Vijay Gehlot

An introduction to systems modeling and simulation with colored petri nets

Petri Nets provide a graphical notation for modeling systems and performing analysis. Colored Petri Nets (CPNs) combine the strengths of ordinary Petri Nets with a high level programming language, making them more suitable for modeling large systems. A CPN model is an executable representation of a system that can be analyzed through simulation. CPN models are built using CPN Tools, a graphical software tool and interface used to create, edit, simulate, and analyze models. This tutorial is meant to introduce the reader to the vocabulary and constructs of CPNs and illustrate the use of CPN Tools in creating and simulating models by means of a familiar, simple example. In particular, we show how to create a CPN model of the call center example presented by White and Ingalls in their tutorial Introduction to Simulation.

Use of Colored Petri Nets to Model, Analyze, and Evaluate Service Composition and Orchestration

The Concurrency, communications, resource constraints, and quality of service attributes are distinguishing features of web services. Assessment and determination of impact of the nonfunctional aspects such as service granularity, governance, composition, and orchestration is an overarching concern and this exercise should be carried out at the architecture design stage rather than post implementation and deployment. In this paper we illustrate, by means of a simple example, the use of Colored Petri Nets (CPNs) to model service composition and orchestration. We use the associated software tool called CPN Tools to perform the analysis. The results can be used in many ways such as to determine design alternative or to check conformance with existing service level agreements, etc. Colored Petri Nets, being a graphical modeling language suitable for modeling distributed, concurrent, deterministic and nondeterministic systems with synchronous and asynchronous communications, offer a natural choice for this endeavor. Although the example is given in the context of web services, CPNs view of interaction and coordination of systems is

A formalized and validated executable model of the SIP-based presence protocol for mobile applications

Presence information is one of the key aspects of mobile computing. Depending on the type of presence information, a variety of services can be built on top of a basic presence architecture. These can range from simple notification services and context-aware computing to more complex dynamic discovery and load balancing in mobile environments such as airborne web services (AWS). This paper gives details of a validated presence architecture based on the Session Initiation Protocol (SIP). Although SIP is viewed as an internet telephony protocol, in reality SIP applications go much beyond basic telephone services. This makes a SIP based Presence architecture even more attractive. By using the modular and hierarchy mechanism of our adopted modeling approach, other services and applications for mobile computations that make use of presence can be easily built on the top of the presented model and analyzed.

A Case Study in Defining Colored Petri Nets Based Model Driven Development of Enterprise Service Oriented Architectures

Many businesses as well as government enterprises are moving towards Service Oriented Architectures for their operation. The level of complexity, interaction and interdependence among various components make it difficult to design, develop, and analyze such systems as a whole. In particular it is difficult to foretell the effect of proposed changes and to evaluate alternative architectures. Adoption of a model-driven approach, coupled with sound Verification and Validation (V&V) techniques can provide a key solution for making qualitative and quantitative predictions about the possible system behaviors. This paper details a case study in Verification and Validation of a Service Oriented Architecture called MCSOA (Multi Channel Service Oriented Architecture). It is part of a larger exercise to integrate a model-driven approach into MCSOA Software Development for the US Department of Defense. We show how support for hierarchical and

A Hybrid Approach to Modeling SOA Systems of Systems Using CPN and MESA/Extend

Service oriented architectures (SOAs) are rapidly becoming an accepted means of providing network information exchange across a heterogeneous fabric of nodes. Given the complexity of such architectures and multiple levels of system interactions, creating a valid and usable SoS model for SOA application using a single technique that captures the desired level of details can be a daunting task. In this paper we give details of a hybrid approach to modeling and simulation of a specific SOA that is named MCSOA and has been configured for potential defense applications. In this approach, at the lowest system level (call it white-box level) we are using colored Petri nets (CPNs) to model internal protocols, communications, and resource consumption. This allows us to validate the components of the system and identify weaknesses at the component level. For the interaction at the highest system level (call it black-box level), we are using a set of discrete-event simulation tools known as MESA/Extend. The major advantage of this combination is that the assumptions at the black-box level are fully validated at the white-box level, which greatly simplifies and accelerates the design, development, and testing of the simulations of very large scale SOA-based systems of systems.

Ensuring Patient Safety by using Colored Petri Net Simulation in the Design of Heterogeneous, Multi-Vendor, Integrated, Life-Critical Wireless (802.x) Patient Care Device Networks

Hospitals and manufacturers are designing and deploying the IEEE 802.x wireless technologies in medical devices to promote patient mobility and flexible facility use. There is little information, however, on the reliability or ultimate safety of connecting multiple wireless life-critical medical devices from multiple vendors using commercial 802.11a, 802.11b, 802.11g or pre-802.11n devices. It is believed that 802.11-type devices can introduce unintended life-threatening risks unless delivery of critical patient alarms to central monitoring systems and/or clinical personnel is assured by proper use of 802.11e quality of service (QoS) methods. Petri net tools can be used to simulate all possible states and transitions between devices and/or systems in a wireless device network, and can identify failure modes in advance. Colored petri net (CPN) tools are ideal, in fact, as they allow tracking and controlling each message in a network based on pre-selected criteria. This paper describes a research project using CPN to simulate and validate alarm integrity in a small multi-modality wireless patient monitoring system. A 20-monitor wireless patient monitoring network is created in two versions: one with non-prioritized 802.x CSM protocols and the second with simulated quality of service (QoS) capabilities similar to 802.11e (i.e., the second network allows message priority management.) In the standard 802.x network, dangerous heart arrhythmia and pulse oximetry alarms could not be reliably and rapidly communicated, but the second networks QoS priority management reduced that risk significantly

