Analysing Internet Software Retrieval Systems: Modeling and Performance Comparison

Stochastic Petri Net Package (SPNP) is a software package whose goal is to compute performance, availability or performability measures from Stochastic Petri Nets (SPN) and Fluid Stochastic Petri nets (FSPN). This software can use either analytic numeric methods, or simulation methods. Unfortunately, the standard discrete event simulation is inefficient to estimate the probabilities of rare events. For such rare event simulations, importance splitting technique is a good method to speedup the simulation. In the literature, two diffierent importance splitting techniques are known: RESTART and splitting. In this paper, we describe the application of these methods to (both fluid and discrete) Petri nets, their implementation in SPNP and we give some illustrations of the speed-up. The RESTART technique has already been applied in another Petri net package, TimeNet, but here we implement both RESTART and splitting, and we apply them to a more general class of Petri nets including the fluid ones.

The situation of telecommunications systems is rapidly evolving towards a state characterized by the presence of a multitude of services integrated in a single network structure. In order to ease their integration, it is important to provide an unambiguous description of the organization, of the functionality, and of the relationships existing among services. Since it is not possible to completely foresee the situation of services in the future, the description should be modular, so that the task of adding new services to an existing environment is simplified. Formal description techniques are languages designed to handle such descriptions. Their goal is to produce correct and unambiguous specifications, understanding and modeling the system and the domain within which it operates. Their input is a set of required statements and a conceptual overview of the system. Their output is a formal representation that captures the functional behavior. On the other hand, the throughput of physical networks is constantly growing, reaching the order of Gbit/s. In the meantime, services needing high throughput, like multimedia conferencing systems are being designed, creating a need for flexible, high performance communication systems. An important feature of new high speed services is their increased need for guarantees. Therefore, the use of powerful performance modeling techniques becomes of prime importance. Their goal is to obtain some notion of how the system will perform under a given set of conditions. Performance evaluation theorists have long been using system specifications to make a heuristic model of the system and then an analysis of the model. When system specifications exist, the question arises whether a more direct path to performance analysis can be found. Our scope is to provide a methodology that fully and consistently integrates formal specification and performance evaluation. A problem is that many of the bottlenecks and impediments to smooth data flow have to do with implementation issues not directly dealt within the system specification itself. Therefore, prior to generating a performance model, the formal specification should be enhanced with the relevant non-functional information. It is essential to assure a smooth specification of the non-functional information. Thus, it is necessary to consistently add this information to the formal specification. Besides, the extended formal method should focus on conserving the semantics of the original one, and on providing a coherent extension of the semantics and syntax for the new concepts. Since all relevant required information is integrated within the formal extended representation, we provide a mapping technique that translates the extended formal representation into a performance modeling conserving the properties of the formal model. In parallel to the development of a performance model, the formal model can be validated and verified, and their implementation can be tested for conformance towards the original formal model. Finally, the performance modelling is implemented in a programming language, and then simulated. The results from simulation can be reused as inputs for the optimization of the formal specification (functional optimization) or the extended formal model (design optimization). To sum up, the goal of this work is to: Define extensions to current formal methods that allow to enhance the formal specification with performance modeling related information. Develop a method to translate the extended formal specification into a performance modeling. Develop rules and guidelines to automatically implement the performance modeling for the purpose of a performance evaluation (we use the commercial tool Opnet). Develop a consistent and coherent methodology for the integration of performance modeling and formal specification.

