatics".
2. Methodology The characteristic feature of the methodology is the two phase problem solving process with the phases modelling the real problem by describing a model problem 8nd solving the described model problem by model experiments. The first phases include problem analysis and systems analysis as well 8s model validation and planning of model experiments. The second phases include the method(s) for model computation, experimental problem solving as well as ev8luation ox estimation of experiment results and integrating of human intelligence and creativeness in this problem solving process. Today somputer @.ded problem solving CAPS - using special tools like simulation systems, problem solving ayeterns, model and method base systems or calculation systems as well as special languages or parameter controlled programs are a common practice. Detailed general descriptions see /l-IO/. The model problem description after the first phase may describe a model like linked 3 submodele resp. components: - system model, which represents the essential beheviour of the real system marked off by system analysis, - input model or work load model, which represents the env$ronment influence ox the work load of the real system, - evaluation model, which determinates the model evaluation quantities using information of the system m#el or the input/work load model. '&mboldt-UniversiMt zu Berlin, Sektion Mathematik, Bereich Informationsverarbeitung, 1086 Berlin, PF 1297
125
We describe a model like a parametrization of a parametric model class. ILode modification (used for model experiments) may be realized by a new parametrization of the considered model class or exchange of models or components belonging to other model classes.
It is our aim to distinguish between model problem description and model problem solution and to formulate the model problem on a conceptual level. That means: - description like a mathematical model (in a wide sense), - problem solving without transformation in an other corresponding mathematical problem may be possible, - no loading with aspects of modelling software or methods for proble:rl salving;. The conceptual model may be described by mathematical means only or by use of a technical language. In the last case we demand that the description in the technical language implicate automatic a mathematical problem description. Essential examples of mathematical means in our sense are: - mathematical equations, - abstract automatons and linked abstract automatons, - aggregative systems, - nets like Petri nets, queuing neta and their extensions or modifications, - graphs. Special in the case of discrete models we don't have a general description base and Kalshnikov said during a Round Table (see /II, p. 242/l "Let us consider discrete simulation models as a mathematical object". The mentioned aggregative systems /12/ are a step in this direction. Furthermore we demand the separate development and (in the second phase) use of input/work load model, system model, evaluation model, and submodels or components of the system model. The model development may be bottom up or top down like use in modern software technology /13, 14/. Technical languages offer predefined parametric submodels and the user has to parametrisize selected submodel classes and to link by link rules. The basic notion of the second phase is method. We distinguish between two separate classes of methods: - CALCULhTION, that means immediate determination of the evaluation quantities direct from parameters of the input/work load model or system model. - SIMULATION, that means immediate determination of the time behaviour of the system model and use of information direct from the processes taken place in the system model or input model to determine the connection between the parameters of input or system model and the evaluation quantities. Sometimes the problem solving process may be divided into steps and for
one step we use S~~~TIO~
and fox an other step ~AL~U~TIO~*
case we use the designation HYBRID method like /15/.
There
is
In this a wide
hybrid range between simulation and calculation (see F. Burghardt in this section /I&'), In our sense hybrid is not
an attribut of model but
of model problem solving. Further methods belong to the realization of planned experiments, evaluation or estimation of experiment results up to report generation (see /5, 6, 17, 18/). That means we use method in the sense of method base systems. Models in informatics belong to problem descriptions connected with - user software (application software), - software neighbouxing hardware (operating systems for instance), computer installation, - hardware. Today we demand the use of models not only before or during the design of software but also during the implementation, that means the target larguage of the program representing the model must agree with the implementation language. In this case we use systems with components being models and others being originals. These systems are models in an extended sense (see Griitzner /19/). Generally speaking methodology means modelling In a wide sense including model problem description, for instance study of special model behaviour or solving nearly an optimization problem including even a sensitivity study, and discovery a model problem solution (if solvable) with sufficient or adequate accuracy using model experiments in a wide sense. An effective problem solution calls for human intelligence and proper tools for experimental computer aided problem solving.
3. Tools and applications Referring to the aim of this conference we get out the user's point of view. Tools have to help the problem solving, That means we need languages assisting the user or user oriented software systems Oriented to problem classes being typical of informatics. We have to take into account distributed problem solving systems marked by software modules being ~plemented on different computer systems f5l. The multi microprocessors allow the development of new simulators (special computexs) as an aid for problem solving for a wide range of model problem classes, fox instance stochastic discret event models /20/.