SoSE Modeling and Simulation Approaches to Evaluate Security and Performance Limitations of a Next Generation National Healthcare Information Network (NHIN-2)

The emerging national healthcare information network (NHIN) is intended to improve the efficacy, efficiency, and safety of healthcare. At the same time. Service oriented architectures (SOAs) are rapidly becoming an accepted means of providing network information exchange across a heterogeneous fabric of node, and may be suitable for a next-generation NHIN-2s. Given the complexity NHIN and SOA due to multiple levels of system interactions, creating a valid and usable SoSE model for SOA application using a single technique that captures the desired level of details can be a daunting task. In this paper we give details of a hybrid approach to modeling and simulation of a specific SOA that is named MCSOA and has been configured for potential defense applications. Further, we show how MCSOA could be used to link low-comnmunications-capability healthcare data from sources like, the Alaska telemedicine testbed project (ATTP) to the proposed NHIN-2.

Colored Petri Net model of the Session Initiation Protocol (SIP)

The Session Initiation Protocol (SIP) together with its extension for presence and messaging is considered to be an enabler of converged communications. At the core, SIP is a signaling protocol used for establishing sessions in an IP network. SIP has been adopted by 3GPP for multimedia streaming services in cellular networks. This paper presents a formalized and executable Colored Petri Net model of SIP and its components. Since the broader impact of SIP is the realization of SIP-based services, these models can be used to design and analyze such services for desired behaviors prior to implementation.

Systems modeling and analysis using colored Petri Nets: a tutorial introduction and practical applications

Petri Nets are a graphical modeling language suitable for modeling distributed, concurrent, deterministic and nondeterministic systems with synchronous and asynchronous communications. One attraction of Petri Nets is that the basic vocabulary is small which renders them very flexible in terms of application domains for modeling. However, lack of high-level constructs and related support makes modeling of large system with Petri nets impractical. This is where Colored Petri Nets come into play. Colored Petri Nets (CP-Nets or CPNs) extend the vocabulary of ordinary Petri Nets and add features that make them suitable for modeling large systems. CPNs combine the strengths of ordinary Petri Nets with the strengths of a high-level programming language. Petri Nets provide the primitives for process interaction, while the programming language provides the primitives for the definition of data types and the manipulations of data values. From a practical applications point of view, CPNs support a mechanism of modules that allows one to construct models of large systems in a hierarchical manner. The hierarchy and module concept of CPNs allow different levels of abstraction that are inherent in most systems. The graphical representation makes it easy to see the basic structure of a complex CPN model, i.e., understand how the individual subsystems interact with each other. A major benefit gained by using CPNs is to obtain complete and unambiguous specification in the design stage of a project, and verify if it indeed provides the defined services correctly. CPN models can be made with or without explicit reference to time. Untimed CPN models are usually used to validate the functional/logical correctness of a system, while timed CPN models are used to evaluate the performance of the system. CPN models are built using CPN Tools which is a graphical software tool for creating, editing, simulating and analyzing models. It has a graphical editor that allows the user to create and layout the different net components. One of its nice features is that it uses pages to visually divide the model into components, enhancing its maintainability and readability without affecting the execution or analysis of the model. CPNs and CPN Tools have been used in numerous practical projects within a large variety of different application areas (see http://www.daimi.au.dk/CPnets/intro/example_indu.html). CPNs have been used to model and analyze hardware systems, software systems, biological systems, network and communication protocols, healthcare systems, workflow and business processes, distributed and resource constrained systems, manufacturing systems, control systems, etc. CPNs also have a formal, mathematical representation with a well-defined syntax and semantics. This representation is the foundation for the definition of the different behavioral properties and the analysis methods. CPNs provide several analysis methods, including simulation, state space analysis, sweep-line analysis and language analysis. However, for the practical use of CPNs and CPN Tools, it suffices to have an intuitive understanding of the syntax and semantics. CPNs and CPN Tools have been designed and developed with practical applications and practical use in mind. This tutorial will introduce the audience to basics of CPNs as well as CPN Tools. We will illustrate the key ideas by means of numerous examples and live demonstrations that emphasize practical applications of CPNs and CPN Tools. It requires no prior familiarity with Petri nets, system design and analysis, modeling, simulation, or any particular computer language. Its emphasis is on the practical, hands-on use of CPN Tools to build and execute CPN models. The major goals of this tutorial are as follows: • To give an informal introduction to Colored Petri Nets with emphasis on practical applications. • To familiarize with CPN Tools, its facility, and interface for creating, editing, simulating, and analyzing models graphically. • To familiarize with various visualization facilities built on top of CPN Tools. • To provide information on existing work on CPNs and existing models created using CPN Tools. • To help get started with model creation and analysis using CPNs.

Digital pathology annotation data for improved deep neural network classification

In the field of digital pathology, there is an explosive amount of imaging data being generated. Thus, there is an ever growing need to create assistive or automatic methods to analyze collections of images for screening and classification. Machine learning, specifically deep learning algorithms, developed for digital pathology have the potential to assist in this way. Deep learning architectures have demonstrated great success over existing classification models but require massive amounts of labeled training data that either doesn’t exist or are cost and time prohibitive to obtain. In this project, we present a framework for representing, collecting, validating, and utilizing cytopathology features for improved neural network classification.