As a network's uses, and especially the number of internet users, increases rapidly, an efficient system for managing large-network traffic datasets becomes an important issue. Although there are several network traffic analysis tools, such as tcpdump, Ethereal, and other applications, these tools have weaknesses, namely the limited size of files, the use of command line execution, the large memory and huge computational power requirements. In addition to these scalability limitations, both tcpdump and Ethereal have security issues. Files captured by these tools keep all of the packet information, such as internet protocol (IP) addresses, port numbers, and packet sizes. As well as basic network traffic information, the captured files contain secure information: user identification numbers (IDs) and passwords. Therefore, the captured files should not be allowed to be leaked out. However, network analyses need to be performed outside the target networks in some cases. This paper presents an approach to efficiently and quickly analyze a large number of network behaviors. This is achieved by applying System Entity Structure (SES) theory. To speed up evaluation time, a web-based distributed simulation approach over Service-oriented Architecture (SOA) is applied. Discrete Event System Specification/Service-oriented Architecture (DEVS/SOA) is used to deploy workloads into multi-servers, increasing overall system performance. A web-based distributed simulation contains two fundamental processes: distributing and analyzing among loosely coupled models through message-passing methods. The distributed simulation – allocating distributing models inside networks and assigning analyzing models outside networks – also allows the analysis of network behaviors out of networks while keeping important information secured.

This paper addresses the problem of maximizing the utilization of operating rooms, which is translated to jobs scheduling in an identical parallel machine environment with sequence-dependent setup times and an objective of minimizing the makespan. The jobs' processing times and setup times are stochastic for better depiction of the real world. This is a non-deterministic polynomial time (NP)-hard problem, and in this paper a new heuristic is developed and compared to existing ones using simulation and optimization. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm Longest Expected Processing with Setup Time (LEPST) over the other algorithms presented.

Integrated development environments such as Eclipse allow users to write programs quickly by presenting a set of recommendations for code completion. Similarly, word processing tools such as Microsoft Word present corrections for grammatical errors in sentences. Both of these existing structure editors use a set of constraints expressed in the form of a natural language grammar to restrict/correct the user (syntax-directed editing) or formal grammar (language-directed editing) to aid document completion. Taking this idea further, in this paper we present an integrated software system capable of generating recommendations for model completion of partial models built in editors for domain-specific modeling languages.We present a methodology to synthesize model editors equipped with automatic completion from a modeling language’s declarative specification consisting of a meta-model with a visual syntax. This meta-model directed completion feature is powered by a first-order relational logic engine implemented in ALLOY. We incorporate automatic completion in the generative tool AToM3. We use the finite state machines modeling language as a concise running example. Our approach leverages a correct by construction philosophy that renders subsequent simulation of models considerably less error-prone.

The emergence of chip multiprocessors is leading to rapid advances in hardware and software systems to provide distributed shared memory (DSM) programming models, so-called DSM systems. A DSM system provides programming advantages within a scalable and cost-effective hardware solution. This benefit derives from the fact that a DSM system creates a shared-memory abstraction on top of a distributed-memory machine by caching data replicas locally. In this respect, a coherence protocol is a vital component responsible for assuring data consistency across all replicas. The design of coherence protocols impacts a DSM system in terms of both performance and accuracy. Performance is often measured via simulation and various verification techniques have been proposed to deal with protocol accuracy. Nevertheless, integrating accuracy verification into a DSM cluster simulation to ensure correct simulation results is still an open issue.

In this paper, we address three properties of a coherence protocol (safety, liveness, and inclusion) without which errors may occur in the simulation results. We propose a specification-based parameter–model interaction (SPMI) technique to detect these cases in a particular DSM cluster simulator called DSiMCluster. Our experimental results demonstrate that with SPMI, DSiMCluster can ensure the coherence protocol properties and provides a correct reflection of memory characteristics in shared-memory and DSM multiprocessors.