High level program languages lilreILu)OL,PORTRAN and PI.& or more modern one like SIMUIA, PASCAL, MODlILA2 are in u8e for writing special parametric programs belonging to one special parametric class of model problems or development of CAPS-software systems like modelling and simulation systems respectively program systems basing on analytical models. In special situations only the user may decide to write an own simulation program using only a high level programming language. For this has to be also a programmer for this language. In both situations the question arise: "Which language gives help for modelling and experimentation with models?" Only SIMULA - referring to the mentioned languages - gives good help and therefore we used in our simulation group SIMULA as a target language and developed also a programming technology for this purpose /I/. There exist extensions of programming languages like SIMPL/l, SIMPASCAL, A68SIM /22/ being a help for modelling and experimentation with discrete models. Most of the extension have been built by adding software modules which imitate essential aspects of SIMULA. PASCAL and MODULA 2 are languages used in informatics special for software being near the hardware. They are also available for small computers. For that colleagues in our group are working in extension of MODULE 2 using the modern concept of this language and also our experiences with SIMUIA. I refer to the contribution /23/ in this section. Special languages have been developed for special informatical problems but not related to a complete CAPS-system. Concerning this matter we refer to CDJJda computer design language for multilevel simulation with the structure description level, behaviour description level and the specification level (see /24/j.
3.2. Software systems Software systems assist the user on very different model description levels for more or less well defined model probelm classes. An extension of the usefulness relative to model classes, methods or linkage with additional software may be realized by use of the target language of the system. We can find software systems only with the method simulation or only with the method calculation but also with hybrid methods. On the other side may exist an exact defined language but also only rules for use of the program package.
Selected software systems useful for study of the behaviour or evaluation of computer systems, computer installations or program development together with the type of methods, the target or internal used language and corresponding literature are tabled in the following: name
tYP
SIMULATION CALCULATION MAOS SIMULATION CALCULATION MARS SIMULATION (CALCULATION) MS&SIM SIMULATION OASIS SIMULATION SIBUR SIMULATION TBRMAL! SIMULATION CALCULATION TOCS SIMULATION PS Computer system simulation SIMULATION PS Calculation of queuing CALCULATION networks BORIS BNBT
language
literature
PASCAL PL/l SIMULA-extension
/13,14/ /25,26/ /15/
PASCAL
/19/
FORTRAN SIMULA SINULA SIMULA
/27/ /28/ /29/ /5,30/
SIMULA
/31/
MODULA-2
/32/
SIMULA
/33/
Extended or modified Petri-networks are the base for model problem description in several software systems. Used selected extended or modified Petri-networks are Place/Transactor-Nets /34/, buffer nets (B-Nets) (SIBUN /29/j, generalized nets /35/, evaluation-net-oriented system PORCASD /36/ and M-nets /19/. There exist other using great software systems with high level user assistance. One representative is the block-oriented interactive simulation system for dicrete systems BORIS /13, 14/. This system assists modelling (system level) and simulation (observation level). Concepts of date, model and method as well as information systems are integrated. The model components are agencies and models are nets of agencies. BORIS may be used for program or computing system development. The system TBRMAL /30/ is an example for an "open distributes experimental useful computer aided problem solving system" and shall be presented in this section.
129
3.3.
Simulatora
Simulators are apeoial devices only for simulation.The developmentof multi microprocessors is also a natural base for the development of discrete event-oriented simulators like special computers only for this aim. For instance on the base of queuing network models they may be used also for problems belonging to infozmatics. In /20/ the development and use of the stochastic discret event simulatorDESC is described. DESC has been used for the simulation of the performance of DESC using a queuing network model. The result are suggestions for performance improvement. If we compare the use of DESC with the use of a universal computer with a discrete simulation system solving one simulation problem, DESC needs very lower costs.
4. Terminating remarks The further development of experimental CAPS-systems has to take into account that more and more essential and realizable parts of human intelligence - artificial intelligence - may be introduced these systems and also fully exhausting the possibilities of parallel computers. Other important aspects are backtracking, formula manipulator, expert systems and fuzzy systems. See also /l-4/. The results of to-day shall be now presented by the following speakers.