The four papers in this special issue apply tools and techniques from computer performance evaluation in the very different domain of modelling biological systems. This might seem to be a very odd thing to do but the practice of analysing biological systems in this way is becoming increasing common. As data about the internal components of biological systems is becoming more readily available, biologists are increasingly asking questions about how systems function. In addition to conducting laboratory experiments, they are supported in this exploration by in silico experimentation based on models. The view taken of the biological processes focusses on the stimuli and responses, a view akin to that taken of engineered systems in systems engineering. Thus this new endeavour in biology is known as Systems Biology . Performance analysts have a long tradition of modelling systems in order to understand and predict their function. Their focus is particularly on the dynamic aspects of the system, the use of, and contention for, resources, and the impact of uncertainty or randomness. These issues are important in the biological setting also, and so it is perhaps inevitable that we see some people and techniques from performance modelling being applied in systems biology. In particular some of the high-level modelling formalisms which have supported Markovian performance modelling in the last few decades (stochastic Petri nets, stochastic process algebras, etc.) are being applied in the biological domain. Furthermore analysis techniques, such as Markovian analysis, Monte Carlo simulation and probabilistic model checking have also been adoped. In this volume we have sought to give a snapshot of a variety of work which is going on at this interface between systems biology and more traditional quantitative analysis techniques. It is by no means an exhaustive account of this exciting area, but rather a taster which will hopefully whet your appetite to find out more. To open the volume, the editors provide a survey paper describing the motivations and goals of the systems biology endeavour, summarising the existing modelling techniques and outlining some instances of cross-over between performance modelling and systems biology. This includes an account of the use of ordinary differential equations (ODE) and stochastic simulation to analyse biological systems, and the adoption of high-level modelling formalisms such as Petri nets and process algebras to drive these ODE models and simulations. In their paper Kwiatkowska, Norman and Parker show the application of logic and probabilistic model checking to the analysis of biological signalling pathways. They use the PRISM probabilistic model-checker to check formulae of the CSL logic against CTMC-based models of the MAPK cascade, a sequence of biochemical reactions which sends a message within a cell. The paper provides an introduction to the CSL logic as well as the reactive modules language implemented by the PRISM model checker. Performance measures of interest are described using reward structures and the analysis achieved by PRISM is able to show how the percentage of activated MAPK, a key component of the pathway, and the number of MAPK-MAPKK reactions, vary as a function of time, for different values of the initial number of MAPKs. The paper by Jeschke, Ewald, Park, Fujimoto and Uhrmacher addresses the drive for increased physical accuracy in simulation models which represent the spatial aspects of cell biology. Standard approaches to stochastic simulation of cellular systems assume that the cell is a homogeneous soup of biochemical components. The truth is far removed from this, as the cell has a lot of internal structure which can have a profound effect on the dynamics of reactions. Setting aside the assumption that the reacting chemical species are well-stirred, spatial approaches divide the volume into sub-volumes and apply a structured method which identifies the next reaction to occur in each subvolume. The cost of such an increase in accuracy in the simulation model is a much increased running time so the authors use a parallel and distributed approach to improve performance. To close this special issue we have a paper by DemattB, Priami and Romanel which uses the BlenX language and the Beta Workbench software to analyse the MAPK pathway considered also by Kwiatkowska, Norman and Parker. The BlenX language, and the Beta-binders process calculus which was its inspiration, are examples of a new generation of languages which have been designed specifically for the biological domain, as an alternative to using existing languages designed for modelling computer systems. The paper shows how a well-designed platform for modelling and simulation can lift the user's experience and make their use of process calculi more valuable, delivering insights which would not have been seen otherwise.

Basic graph models of processes, such as Petri nets, have usually omitted the concept of time as a parameter. Time has been added to the Petri net model in two ways. The timed Petri net (TPN) uses a fixed number of discrete time intervals. The stochastic Petri net (SPN) uses an exponentially distributed random variable. In this paper, a discrete time stochastic Petri model is described. These discrete time SPN's fill the gap between TPN and normal SPN. However, the use of discrete time complicates the SPN model in that more than one transition may fire at a time step. Finally, an example of a live and bounded Petri net which has nonempty, disjoint, recurrent subsets of markings is given.

This article presents a Petri net‐based approach to modeling and evaluating four different deadlock avoidance schemes for a distributed robotic system, i.e., a five‐robot‐five‐assembly‐line system. Among these four schemes are the conventional, full synchronization, global semaphore, and partial synchronization schemes. To explore such issues as the system performance and control structure complexity, this article conducts detailed Petri net modeling for this system and evaluates performance of the deadlock avoidance schemes using stochastic Petri nets. The interesting results presented include that: (1) any possible system deadlock can seriously degrade the system performance even if effective deadlock resolution techniques are available; (2) conservative use of resources is likely to be the best policy; and (3) higher resource utilization may not necessarily imply higher system production rate in a resource‐sharing environment. The related results need to be further explored for the larger resource‐sharing discrete event systems. ©1995 John Wiley & Sons, Inc.