Verification and validation (V&V) is a critically important phase in the development life cycle of a simulation model. In the context of network simulation, traditional network simulators perform well in using a simulation model for evaluating the performance of a network protocol but lack the capability to check the ''correctness'' of the simulation model being used. To address this problem, we have extended J-Sim—an open-source component-based network simulator written entirely in Java—with a state space exploration (SSE) capability that explores the state space created by a network simulation model, up to a configurable maximum depth, in order to find an execution (if any) that violates an assertion, i.e. a property specifying an invariant that must always hold true in all states. In this paper, we elaborate on the SSE framework in J-Sim and present one of our fairly complex case studies, namely verifying the simulation model of the Ad-hoc On-demand Distance Vector (AODV) routing protocol for wireless ad-hoc networks. The SSE framework makes use of protocol-specific properties along two orthogonal dimensions: state similarity and state ranking. State similarity determines whether a state is ''similar to'' another in order to enable the implementation of stateful search. State ranking determines whether a state is ''better than'' another in order to enable the implementation of best-first search (BeFS). Specifically, we develop protocol-specific search heuristics to guide SSE towards finding assertion violations in less time. We evaluate the efficiency of our SSE framework by comparing its performance with that of a state-of-the-art model checker for Java programs, namely Java PathFinder (JPF). The results of the comparison show that the time needed to find an assertion violation by our SSE framework in J-Sim can be significantly less than that in JPF unless a substantial amount of programming effort is spent in JPF to make its performance close to that of our SSE framework.

We present an end-to-end simulation framework that is capable of simulating High-Performance Computing (HPC) systems with hundreds of thousands of interconnected processors. The tool applies discrete event simulation and is driven by real-world application traces. It provides a semantically correct replay of MPI application traces and maintains reasonable simulation details of both the processors in general and the interconnection network in particular. Among other things, it features several interconnection network topologies, flexible routing schemes, arbitrary application task placement, point-to-point statistics collection, and data visualization. With a few case studies, we demonstrate the usefulness of this tool for assisting high-level system design as well as for performance projection and application tuning of future HPC systems.

The graphics processing unit (GPU) has evolved into a flexible and powerful processor of relatively low cost, compared to processors used for other available parallel computing systems. The majority of studies using the GPU within the graphics and simulation communities have focused on the use of the GPU for models that are traditionally simulated using regular time increments, whether these increments are accomplished through the addition of a time delta (i.e., numerical integration) or event scheduling using the delta (i.e., discrete event approximations of continuous-time systems). These types of models have the property of being decomposable over a variable or parameter space. In prior studies, discrete event simulation has been characterized as being an inefficient application for the GPU primarily due to the inherent synchronicity of the GPU organization and an apparent mismatch between the classic event scheduling cycle and the GPU’s basic functionality. However, we have found that irregular time advances of the sort common in discrete event models can be successfully mapped to a GPU, thus making it possible to execute discrete event systems on an inexpensive personal computer platform at speedups close to 10x. This speedup is achieved through the development of a special purpose code library we developed that uses an approximate timebased event scheduling approach. We present the design and implementation of this library, which is based on the compute unified device architecture (CUDA) general purpose parallel applications programming interface for the NVIDIA class of GPUs.

Agent-directed simulations (ADS) are used in many domains to study complex systems. These are systems where non-linear effects can result from these emergent behaviors, making them difficult to analyze and predict. Correspondingly, in ADS, as well as explicitly specified behaviors of individual agents, higher level behaviors can emerge spontaneously from agent action sequences and agent–agent interactions. We have previously introduced the complex event formalism for specifying emergent behaviors in dynamically executing ADS [1, 2]. Based on the formalism, we also described a method for detecting and analyzing emergent behaviors in multi-agent simulations, giving us an effective means of studying, and a more reliably way of predicting, these systems. Complex event types define sets of multi-dimensional structures of interrelated events arising from the actions of one or more agents. They are therefore directly related to the agent specifications, which determine the behavior of individual agents. Although the abstract constructs of the formalism have already been introduced in [1] and [2], they have not yet been related to a specific agent-based specification language. Here, we define the constructs in terms of the X-machine formalism, which is widely used to specify multi-agent systems. This extends the existing X-machine framework to model higher level emergent behaviors as well as agent-level state transitions. Thus, emergent behaviors at any level of abstraction can be specified for detection and analysis in a dynamically executing ADS.

