2022
Conference article
Open Access
Solution bundles of Markov performability models through adaptive cross approximation
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
A technique to approximate solution bundles, i.e., solutions of a parametric model where parameters are treated as independent variables instead of constants, is presented for Markov models. Analyses based on an approximated solution bundle are more efficient than those that solve the model for all combinations of parameters' values separately. In this paper the idea is to properly adapt low rank tensor approximation techniques, and in particular Adaptive Cross Approximation, to the evaluation of performability attributes. Application on exemplary case studies confirms the advantages of the new solution technique with respect to solving the model for all time and parameters' combinations.Source: DSN 2022 - 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 381–392, Baltimora, USA, 27-30/06/2022
DOI: 10.1109/dsn53405.2022.00046
Metrics:
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
A technique to approximate solution bundles, i.e., solutions of a parametric model where parameters are treated as independent variables instead of constants, is presented for Markov models. Analyses based on an approximated solution bundle are more efficient than those that solve the model for all combinations of parameters' values separately. In this paper the idea is to properly adapt low rank tensor approximation techniques, and in particular Adaptive Cross Approximation, to the evaluation of performability attributes. Application on exemplary case studies confirms the advantages of the new solution technique with respect to solving the model for all time and parameters' combinations.Source: DSN 2022 - 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 381–392, Baltimora, USA, 27-30/06/2022
DOI: 10.1109/dsn53405.2022.00046
Metrics:
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ISTI Repository 
| doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
Implicit reward structures for implicit reliability models
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method.Source: IEEE transactions on reliability (2022). doi:10.1109/TR.2022.3190915
DOI: 10.1109/tr.2022.3190915
Metrics:
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method.Source: IEEE transactions on reliability (2022). doi:10.1109/TR.2022.3190915
DOI: 10.1109/tr.2022.3190915
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
TAPAS: a tool for stochastic evaluation of large interdependent composed models with absorbing states
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
TAPAS is a new tool for efficient evaluation of dependability and performability attributes of systems composed of many interconnected components. The tool solves homogeneous continuous time Markov chains described by stochastic automata network models structured in submodels with absorbing states. The measures of interest are defined by a reward structure based on submodels composed through transition-based synchronization. The tool has been conceived in a modular and flexible fashion, to easily accommodate new features. Currently, it implements an array of state-based solvers that addresses the state explosion problem through powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. A simple, yet representative, case study is adopted, to present the tool and to show the feasibility of the supported methods, in particular frommemory consumption point of view.Source: Performance evaluation review 49 (2022): 41–46. doi:10.1145/3543146.3543157
DOI: 10.1145/3543146.3543157
Metrics:
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
TAPAS is a new tool for efficient evaluation of dependability and performability attributes of systems composed of many interconnected components. The tool solves homogeneous continuous time Markov chains described by stochastic automata network models structured in submodels with absorbing states. The measures of interest are defined by a reward structure based on submodels composed through transition-based synchronization. The tool has been conceived in a modular and flexible fashion, to easily accommodate new features. Currently, it implements an array of state-based solvers that addresses the state explosion problem through powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. A simple, yet representative, case study is adopted, to present the tool and to show the feasibility of the supported methods, in particular frommemory consumption point of view.Source: Performance evaluation review 49 (2022): 41–46. doi:10.1145/3543146.3543157
DOI: 10.1145/3543146.3543157
Metrics:
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ISTI Repository | dl.acm.org | ACM SIGMETRICS Performance Evaluation Review | CNR ExploRA
2022
Journal article
Open Access
Redundancy-based intrusion tolerance approaches moving from classical fault tolerance methods
Di Giandomenico F., Masetti G., Chiaradonna S.
Borrowing from well known fault tolerant approaches based on redundancy to mask the effect of faults, redundancy-based intrusion tolerance schemes are proposed in this paper, where redundancy of ICT components is exploited as a first defense line against a subset of compromised components within the redundant set, due to cyberattacks. Features to enhance defense and tolerance capabilities are first discussed, covering diversity-based redundancy, confusion techniques, protection mechanisms, locality policies and rejuvenation phases. Then, a set of intrusion tolerance variations of classical fault tolerant schemes (including N Version Programming and Recovery Block, as well as a few hybrid approaches) is proposed, by enriching each original scheme with one or more of the previously introduced defense mechanisms. As a practical support to the system designer in making an appropriate choice among the available solutions, for each developed scheme a schematic summary is provided, in terms of resources and defense facilities needed to tolerate f value failures and k omission failures, as well as observations regarding time requirements. To provide an example of more detailed analysis, useful to set up an appropriate intrusion tolerance configuration, a trade-off study between cost and additional redundancy employed for confusion purposes is also carried out.Source: International Journal of Applied Mathematics and Computer Science 32 (2022): 701–719. doi:10.34768/amcs-2022-0048
DOI: 10.34768/amcs-2022-0048
Project(s): BIECO
Metrics:
Di Giandomenico F., Masetti G., Chiaradonna S.