5. Literatur
/I/ /2/
/3/ /4/
/5/
/6/ /7/ /8/ /9/
Goller, P. (Ed.): Simulationstechnik. 1. Symposium Simulationstechnik Erlangen, 1982, Proceedings. Springer-Verla Berlin - Heidelberg - New York 1982 (Informatik-Fachberichte 5% j Ameling, W. (Ed.): First European Simulation Congress ESC83. Aachen 1983, Proceedi s. Springer-Verlag Berlin - Heidelberg New York - Tokyo 1983"iInformatik-Wchberichte 71) J&vor, A. (Ed.): Simulation in Research and Development. IhUCS European Simulation Meeting, Eger 1984 (Preprints) Breitenecker, F.; Kleibsrt, Vi. (Eds.): Simulationstechnik. 2, Sym+xx&.~ Simulationstechnik, lien 1984 Proceedings. Springer-Verlag, - Heidelberg - Rew York 1984 (Informatik-Fachberichte 85) Carl. D.: Schwarze. G.: Exuerimental Use of Models Connected with Computer.Aided Problem Sol&n(: - Problems Connected with Informatics. In /3/ pp. 365-374 Schwarze, G.: Systemsimulation. In Handbuch der Systemtheorie (Hrsg.: Wunsch, G.). Akademie-Verlag 1985 Vansteenkiste, G.C.: Simulation Methodology for Improved Process Interaction. In /2/ pp. 32-43 Sol, H.G.: Simulation Based Inquiry Systems. In /2/ pp. 58-62 Schone, A.: Simulation Techniques and Design in Engineering Foundations, Nethods and Possibilities. In /2/ pp. 77-81
/IO/ /II/
/12/ /13/ /14/
/15/
/16/ /17/
/18/
/19/
/20/
/21/
/22/ /23/
/24/
Molti, I.: Some Problems in Rese&h of General Simulation Systems. In /2/ pp. 88-92 JBvor, J. (Ed.): Proceedings of the IMACS Simulation Meeting on Discrete Simulation and Releated Fields. North-Holland Publishing Company 1982 Kalashnikov, V.V.; Nemchiniv, B.V.: The Organization of the Research Process by Aggregative Simulation Systems. Syt. Anal. Model. Simul. l(1984) 2, 101-111 Maierhofer, J.; Schmitt, H.; Trosch, S.: Software Simulation with BORIS. In /2/ pp. 239-244 Decker, H.; Zobel, A.: Simulation and Verification - a Combined Approach to the Development of Software Systems. In /3/ pp. 135-149 Jobmann, M.: ILMAOS - eine Sprache zur Formulierung von Rechnersystemen. Univ. Hamburg, Fachbereich Inforrnatik,Bericht IFI-HHB-91/82, Hamburg 1982 Burghardt, K.: Hybrid Simulation of Queueing Networks. In this proceedings. Carl, D.: Eine Methodik zur Leistungsanalyse komplexer Rechnersysteme. Akad. d, Wiss. d. DDR, ZfR-Information-82.11. Berlin 1982 Carl, D.: RechnergestiitzteVerhaltensmodellierung und -bewertung komplexer Rechnersysteme. Akad. d. Wiss. d. DDR, ZfR-Information84.03, Berlin 1984 Griitzner,R.: Konzeptionelle Grundlagen der Modellierung und Simulation von Software auf der Basis modifizierter Petri-Netze. Akad. d. Wiss. d. DDR, ZfR-Information-84.06, Berlin 1984 Barel, M.: Ein Multimikroprozessorsystem fur die stochastische ereignisorientierte Simulation. Diss. TH Aachen, Fak. f. Elektrotechnik, Aachen 1983 Fischer, J.: Modellierung und Simulation paralleler diskreter, kontinuierlicher und kombinierter Prozesse in SIMULA. Akad. d. Wiss. d. DDR, ZfR-Informationen-82.20, Berlin 1982 Buschmann, H.: A68SIM - Discrete Event Simulation in ALGOL 68. In /2/ pp. 301-306 Ahrens, K.; Burghardt, F.; Fischer, J.: Beitrage zur Modellierung und Simulation in der Informatik. Humboldt-Univ. z. Berlin, Sekt. Math. Seminarbericht (in print) Hahn, W.: Computer Design Language - Version Munich (CDLM) A Multi-Level Simulation Tool. In /2/ pp. 207-212
/25/ Irmscher, K.: Programmsystem BNET (Bedienungsnetze der TU Dresden) - Analyseverfahren geschlossener Bedienungsnetze zur Leistungsbewertung von Rechnersystemen. TU-Informationen, Preprint 08-01-84, TU Dresden, Sektion Informationsveru.r~beFtlung, Dresden 1984 /26/ Bergholz, G.; Irmscher, K.: Perfomance Modelling of Computer Systems. In this proceedings /27/ Schmidt, B.: Rechnermodelle - die Simulation von Rechenanlagen mit GPSS-FORTRAN. R. Oldenbourg Verlag, Miinchen- Wien 1978 /28/ Unger, B.W.: OASIS 3.0 Reference Manual. Univ. Calgary, 1980 /29/ Bladko, R.: Simulation of Parallel Asynchronous Systems Described by B-Nets. In /3/ pp. 59-68 /30/ Carl, D.: Software Tool for Computer System Simulation. In this proceedings /31/ Muhlenbein, H.: TOCS - Ein Programmsystem zur Simulation von Rechensystemen. GMB Bonn, Jahresbericht 1980/81 K.: Computer System Simulation with MODlILA-2.Humboldt1321 E;ns, , zu Berlin, Sekt. Math. preprint 1985 (in print)
131
/33/ Burghardt,I?.:Ein Programmeystemzur Untersuchungvon Bedienunganetzwerkenin SIMUL4. Humboldt-UniversitIt zu Berlin, Sekt. Math. preprint in working out, Berlin 1985 In /3/ /34/ Fuss, IL: ImprovingSimulationswith Place/Transactor-Nets. pp* 57-58 /35/ Dahmen, N.: FORCASD - an EvaluationNet Oriented Program System for Modelling and Simulation.In /2/ pp. 267-272
132