Agents offer a new and exciting way of understanding the world of work. In this paper we describe the development of agent-based simulation models, designed to help to understand the relationship between people management practices and retail performance. We report on the current development of our simulation models which includes new features concerning the evolution of customers over time. To test the features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of customers' word of mouth. To validate and evaluate our model, we introduce new performance measure specific to retail operations. We show that by varying different parameters in our model we can simulate a range of customer experiences leading to significant differences in performance measures. Ultimately, we are interested in better understanding the impact of changes in staff behavior due to changes in store management practices. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organizational capabilities in the future.

Operator training systems are essential tools for industrial systems, particularly for those where human error has an impact on the safety of materials and personnel and/or may cause significant financial losses. The work presented here is part of a major research project concerned with the study of operator behavior when facing safety-critical situations. The application concerns the supervisory control of an electricity distribution substation. This paper focuses on the first phase of the project; the development of a real-time operator training system that simulates the supervisory control system. The simulator promotes the operator's immersion into a virtual replica of the real working environment. The simulator's architecture, based upon a set of formal models interconnected to form the simulation engine, is presented. The models were constructed using the colored Petri nets formalism. The modular architecture allows for remote interaction via the web and offers two interfaces with the plant control system: a virtual reality representation of the human–machine interface and a supervisory system representation. This paper presents and discusses the architecture of the Operator Training Simulator.

In this paper, we study the behaviour of the slotted Aloha multiple access scheme with a finite number of users under different traffic loads and optimize the retransmission probability q_r for various settings, cost objectives and policies. First, we formulate the problem as a parameter optimization problem and use certain efficient smoothed functional algorithms for finding the optimal retransmission probability parameter. Next, we propose two classes of multi-level closed-loop feedback policies (for finding in each case the retransmission probability q_r that now depends on the current system state) and apply the above algorithms for finding an optimal policy within each class of policies. While one of the policy classes depends on the number of backlogged nodes in the system, the other depends on the number of time slots since the last successful transmission. The latter policies are more realistic as it is difficult to keep track of the number of backlogged nodes at each instant. We investigate the effect of increasing the number of levels in the feedback policies. We also investigate the effects of using different cost functions (with and without penalization) in our algorithms and the corresponding change in the throughput and delay using these. Both of our algorithms use two-timescale stochastic approximation. One of the algorithms uses one simulation while the other uses two simulations of the system. The two-simulation algorithm is seen to perform better than the other algorithm. Optimal multi-level closed-loop policies are seen to perform better than optimal open-loop policies. The performance further improves when more levels are used in the feedback policies.

Processor concepts, implementation details, and performance analysis are fundamental in computer architecture education, and MIPS (microprocessor without interlocked pipeline stages) processor designs are used by many universities in teaching the subject. In this paper we present a MIPS32 processor simulator, which enriches students' learning and instructors' teaching experiences. A family of single-cycle, multi-cycle, and pipeline processor models for the MIPS32 architecture are developed according to the parallel Discrete Event System Specification (DEVS) modeling formalism. A collection of elementary sequential and combinational model components along with the processor models are implemented in DEVS-Suite. The simulator supports multi-level model abstractions, register-transfer level animation, performance data collection, and time-based trajectory observation. These features, which are partially supported by a few existing simulators, enable important structural and behavioral details of computer architectures to be described and understood. The MIPS processor models can be reused and systematically extended for modeling and simulating other MIPS processors.