Borrowing from well known fault tolerant approaches based on redundancy to mask the effect of faults, redundancy-based intrusion tolerance schemes are proposed in this paper, where redundancy of ICT components is exploited as a first defense line against a subset of compromised components within the redundant set, due to cyberattacks. Features to enhance defense and tolerance capabilities are first discussed, covering diversity-based redundancy, confusion techniques, protection mechanisms, locality policies and rejuvenation phases. Then, a set of intrusion tolerance variations of classical fault tolerant schemes (including N Version Programming and Recovery Block, as well as a few hybrid approaches) is proposed, by enriching each original scheme with one or more of the previously introduced defense mechanisms. As a practical support to the system designer in making an appropriate choice among the available solutions, for each developed scheme a schematic summary is provided, in terms of resources and defense facilities needed to tolerate f value failures and k omission failures, as well as observations regarding time requirements. To provide an example of more detailed analysis, useful to set up an appropriate intrusion tolerance configuration, a trade-off study between cost and additional redundancy employed for confusion purposes is also carried out.Source: International Journal of Applied Mathematics and Computer Science 32 (2022): 701–719. doi:10.34768/amcs-2022-0048
DOI: 10.34768/amcs-2022-0048
Project(s): BIECO
Metrics:
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ISTI Repository | www.amcs.uz.zgora.pl | doi.org | CNR ExploRA
2022
Conference article
Open Access
Random bad state estimator to address false data injection in critical infrastructures
Masetti G., Chiaradonna S., Robol L., Di Giandomenico F.
Given their crucial role for a society and economy, an essential component of critical infrastructures is the Bad State Estimator (BSE), responsible for detecting malfunctions affecting elements of the physical infrastructure. In the past, the BSE has been conceived to mainly cope with accidental faults, under assumptions characterizing their occurrence. However, evolution of the addressed systems category consisting in pervasiveness of ICT-based control towards increasing smartness, paired with the openness of the operational environment, contributed to expose critical infrastructures to intentional attacks, e.g. exploited through False Data Injection (FDI). In the flow of studies focusing on enhancements of the traditional BSE to account for FDI attacks, this paper proposes a new solution that introduces randomness elements in the diagnosis process, to improve detection abilities and mitigate potentially catastrophic common-mode errors. Differently from existing alternatives, the strength of this new technique is that it does not require any additional components or alternative source of information with respect to the classic BSE. Numerical experiments conducted on two IEEE transmission grid tests, taken as representative use cases, show the applicability and benefits of the new solution.Source: PRDC 2022 - 27th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 98–108, Beijing, China, 28/11/2022 - 01/12/2022
DOI: 10.1109/prdc55274.2022.00024
Metrics:
Masetti G., Chiaradonna S., Robol L., Di Giandomenico F.
Given their crucial role for a society and economy, an essential component of critical infrastructures is the Bad State Estimator (BSE), responsible for detecting malfunctions affecting elements of the physical infrastructure. In the past, the BSE has been conceived to mainly cope with accidental faults, under assumptions characterizing their occurrence. However, evolution of the addressed systems category consisting in pervasiveness of ICT-based control towards increasing smartness, paired with the openness of the operational environment, contributed to expose critical infrastructures to intentional attacks, e.g. exploited through False Data Injection (FDI). In the flow of studies focusing on enhancements of the traditional BSE to account for FDI attacks, this paper proposes a new solution that introduces randomness elements in the diagnosis process, to improve detection abilities and mitigate potentially catastrophic common-mode errors. Differently from existing alternatives, the strength of this new technique is that it does not require any additional components or alternative source of information with respect to the classic BSE. Numerical experiments conducted on two IEEE transmission grid tests, taken as representative use cases, show the applicability and benefits of the new solution.Source: PRDC 2022 - 27th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 98–108, Beijing, China, 28/11/2022 - 01/12/2022
DOI: 10.1109/prdc55274.2022.00024
Metrics:
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2021
Journal article
Open Access
Enhancing sustainability of the railway infrastructure: Trading energy saving and unavailability through efficient switch heating policies
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: Sustainable computing: informatics and systems (Print) 30 (2021). doi:10.1016/j.suscom.2021.100519
DOI: 10.1016/j.suscom.2021.100519
Metrics:
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: Sustainable computing: informatics and systems (Print) 30 (2021). doi:10.1016/j.suscom.2021.100519
DOI: 10.1016/j.suscom.2021.100519
Metrics:
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ISTI Repository | Sustainable Computing Informatics and Systems | www.sciencedirect.com | CNR ExploRA
2021
Journal article
Open Access
On identity-aware replication in stochastic modeling for simulation-based dependability analysis of large interconnected systems
Chiaradonna S., Di Giandomenico F., Masetti G.
