The Experts below are selected from a list of 1998 Experts worldwide ranked by ideXlab platform
Xuemin Shen - One of the best experts on this subject based on the ideXlab platform.
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Dependability Analysis of Control Center Networks in Smart Grid Using Stochastic Petri Nets
IEEE Transactions on Parallel and Distributed Systems, 2012Co-Authors: Rongfei Zeng, Rongfei Zeng, Yixin Jiang, Chuang Lin, Chuang Lin, Xuemin Sherman Shen, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
Rongfei Zeng - One of the best experts on this subject based on the ideXlab platform.
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Dependability Analysis of Control Center Networks in Smart Grid Using Stochastic Petri Nets
IEEE Transactions on Parallel and Distributed Systems, 2012Co-Authors: Rongfei Zeng, Rongfei Zeng, Yixin Jiang, Chuang Lin, Chuang Lin, Xuemin Sherman Shen, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
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a stochastic petri nets approach to Dependability Analysis of control center networks in smart grid
International Conference on Wireless Communications and Signal Processing, 2011Co-Authors: Rongfei Zeng, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
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WCSP - A stochastic Petri nets approach to Dependability Analysis of control center networks in smart grid
2011 International Conference on Wireless Communications and Signal Processing (WCSP), 2011Co-Authors: Rongfei Zeng, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
Andrea Bobbio - One of the best experts on this subject based on the ideXlab platform.
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bayesian networks for Dependability Analysis an application to digital control reliability
arXiv: Artificial Intelligence, 2013Co-Authors: Luigi Portinale, Andrea BobbioAbstract:Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to their use in Dependability Analysis. The aim of this paper is to propose BN as a suitable tool for Dependability Analysis, by challenging the formalism with basic issues arising in Dependability tasks. We will discuss how both modeling and Analysis issues can be naturally dealt with by BN. Moreover, we will show how some limitations intrinsic to combinatorial Dependability methods such as Fault Trees can be overcome using BN. This will be pursued through the study of a real-world example concerning the reliability Analysis of a redundant digital Programmable Logic Controller (PLC) with majority voting 2:3
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Parametric Dependability Analysis through Probabilistic Horn Abduction
arXiv: Artificial Intelligence, 2012Co-Authors: Andrea Bobbio, Stefania Montani, Luigi PortinaleAbstract:Dependability modeling and evaluation is aimed at investigating that a system performs its function correctly in time. A usual way to achieve a high reliability, is to design redundant systems that contain several replicas of the same subsystem or component. State space methods for Dependability Analysis may suffer of the state space explosion problem in such a kind of situation. Combinatorial models, on the other hand, require the simplified assumption of statistical independence; however, in case of redundant systems, this does not guarantee a reduced number of modeled elements. In order to provide a more compact system representation, parametric system modeling has been investigated in the literature, in such a way that a set of replicas of a given subsystem is parameterized so that only one representative instance is explicitly included. While modeling aspects can be suitably addressed by these approaches, analytical tools working on parametric characterizations are often more difficult to be defined and the standard approach is to 'unfold' the parametric model, in order to exploit standard Analysis algorithms working at the unfolded 'ground' level. Moreover, parameterized combinatorial methods still require the statistical independence assumption. In the present paper we consider the formalism of Parametric Fault Tree (PFT) and we show how it can be related to Probabilistic Horn Abduction (PHA). Since PHA is a framework where both modeling and Analysis can be performed in a restricted first-order language, we aim at showing that converting a PFT into a PHA knowledge base will allow an approach to Dependability Analysis directly exploiting parametric representation. We will show that classical qualitative and quantitative Dependability measures can be characterized within PHA. Furthermore, additional modeling aspects (such as noisy gates and local dependencies) as well as additional reliability measures (such as posterior probability Analysis) can be naturally addressed by this conversion. A simple example of a multi-processor system with several replicated units is used to illustrate the approach.
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System-level Dependability Analysis
System-level Test and Validation of Hardware Software Systems, 2005Co-Authors: Andrea Bobbio, D. Codetta Raiteri, M. De Pierro, Giuliana FranceschinisAbstract:The focus of this work is on the Dependability Analysis of safety or mission-critical systems; in particular, we concentrate on the control subsystem, which is made up of several components. We assume that the components, which may be designed with the support of hardware—software codesign tools, are characterized by Dependability (e.g. failure rate) parameters, which may derive from simulators of the components while they are under development, or as a result of testing (possibly combined with fault injection techniques). By using combinatorial and state-space-based techniques it is possible to derive the reliability of the whole system as a function of the system configuration and of the component parameters values, and to identify the criticality of a given component or subset of components. The Analysis is performed by applying Fault Tree Analysis (FTA) techniques enhanced with recently introduced features that allow one to remove the components’ independence assumptions imposed by classical FTA, and to include the possibility of component as well as subsystem repair.
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comparing fault trees and bayesian networks for Dependability Analysis
International Conference on Computer Safety Reliability and Security, 1999Co-Authors: Andrea Bobbio, Luigi Portinale, Michele Minichino, Ester CiancamerlaAbstract:Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks and their suitability for Dependability Analysis is now considered by several researchers. In the present paper, we aim at defining a formal comparison between BN and one of the most popular techniques for Dependability Analysis: Fault Trees (FT). We will show that any FT can be easily mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed using the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc...). Moreover, we will discuss how, by using BN, some additional power can be obtained, both at the modeling and at the Analysis level. In particular, dependency among components and noisy gates can be easily accommodated in the BN framework, together with the possibility of performing general diagnostic Analysis. The comparison of the two methodologies is carried on through the Analysis of an example that consists of a redundant multiprocessor system, with local and shared memories, local mirrored disks and a single bus.