This paper proposes an advanced decision system, called Virtual Dynamic Store (VDS), for supporting retail store management. The VDS architecture supports the decision processes within retail stores by providing users with three main functionalities: store layout analysis, item and shelf management, and store monitoring and control by real-time simulation (or remote control by on-line simulation). As additional functionality, not related to the decision process within a retail store, the VDS architecture could also be used as a virtual web shop for e-commerce. To provide evidence on the relevance of the VDS, the architecture is tested within a real store by proposing four different application examples that show the potential of the VDS as an advanced decision support system.

Simulation is an effective training method for air traffic control to ensure the safety and efficiency of the aviation industry, which is growing quickly. In this paper, the key techniques are explored in the implementation of a cost-effective approach to constructing a flight control tower simulator using commodities. A client/server styled architecture is proposed to divide the system into sub-systems, and a process model is abstracted to describe the high-level process of the system. Finite state machines (FSMs) and a primitive–command interaction mechanism are proposed for efficient behavior representation and flexible control of the simulated entities (which are encapsulated into a computing kernel component to handle the real-time computation and interaction), respectively, resulting in higher maintainability and extensibility. Parallel rendering based on a PC cluster and image warping and edge blending algorithms are explored to generate virtual tower out-the-window scenes of ultra-high resolutions and seamless tiled displays on a cylindrical screen, respectively; thus the special hardware is replaced with commercial off-the-shelf hardware to achieve much lower cost and higher maintainability. With these techniques, a flight control tower simulator based on COTS hardware has been implemented and employed to train some tower controllers in China.

We describe an integrated modeling framework for an interactive power network (IPN) consisting of a power network (PN) and a wireless communication network (WCN). The PN is modeled using a set of piecewise linear (PWL) equations. The WCN is modeled using a Markov chain-based model that can capture the randomness of the communication channel. The impacts of the WCN are incorporated into the PN models using variable time delays. By formulating a convex optimization problem based on a composite Lyapunov function and solving this problem using linear matrix inequality solvers, we predict the reaching criteria for orbital existence. We investigate the impacts of time delays due to the wireless network and communication channel disruptions on the reachability bound, mean square stability, and performance of the IPN. Subsequently, using the integrated modeling framework, we demonstrate the efficacy of a scheme to jointly optimize control performance and network resource utilization. We demonstrate how communication fault tolerant protocols can be implemented to ensure that the IPNs operate within their reachability and performance bounds, despite one or more disruptions in the communication channels. We further demonstrate that when the WCN is clustered due to communication disruptions, each cluster can optimize its control communication network.

In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data.

During the last few years, continuous progresses in wireless communications have opened new research fields in computer networking, aimed at extending data networks connectivity to environments where wired solutions are impracticable. Among these, vehicular communication is attracting growing attention from both academia and industry, owing to the amount and importance of the related applications, ranging from road safety to traffic control and up to mobile entertainment. Vehicular Ad-hoc Networks (VANETs) are self-organized networks built up from moving vehicles, and are part of the broader class of Mobile Ad-hoc Networks (MANETs). Owing to their peculiar characteristics, VANETs require the definition of specific networking techniques, whose feasibility and performance are usually tested by means of simulation. One of the main challenges posed by VANETs simulations is the faithful characterization of vehicular mobility at both the macroscopic and microscopic levels, leading to realistic non-uniform distributions of cars and velocity, and unique connectivity dynamics. However, freely distributed tools which are commonly used for academic studies only consider limited vehicular mobility issues, while they pay little or no attention to vehicular traffic generation and its interaction with its motion constraints counterpart. Such a simplistic approach can easily raise doubts on the confidence of derived VANETs simulation results. In this paper we present VanetMobiSim, a freely available generator of realistic vehicular movement traces for networks simulators. The traces generated by VanetMobiSim are validated first by illustrating how the interaction between featured motion constraints and traffic generator models is able to reproduce typical phenomena of vehicular traffic. Then, the traces are formally validated against those obtained by TSIS-CORSIM, a benchmark traffic simulator in transportation research. This makes VanetMobiSim one of the few vehicular mobility simulator fully validated and freely available to the vehicular networks research community.