This paper focuses on the generation of stochastic models for dependability and performability analysis, through mechanisms for the automatic replication of template models when identity of replicas cannot be anonymous. The major objective of this work is to support the modeler in selecting the most appropriate replication mechanism, given the characteristics of the system under analysis. To this purpose, three most used solutions to identity-aware replication are considered and a formal framework to allow representing them in a consistent way is first defined. Then, a comparison of their behavior is extensively carried out, with focus on efficiency, both from a theoretical perspective and from a quantitative viewpoint. For the latter, a specific implementation of the considered replication mechanisms in the Möbius modeling environment and a case study representative of realistic interconnected infrastructures are developed.Source: Performance evaluation 147 (2021). doi:10.1016/j.peva.2021.102192
DOI: 10.1016/j.peva.2021.102192
Metrics:
Chiaradonna S., Di Giandomenico F., Masetti G.
This paper focuses on the generation of stochastic models for dependability and performability analysis, through mechanisms for the automatic replication of template models when identity of replicas cannot be anonymous. The major objective of this work is to support the modeler in selecting the most appropriate replication mechanism, given the characteristics of the system under analysis. To this purpose, three most used solutions to identity-aware replication are considered and a formal framework to allow representing them in a consistent way is first defined. Then, a comparison of their behavior is extensively carried out, with focus on efficiency, both from a theoretical perspective and from a quantitative viewpoint. For the latter, a specific implementation of the considered replication mechanisms in the Möbius modeling environment and a case study representative of realistic interconnected infrastructures are developed.Source: Performance evaluation 147 (2021). doi:10.1016/j.peva.2021.102192
DOI: 10.1016/j.peva.2021.102192
Metrics:
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ISTI Repository | Performance Evaluation | www.sciencedirect.com | CNR ExploRA
2021
Contribution to conference
Open Access
TAPAS: a tool for stochastic evaluation of large interdependent composed models with absorbing states
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
TAPAS is a new tool for efficient evaluation of dependability and performability attributes of systems composed of many interconnected components. The tool solves homogeneous continuous time Markov chains described by stochastic automata network models structured in submodels with absorbing states. The measures of interest are defined by a reward structure based on submodels composed through transition-based synchronization. The tool has been conceived in a modular and flexible fashion, to easily accommodate new features. Currently, it implements an array of state-based solvers that addresses the state explosion problem through powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. A simple, yet representative, case study is adopted, to present the tool and to show the feasibility of the supported methods, in particular from memory consumption point of view.Source: TOSME Workshop - Tools for Stochastic Modelling and Evaluation (performance, dependability, security and verification), Online workshop, 12/11/2021
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
TAPAS is a new tool for efficient evaluation of dependability and performability attributes of systems composed of many interconnected components. The tool solves homogeneous continuous time Markov chains described by stochastic automata network models structured in submodels with absorbing states. The measures of interest are defined by a reward structure based on submodels composed through transition-based synchronization. The tool has been conceived in a modular and flexible fashion, to easily accommodate new features. Currently, it implements an array of state-based solvers that addresses the state explosion problem through powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. A simple, yet representative, case study is adopted, to present the tool and to show the feasibility of the supported methods, in particular from memory consumption point of view.Source: TOSME Workshop - Tools for Stochastic Modelling and Evaluation (performance, dependability, security and verification), Online workshop, 12/11/2021
See at:
ISTI Repository | www.performance2021.deib.polimi.it | CNR ExploRA
2020
Conference article
Open Access
Trading dependability and energy consumption in critical infrastructures: Focus on the rail switch heating system
Chiaradonna S., Di Giandomenico F., Masetti G.