The inference of performance models from low-level location tracking traces provides a means to gain high-level insight into customer and/or resource flow in complex systems. In this context our earlier work presented a methodology for automatically constructing Petri Net performance models from location tracking data. However, the capturing of synchronisation between service centres – the natural expression of which is one of the most fundamental advantages of Petri nets as a modelling formalism – was not explicitly supported. In this paper, we introduce mechanisms for automatically detecting and incorporating synchronisation into our existing methodology. We present a case study based on synthetic location tracking data where the derived synchronisation detection mechanism is applied.KeywordsLocation TrackingPerformance ModellingData MiningGeneralised Stochastic Petri Nets

The focus area of this work is integrating reliability, availability, and maintainability (RAM) aspects into the conceptual process design of a real industrial system. Quantitative performance analysis has been carried out using Petri nets (PN) approach. In the present study, availability is considered as a performance measure which has been used as an indicator of the reliability and maintainability of the plant. The availability is considered in the process design stage for sorting different design alternatives. The proposed technique suggested in this paper provides a better way of understanding the behaviour of a system under varying operating conditions. It will also help practitioners in deciding upon maintenance strategy so that operation and maintenance cost can be optimised. Stochastic Petri nets (SPN) has been used to model the possible interactions among all active or parallel equipments in the system. Performance parameters have been evaluated using modelling and simulation software GRIF of Petri module.

A new approach to Vulnerability, Uncertainty and Probability (VUP) quantification procedure using Stochastic Petri Nets within a network of interacting satellites is presented. A Stochastic Petri Net based model is developed to quantify VUP in a network of interacting satellites. Three indicators are proposed to determine the VUP definitions in interacting network of satellites. The proposed VUP quantification scheme addresses a methodology which employs a Stochastic Petri Net for quantitative analysis of the behavior of the network. With the random variables associated with the Petri Net transitions, the dynamic behavior of the cooperating satellites in a SPN model can be mapped onto a time-continuous Markov chain with discrete state space. After generating a Markov Stochastic Petri Net model, the probability of a given condition in the network at a specified time can be computed and quantified as well as the vulnerability and uncertainty of the system using the identified indicators.

Performance analysis can be carried out in several ways, especially in case of Markovian models. In order to interpret high level of abstraction, we often use modeling tools like timed Petri nets (TPNs). Although some subclasses of TPNs (e.g. stochastic Petri nets (SPNs) [17, 19]) can be handled analytically, a general timed Petri net is hard to evaluate via numerical analysis. However, the simulation of SPNs or deterministic and stochastic Petri nets (DSPNs) [16] are supported by many known tools (see, e.g. [3, 20]), it is hard to find a simulation tool for timed Petri nets with generally distributed (i.e., particular but arbitrarily chosen) firing times. In this paper, we present TiPeNeSS (Timed Petri Net Simulator Software) which supports the simulation of timed Petri nets containing transitions with generally distributed firing delays. The input of the software (the Petri net and the parameters) is defined in an XML file, what allows us to generate results in batch mode. Besides, we describe a case study in which we optimize the frequency of the regular maintenance in a manufacturing process.

In this work, we explore the applicability of Stoch astic Petri Nets (SPNs) to multi-state failure and survivability analyses, using space sys tems as examples. Multi-state failure analyses introduce degraded states, and thus provid e more insights than the traditional binary reliability analysis into the degradation be havior of a system and its progression towards complete failure. Survivability analysis fo cuses on, among other things, the failure propagation in a system or a network following node or component failure, and it assesses for example whether the system will experience graceful degradation or catastrophic failure. The potential complexity of multi-state failure and system survivability analyses requires powerful (and flexible) stochastic modeling and simulation tools. After a brief introduction to Petri Nets, we argue that SPNs are particularly well suited to these types of analyses, and they are better at the task than the commonly used Markov Chains. We then propose a general framework for system survivability analysis, and we illustrate its applicability using space systems examples. We compare the survivability of two space architectures, a monolith spacecraft and a space-based network (which allows for distributed redundancy of certain subsystems). Monte Carlo simulations are run to generate representative results of stochastic behavior of the two architectures with r espect to on-orbit anomalies and failures. Finally, a comparison of the outputs of the two SPN models indicate, and quantify, the survivability advantage of the space-based network over the monolith spacecraft with respect to on-orbit anomalies and failures.