High impedance faults (HIF) are faults that are difficult to detect by conventional protection relays. In this paper, a new HIF model is introduced and a novel methodology is presented to detect HIF by means of discrete wavelet transform (DWT) and artificial neural network (ANN). The distorted waveforms (HIF, load switching, line switching, capacitor switching and non-linear loads that behave similar to HIF current) are generated using PSCAD/EMTDC, captured with a sampling rate of 20 kHz and de-noised using DWT to obtain signals with higher signal-to-noise ratio. DWT is used to decompose the distorted signal and to extract its useful information. Appropriate feature vectors are created and applied in training the ANN. The effectiveness of the proposed method was tested using a wide spectrum of disturbances. Simulations are carried out to confirm the suitability and capability of the proposed method in HIF detection.

The Multi-Paradigm Modeling (MPM) approach of model-based development emphasizes the specification of a system by multiple models. We use transformations to automatically transform, integrate and synchronize models. Verification and validation of model transformations are fundamental issues: we need to express what a valid model is and how a valid model transformation may transform the models; otherwise, we have to analyze each transformed model individually, which makes it difficult to automate the process of using models. We have formally analyzed various model transformations in several case studies and industrial projects. From this experience, we have distilled the frequently recurring techniques and solutions, referred to as Model Transformation Analysis (MTA) methods. These instances, similarly to design patterns in object-oriented programming, define special constructions as solutions for recurring problems that arise when one implements a model transformation. Moreover, MTA methods contain special techniques and language features that should be taken into account when one designs a model transformation framework or a model transformation language. We hope that MTA methods may be the basis of automated formal analysis techniques of model transformations. This paper contributes the concept and instances of MTA methods and provides a case study based on an industrial project of mobile application development. With this real-world example, we want to demonstrate the role and use of MTA methods. The case study is implemented in Visual Modeling and Transformation System (VMTS), which is a tool that realizes the MPM concept to provide a model and model transformation-based environment for software development.

Carousel conveyors (also known simply as "carousels") are an important and well-known type of material handling system used in warehousing and manufacturing to store/retrieve small-to-medium-sized parts. A traditional multi-tier (or multi-level) carousel, which is powered by a single drive, rotates as a whole unit, and brings the items to the input/output point one at a time, since each level cannot be rotated independently. A special type of carousel (also known as a "rotary rack") uses a dedicated drive for each level of the carousel. This allows each level of the carousel to rotate independently of and concurrently with the other levels. It also allows a rotary rack to support multiple pickers (or robots) while a traditional carousel can support typically only one picker. Although rotary racks have been available for quite some time, questions about their performance and efficient use remain largely unanswered, especially in comparison with traditional carousels, which have been the subject of numerous studies. Assuming one or more picking robots, in this paper we use a simulation model to develop and analyze four retrieval strategies for a rotary rack. We compare the performance of the four strategies as a function of the input data and the number of robots in the system. We also address the question of the appropriate number of robots. As one would expect, the simulation results indicate that the throughput of the system increases with the number of robots. However, more robots also lead to more idle time per robot. We show the tradeoff between the expected time required to process an order and the expected idle time per robot.

Simulation of moving interfaces such as a fire front usually requires resolution of a large-scale and detailed domain. Such computing involves the use of supercomputers to process the large amount of data and calculations. This limitation is mainly due to the fact that a large scale of space and time is usually split into nodes, cells, or matrices and the solving methods often require small time steps. In this paper we present a novel method that enables the simulation of large-scale/high-resolution systems by focusing on the interface and its application to fire-spread simulation. Unlike the conventional explicit and implicit integration schemes, it is based on the discrete-event approach, which describes time advance in terms of increments of physical quantities rather than discrete time stepping. In addition, space is not split into discrete nodes or cells, but we use polygons with real coordinates. The system is described by the behavior of its interface and evolves by computing collision events of this interface in the simulation. As this simulation technique is suitable for a class of models that can explicitly provide the rate of spread, we developed a radiation-based propagation model of wild land fire. Simulations of a real large-scale fire performed by implementation of our method provide very interesting results in less than 30 s with a 3-m resolution with current personal computers.