Traditionally, critical infrastructures demand for high dependability, being the services they provide essential to human beings and the society at large. However, more recent attention to cautious usage of energy resources is changing this vision and calls for solutions accounting for appropriate multi-requirements combinations when developing a critical infrastructure. In such a context, analysis supports able to assist the designer in envisioning a satisfactory trade-off among the multi-requirements for the system at hand are highly helpful. In this paper, the focus is on the railway sector and the contribution is a stochastic model-based analysis framework to quantitatively assess trade-offs between dependability indicators and electrical energy consumption incurred by the rail switch heating system.Moving from a preliminary study that concentrated on energy consumption only, the analysis framework has been extended to become a solid support to devise appropriate tuning of the heating policy that guarantees satisfactory trade-offs between dependability and energy consumption. An evaluation campaign in a variety of climate scenarios demonstrates the feasibility and utility of the developed framework.Source: 25th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 150–159, Perth, Australia, 01/12/2021
DOI: 10.1109/prdc50213.2020.00026
Metrics:
Chiaradonna S., Di Giandomenico F., Masetti G.
Traditionally, critical infrastructures demand for high dependability, being the services they provide essential to human beings and the society at large. However, more recent attention to cautious usage of energy resources is changing this vision and calls for solutions accounting for appropriate multi-requirements combinations when developing a critical infrastructure. In such a context, analysis supports able to assist the designer in envisioning a satisfactory trade-off among the multi-requirements for the system at hand are highly helpful. In this paper, the focus is on the railway sector and the contribution is a stochastic model-based analysis framework to quantitatively assess trade-offs between dependability indicators and electrical energy consumption incurred by the rail switch heating system.Moving from a preliminary study that concentrated on energy consumption only, the analysis framework has been extended to become a solid support to devise appropriate tuning of the heating policy that guarantees satisfactory trade-offs between dependability and energy consumption. An evaluation campaign in a variety of climate scenarios demonstrates the feasibility and utility of the developed framework.Source: 25th IEEE Pacific Rim International Symposium on Dependable Computing, pp. 150–159, Perth, Australia, 01/12/2021
DOI: 10.1109/prdc50213.2020.00026
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Contribution to conference
Open Access
Enhancing sustainability of the railway infrastructure through efficient energy management policies
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: 11th International Green and Sustainable Computing Conference, Virtual Conference, 19/10/2020, 22/10/2020
Chiaradonna S., Masetti G., Di Giandomenico F., Righetti F., Vallati C.
Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.Source: 11th International Green and Sustainable Computing Conference, Virtual Conference, 19/10/2020, 22/10/2020
See at:
ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
On Extending and Comparing Newton-Raphson Variants for Solving Power-Flow Equations
Dutto S., Masetti G., Chiaradonna S., Di Giandomenico F.
This paper focuses on power-flow equations solutions, based on the Newton-Raphson method. Two major contributions are offered. First, the definition of novel solution variants, resorting to Wirtinger calculus, is attempted. The obtained developments, although original in their formulation, led to already known variants. Despite the impaired originality of the obtained solution, there are significant lessons learned from such an effort. The second major contribution consists of a deep comparison analysis of existing solution strategies, based on complex and real variables, and the Wirtinger based ones, all properly reformulated to allow direct comparison with each other. The goal is to investigate strengths and weaknesses of the addressed techniques in terms of computational effort and convergence rate, which are the most relevant aspects to consider while choosing the approach to employ to solve power-flow equations for a specific power system under study.Source: IEEE transactions on power systems 34 (2019): 2577–2587. doi:10.1109/TPWRS.2019.2897640
DOI: 10.1109/tpwrs.2019.2897640
Metrics:
Dutto S., Masetti G., Chiaradonna S., Di Giandomenico F.