PurposeThe purpose of this paper is to examine the potential of applying Petri nets to improve construction material logistics analysis and modelling.Design/methodology/approachThe characteristics of construction logistics are unveiled by analysing the existing practices of logistics management. In views of the dynamic nature of construction logistics problem, a stochastic Petri nets (SPNs) approach is proposed to tackle the time‐evolution property. Using a simulation package called PetriTool™ a simulation model is developed. Finally, a case example is applied to illustrate the way in which SPNs is used for analysing and modelling construction material logistics problems.FindingsThe results indicate that the impacts triggered by variations in delivery lead‐time and changes in delivery quantities can be approximated thereby facilitating decision makers to devise a more reliable and optimal materials management plan for construction projects.Research limitations/implicationsThe complex routing patterns in demand analysis and materials procurement methods that results in the enlarged supply chains have not been considered in this paper.Practical implicationsThe lack of a simple but powerful formalism to analyse and model the decision process under a dynamic environment hinders the implementation of efficient logistics systems in the construction industry. The SPNs model presented in this paper can support planners and managers in making construction logistics management decisions under dynamic environment.Originality/valueThis paper demonstrates that the time‐based SPNs can offer more enriched solutions especially when modelling the time‐evolution behaviours of construction logistics.

The stochastic Petri net package (SPNP) is a versatile modeling tool for the solution of generalized stochastic Petri nets (GSPN). The GSPN models are described in the input language for SPNP called C-based SPN language (CSPL). The CSPL is an extension of the C programming language with additional constructs that facilitate easy description of GSPN models. The GSPN models specified to SPNP are actually “GSPN reward models” that are based on the “Markov reward models” paradigm. This provides a powerful modeling environment for dependability analysis, performance analysis, and performability modeling. A number of important Petri net constructs like marking dependency, variable cardinality arc, and enabling functions facilitate the construction of models for complex systems.

The presented review analyzes the approaches and methods of using simulation models in the maintenance and repair organization of the complex technical objects and systems. The development of the concept of "Industry 4.0" and the Internet of things involves the transition from periodic and preventive maintenance to predictive maintenance based on the study of processes during the object operation. It is shown that system models based on Petri nets are convenient for describing and analyzing technological processes and equipment maintenance. The effectiveness of timed stochastic colored Petri nets for virtual tests in the design of maintenance and repair procedures in complex systems is revealed. The use of hierarchical Petri nets allows building complex models that describe interrelated processes. A general model based on a hierarchical network is proposed to study the processes of deterioration and degradation of the technical object aggregates and elements and to form a predictive maintenance strategy. The model includes a Gantt chart of production tasks, a set of active and reserve units, maintenance and repair modules. The examples of simulation models on Petri nets for various technical systems are considered. Solutions for flexible production systems, railway bridges, offshore wind turbines, an aircraft fleet, a group of robotic vehicles, and a complex of computer equipment of an enterprise are given. The models of various maintenance strategies are considered and the generality of the models based on hierarchical Petri nets is shown. The article formulates methodological principles for constructing hierarchical networks to simulate the operation and maintenance of technical systems. The article analyzes known software tools for the implementation of timed stochastic colored Petri nets. The summary concludes that the use of simulation models on Petri nets is promising for organizing maintenance and repair of complex technical objects and systems.

The Petri Net Toolbox (PN Toolbox) for MATLAB is a software package that offers instruments for the simulation, analysis and design of untimed, deterministic and stochastic P-/T-timed and stochastic Petri nets (PNs). The facilities available in this toolbox are appropriate for studying the dynamics of many classes of discrete-event systems. The Petri Net Simulink Block (PNSB) allows the modeling and analysis of hybrid systems whose event-driven part(s) is (are) modeled based on the PN formalism. The current paper focuses on the exploitation of the PN Toolbox for illustrating the usage of the PN theory in Control Engineering from the pedagogic point of view, the discussion being supported by example problems and comments on the teaching goals. The PN Toolbox is included in the Connections Program of The MathWorks Inc., as a third party product.

Service Failure model is described by Stochastic Petri nets. Some of the service failure precisely corresponds to the confusion. Confusion is one of very important concept in the Petri nets. Because it appears to be difficult to obtain a “correct” implementation when emerges confusion in the Petri net system, it is not a good model when exists confusion. When we build some service failure and recovery model based on Stochastic Petri nets, it is very important to avoid some structural design error such as useless service failure structure (siphon and confusion structure). In this paper, we discuss useless service failure structures which are precisely equivalent to structural confusion; an algorithm detection useless service failure structure is presented.