Optical burst switching (OBS) is a promising method for data transfer in photonic networks based on a Wavelength Division Multiplexing (WDM) technology. Transmission control protocol (TCP)-based applications generate the majority of data traffic in the Internet; thus, understanding and improving the performance of TCP over OBS networks is critical. In this paper, we develop a novel burst-dropping strategy to improve the quality of service provided by TCP over OBS networks. Our approach relies on random-segment dropping according to the capacity of a special optical component, called the optical virtual memory, which is used for buffering purposes within the optical switches. The core node predicts incipient congestion by computing the average blocking duration in the optical virtual memories. When this size exceeds a threshold, segments are randomly dropped. Simulation results show that the proposed method performs better than common techniques in terms of burst loss probability and transmission delay.

NeSSi (network security simulator) is a novel network simulation tool which incorporates a variety of features relevant to network security distinguishing it from general-purpose network simulators. Its capabilities such as profile-based automated attack generation, traffic analysis and support for detection algorithm plug-ins allow it to be used for security research and evaluation purposes. NeSSi has been successfully used for testing intrusion detection algorithms, conducting network security analysis and developing overlay security frameworks. NeSSi is built upon the agent framework JIAC, resulting in a distributed and extensible architecture. In this paper, we provide an overview of the NeSSi architecture as well as its distinguishing features and briefly demonstrate its application to current security research projects.

In this paper we present a combinatorial scheme based on hidden Markov models (HMM) and wavelet transform (WT) to discriminate between magnetizing inrush currents and internal faults in power transformers. HMMs are powerful tools for transient classification which compute the maximum likelihood probability between training and testing data signals for identification. The WT is employed to extract certain features which reduce the computation burden of HMMs and enhance detection accuracy. The newly extracted feature efficiently discriminates between faults by different trends. The k-means clustering technique is applied to reduce the training procedure time investment. Since the discrimination method is based on the probabilistic characteristics of the signals without application of any deterministic index, more reliable and accurate classification is achieved. This method is independent of the selection thresholds. Based on the proposed algorithm a high-speed relay response (a quarter of a cycle) can be achieved. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software.

The independent verification and validation (IV&V) of simulation models is largely restricted to applications in the military and public policy domains. There is little evidence of IV&V for industrial simulation models. This is largely because industrial simulations are normally of a much smaller scale and do not warrant a full IV&V. A procedure for IV&V of industrial simulation models is described that provides a viable alternative where the cost and time of IV&V must be contained. The procedure consists of the following activities: structured walkthrough, review of model assumptions, code examination, review of verification procedures, replications analysis, review of static analysis, review of output reporting, and investigation of results and experimentation. The IV&V of a Sellafield Limited supply chain model is described.

Determination of the loadability margin for various security limits is of great importance to the secure operation of the power system as is proposing a reliable method for the fast determination of bifurcation points in the systems. Eigenvalue calculation is normally used for both actions. Whereas this method is computationally expensive, soft computing methods are employed to improve calculation time. In this paper a support vector machine (SVM) method is proposed to aid the fast classifying of bifurcation stability of the system. A novel approach based on particle swarm optimization (PSO) is also introduced to find the closest load ability margin and its corresponding loading direction. Three security limits are considered in this study: saddle-node bifurcation, limit-induced bifurcation, and Hopf bifurcation. The simulation results for two test systems demonstrate the effectiveness of the proposed methods.