This paper focuses on power-flow equations solutions, based on the Newton-Raphson method. Two major contributions are offered. First, the definition of novel solution variants, resorting to Wirtinger calculus, is attempted. The obtained developments, although original in their formulation, led to already known variants. Despite the impaired originality of the obtained solution, there are significant lessons learned from such an effort. The second major contribution consists of a deep comparison analysis of existing solution strategies, based on complex and real variables, and the Wirtinger based ones, all properly reformulated to allow direct comparison with each other. The goal is to investigate strengths and weaknesses of the addressed techniques in terms of computational effort and convergence rate, which are the most relevant aspects to consider while choosing the approach to employ to solve power-flow equations for a specific power system under study.Source: IEEE transactions on power systems 34 (2019): 2577–2587. doi:10.1109/TPWRS.2019.2897640
DOI: 10.1109/tpwrs.2019.2897640
Metrics:
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ISTI Repository | IEEE Transactions on Power Systems | ieeexplore.ieee.org | CNR ExploRA
2019
Conference article
Open Access
Stochastic modeling and evaluation of large interdependent composed models through Kronecker algebra and exponential sums
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
The KAES methodology for efficient evaluation of dependability-related properties is proposed. KAES targets systems representable by Stochastic Petri Nets-based models, composed by a large number of submodels where interconnections are managed through synchronization at action level. The core of KAES is a new numerical solution of the underlying CTMC process, based on powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. Specifically, advancing on existing literature, KAES addresses efficient evaluation of the Mean-Time-To-Absorption in CTMC with absorbing states, exploiting the basic idea to further pursue the symbolic representation of the elements involved in the evaluation process, so to better cope with the problem of state explosion. As a result, computation efficiency is improved, especially when the submodels are loosely interconnected and have small number of states. An instrumental case study is adopted, to show the feasibility of KAES, in particular from memory consumption point of view.Source: The 40th International Conference on Application and Theory of Petri Nets and Concurrency, pp. 47–66, Berlin, 23-28/06/2019
DOI: 10.1007/978-3-030-21571-2_3
Metrics:
Masetti G., Robol L., Chiaradonna S., Di Giandomenico F.
The KAES methodology for efficient evaluation of dependability-related properties is proposed. KAES targets systems representable by Stochastic Petri Nets-based models, composed by a large number of submodels where interconnections are managed through synchronization at action level. The core of KAES is a new numerical solution of the underlying CTMC process, based on powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. Specifically, advancing on existing literature, KAES addresses efficient evaluation of the Mean-Time-To-Absorption in CTMC with absorbing states, exploiting the basic idea to further pursue the symbolic representation of the elements involved in the evaluation process, so to better cope with the problem of state explosion. As a result, computation efficiency is improved, especially when the submodels are loosely interconnected and have small number of states. An instrumental case study is adopted, to show the feasibility of KAES, in particular from memory consumption point of view.Source: The 40th International Conference on Application and Theory of Petri Nets and Concurrency, pp. 47–66, Berlin, 23-28/06/2019
DOI: 10.1007/978-3-030-21571-2_3
Metrics:
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arpi.unipi.it | link.springer.com | ISTI Repository | doi.org | CNR ExploRA
2019
Contribution to book
Open Access
A Refined Framework for Model-Based Assessment of Energy Consumption in the Railway Sector
Chiaradonna S., Di Giandomenico F., Masetti G., Basile D.
Awareness and efforts to moderate energy consumption, desirable from both economical and environmental perspectives, are nowadays increasingly pursued. However, when critical sectors are addressed, energy saving should be cautiously tackled, so to not impair stringent dependability properties such contexts typically require. This is the case of the railway transportation system, which is the critical infrastructure this paper focuses on. For this system category, the attitude has been typically to neglect efficient usage of energy sources, motivated by avoiding to put dependability in danger. The new directives, both at national and international level, are going to change this way of thinking. Our study intends to be a useful support to careful energy consumption. In particular, a refined stochastic modeling framework is offered, tailored to the railroad switch heating system, through which analyses can be performed to understand the sophisticated dynamics between the system (both the cyber and physical components) and the surrounding weather conditions.Source: From Software Engineering to Formal Methods and Tools, and Back. Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday, edited by Maurice H. ter Beek, Alessandro Fantechi, Laura Semini, pp. 481–501, 2019
DOI: 10.1007/978-3-030-30985-5_28
Metrics:
Chiaradonna S., Di Giandomenico F., Masetti G., Basile D.
Awareness and efforts to moderate energy consumption, desirable from both economical and environmental perspectives, are nowadays increasingly pursued. However, when critical sectors are addressed, energy saving should be cautiously tackled, so to not impair stringent dependability properties such contexts typically require. This is the case of the railway transportation system, which is the critical infrastructure this paper focuses on. For this system category, the attitude has been typically to neglect efficient usage of energy sources, motivated by avoiding to put dependability in danger. The new directives, both at national and international level, are going to change this way of thinking. Our study intends to be a useful support to careful energy consumption. In particular, a refined stochastic modeling framework is offered, tailored to the railroad switch heating system, through which analyses can be performed to understand the sophisticated dynamics between the system (both the cyber and physical components) and the surrounding weather conditions.Source: From Software Engineering to Formal Methods and Tools, and Back. Essays Dedicated to Stefania Gnesi on the Occasion of Her 65th Birthday, edited by Maurice H. ter Beek, Alessandro Fantechi, Laura Semini, pp. 481–501, 2019
DOI: 10.1007/978-3-030-30985-5_28
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2018
Conference article
Open Access
An efficient strategy for model composition in the Möbius modeling environment
Masetti G., Chiaradonna S., Di Giandomenico F., Feddersen B., Sanders W. H.