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SAFECOMP - Comparing Fault Trees and Bayesian Networks for Dependability Analysis
Computer Safety Reliability and Security, 1999Co-Authors: Andrea Bobbio, Luigi Portinale, Michele Minichino, Ester CiancamerlaAbstract:Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks and their suitability for Dependability Analysis is now considered by several researchers. In the present paper, we aim at defining a formal comparison between BN and one of the most popular techniques for Dependability Analysis: Fault Trees (FT). We will show that any FT can be easily mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed using the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc...). Moreover, we will discuss how, by using BN, some additional power can be obtained, both at the modeling and at the Analysis level. In particular, dependency among components and noisy gates can be easily accommodated in the BN framework, together with the possibility of performing general diagnostic Analysis. The comparison of the two methodologies is carried on through the Analysis of an example that consists of a redundant multiprocessor system, with local and shared memories, local mirrored disks and a single bus.
Yixin Jiang - One of the best experts on this subject based on the ideXlab platform.
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Dependability Analysis of Control Center Networks in Smart Grid Using Stochastic Petri Nets
IEEE Transactions on Parallel and Distributed Systems, 2012Co-Authors: Rongfei Zeng, Rongfei Zeng, Yixin Jiang, Chuang Lin, Chuang Lin, Xuemin Sherman Shen, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
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a stochastic petri nets approach to Dependability Analysis of control center networks in smart grid
International Conference on Wireless Communications and Signal Processing, 2011Co-Authors: Rongfei Zeng, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
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WCSP - A stochastic Petri nets approach to Dependability Analysis of control center networks in smart grid
2011 International Conference on Wireless Communications and Signal Processing (WCSP), 2011Co-Authors: Rongfei Zeng, Yixin Jiang, Xuemin ShenAbstract:As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and Dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the Dependability Analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the Dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the Dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the Dependability Analysis of control center networks in smart grid.
Rick Karcich - One of the best experts on this subject based on the ideXlab platform.
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A Hierarchical Approach for Dependability Analysis of a Commercial Cache-Based RAID Storage Architecture
1998Co-Authors: Mohamed Kaâniche, Zbigniew Kalbarczyk, Luigi Romano, Ravishankar Iyer, Rick KarcichAbstract:We present a hierarchical simulation approach for the Dependability Analysis and evaluation of a highly available commercial cache-based RAID storage system. The archi-tecture is complex and includes several layers of overlap-ping error detection and recovery mechanisms. Three ab-straction levels have been developed to model the cache architecture, cache operations, and error detection and recovery mechanism. The impact of faults and errors oc-curring in the cache and in the disks is analyzed at each level of the hierarchy. A simulation submodel is associated with each abstraction level. The models have been devel-oped using DEPEND, a simulation-based environment for system-level Dependability Analysis, which provides facili-ties to inject faults into a functional behavior model, to simulate error detection and recovery mechanisms, and to evaluate quantitative measures. Several fault models are defined for each submodel to simulate cache component failures, disk failures, transmission errors, and data errors in the cache memory and in the disks. Some of the parame-ters characterizing fault injection in a given submodel cor-respond to probabilities evaluated from the simulation of the lower-level submodel. Based on the proposed method-ology, we evaluate and analyze 1) the system behavior un-der a real workload and high error rate (focusing on error bursts), 2) the coverage of the error detection mechanisms implemented in the system and the error latency distribu-tions, and 3) the accumulation of errors in the cache and in the disks.
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FTCS - A hierarchical approach for Dependability Analysis of a commercial cache-based RAID storage architecture
Digest of Papers. Twenty-Eighth Annual International Symposium on Fault-Tolerant Computing (Cat. No.98CB36224), 1998Co-Authors: Mohamed Kaâniche, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Luigi Romano, Rick KarcichAbstract:We present a hierarchical simulation approach for the Dependability Analysis and evaluation of a highly available commercial cache-based RAID storage system. The architecture is complex and includes several layers of overlapping error detection and recovery mechanisms. Three abstraction levels have been developed to model the cache architecture, cache operations, and error detection and recovery mechanism. The impact of faults and errors occurring in the cache and in the disks is analyzed at each level of the hierarchy. A simulation submodel is associated with each abstraction level. The models have been developed using DEPEND, a simulation-based environment for system-level Dependability Analysis, which provides facilities to inject faults into a functional behavior model, to simulate error detection and recovery mechanisms, and to evaluate quantitative measures. Several fault models are defined for each submodel to simulate cache component failures, disk failures, transmission errors, and data errors in the cache memory and in the disks. Some of the parameters characterizing fault injection in a given submodel correspond to probabilities evaluated from the simulation of the lower-level submodel. Based on the proposed methodology, we evaluate and analyze: the system behavior under a real workload and high error rate (focusing on error bursts); the coverage of the error detection mechanisms implemented in the system and the error latency distributions; and the accumulation of errors in the cache and in the disks.