We introduce an extension of the classic Discrete Event System Specification (DEVS) formalism that includes stochastic features. Based on the use of the probability spaces theory we define the stochastic DEVS (STDEVS) specification, which provides a formal framework for modeling and simulation of general non-deterministic discrete event systems. The main theoretical properties of the STDEVS framework are treated, including a new definition of legitimacy of models in the stochastic context and a proof of STDEVS closure under coupling. We also illustrate the new stochastic modeling capabilities introduced by STDEVS and their relation with those found in classic DEVS. Practical simulation examples are given involving performance analysis of computer systems and hybrid modeling of networked control systems, applications where the modeling of stochastic components is vital.

OMNeT++ is a discrete event simulation environment primarily designed for communication networks. In this paper we present an approach to enable OMNeT++ to simulate complex hierarchical process chains. Process chains are a common modeling paradigm in the logistics area for analysis and optimization, and have been used intensely in many practical applications. Their evaluation is supported by the ProC/B toolset, a collection of software tools for modeling, analysis, validation and optimization of process chains. Here we describe how OMNeT++ has been integrated as a new simulation engine into the toolset. The integration has to overcome some core problems to allow a smooth interaction between OMNeT++ and the other tools: in particular, the OMNeT++ model description of the logistics network should be kept manageable, it should reflect the entire model structure and non-standard performance figures, being relevant for an economic evaluation should be ascertainable in order to satisfy the specific needs of the application area. This paper highlights the main steps of the automatic transformation of a hierarchical process chain model into a hierarchical model in OMNeT++. Furthermore, we show how the transformation has been validated and how detailed performance figures can be evaluated with OMNeT++.

Grid computing has emerged as a new paradigm for distributed systems, which promotes sharing of distributed resources. To maximize its benefits, it is essential to discover the resources available on the grid, and then effectively map the jobs to the resources for maximizing a given objective function. This paper focuses on the problem of matching of jobs to resources in a computing grid. Jobs are classified based on their service demands. Matching policies that use only the knowledge of job classes are introduced in this paper; simulation experiments demonstrate the effectiveness of these policies. Under a variety of different workload parameters the proposed matching policies demonstrate a performance comparable to, or better than, the well-known Minimum Completion Time matching policy, which is based on detailed a priori knowledge of jobs and resource characteristics.

In this paper, hierarchical models are proposed as a general approach for spatio-temporal problems, including dynamical mapping, and the analysis of the outputs from complex environmental modeling chains. In this frame, it is easy to define various model components concerning both model outputs and empirical data and to cover with both spatial and temporal correlation. Moreover, special sensitivity analysis techniques are developed for understanding both model components and mapping capability. The motivating application is the dynamical mapping of airborne particulate matters for risk monitoring using data from both a monitoring network and a computer model chain, which includes an emission, a meteorological and a chemical-transport module. Model estimation is determined by the Expectation-Maximization (EM) algorithm associated with simulation-based spatio-temporal parametric bootstrap. Applying sensitivity analysis techniques to the same hierarchical model provides interesting insights into the computer model chain.

Parametric uncertainty can represent parametric tolerance, parameter noise or parameter disturbances. The effects of these uncertainties on the time evolution of a system can be extremely significant, mostly when studying closed-loop operation of control systems. The presence of uncertainty makes the modeling process challenging, since it is impossible to express the behavior of the system with a deterministic approach. If the uncertainties can be defined in terms of probability density function, probabilistic approaches can be adopted. In many cases, the most useful aspect is the evaluation of the worst-case scenario, thus limiting the problem to the evaluation of the boundary of the set of solutions. This is particularly true for the analysis of robust stability and performance of a closed-loop system. The goal of this paper is to demonstrate how the polynomial chaos theory (PCT) can simplify the determination of the worst-case scenario, quickly providing the boundaries in time domain. The proposed approach is documented with examples and with the description of the Maple worksheet developed by the authors for the automatic processing in the PCT framework.