Möbius is well known as a modeling and evaluation environment for performance and dependability indicators. One of Möbius' key features is the modular and compositional approach to model definition and analysis. In particular, the modeler can define submodels using several formalisms and compose them to form the overall model of the system under analysis. The current algorithm for model composition in Möbius revealed performance issues when large systems are considered (such as in the modeling of realistic segments of energy or transportation infrastructures), due to the chosen data flow scheme. In this paper, a new algorithm for the same composition mechanism is proposed to improve efficiency. A case study is also developed to demonstrate the performance enhancements.Source: 14th European Dependable Computing Conference 2018, pp. 116–119, Iasi, Romania, 10-14 September, 2018
DOI: 10.1109/edcc.2018.00029
Metrics:
Masetti G., Chiaradonna S., Di Giandomenico F., Feddersen B., Sanders W. H.
Möbius is well known as a modeling and evaluation environment for performance and dependability indicators. One of Möbius' key features is the modular and compositional approach to model definition and analysis. In particular, the modeler can define submodels using several formalisms and compose them to form the overall model of the system under analysis. The current algorithm for model composition in Möbius revealed performance issues when large systems are considered (such as in the modeling of realistic segments of energy or transportation infrastructures), due to the chosen data flow scheme. In this paper, a new algorithm for the same composition mechanism is proposed to improve efficiency. A case study is also developed to demonstrate the performance enhancements.Source: 14th European Dependable Computing Conference 2018, pp. 116–119, Iasi, Romania, 10-14 September, 2018
DOI: 10.1109/edcc.2018.00029
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ieeexplore.ieee.org | ISTI Repository | doi.org | CNR ExploRA
2018
Conference article
Open Access
Supporting CPS modeling through a new method for solving complex non-holomorphic equations
Masetti G., Dutto S., Chiaradonna S., Di Giandomenico F.
Modeling cyber-physical systems (CPSs) for assessment or design support purposes is a complex activity. Capturing all relevant physical, structural or behavioral aspects of the system at hand is a crucial task, which often implies representation of peculiar features/constraints through non-linear equations. Values that fulfill the constraints, described with a domain specific language, are obtained solving the equations through a properly developed solution tool. Only for a limited set of CPSs it is possible to find a straightforward strategy to design the software that solves the constraints equations. In the general case, instead, the modeler has to develop an ad-hoc artifact for each different system. This is the case of non-holomorphic but real analytic complex equations, adopted to represent system components with wave behaviors. In this paper, we present a new approach to develop a software for solving such complex equations following a generative programming strategy, based on Wirtinger derivatives within the Newton-Raphson method.Source: 6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD, pp. 680–688, Madeira, Portugal, 22-24 January, 2018
DOI: 10.5220/0006752306800688
Metrics:
Masetti G., Dutto S., Chiaradonna S., Di Giandomenico F.
Modeling cyber-physical systems (CPSs) for assessment or design support purposes is a complex activity. Capturing all relevant physical, structural or behavioral aspects of the system at hand is a crucial task, which often implies representation of peculiar features/constraints through non-linear equations. Values that fulfill the constraints, described with a domain specific language, are obtained solving the equations through a properly developed solution tool. Only for a limited set of CPSs it is possible to find a straightforward strategy to design the software that solves the constraints equations. In the general case, instead, the modeler has to develop an ad-hoc artifact for each different system. This is the case of non-holomorphic but real analytic complex equations, adopted to represent system components with wave behaviors. In this paper, we present a new approach to develop a software for solving such complex equations following a generative programming strategy, based on Wirtinger derivatives within the Newton-Raphson method.Source: 6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD, pp. 680–688, Madeira, Portugal, 22-24 January, 2018
DOI: 10.5220/0006752306800688
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doi.org | ISTI Repository | www.scitepress.org | CNR ExploRA
2017
Conference article
Open Access
A stochastic modeling approach for an efficient dependability evaluation of large systems with non-anonymous interconnected components
Chiaradonna S., Di Giandomenico F., Masetti G.
This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, interconnected with each other according to a variety of topologies. A new efficient replication technique is presented and its implementation is discussed. The goal is to improve the performance of simulation solvers with respect to standard approaches, when employed in the modeling of the addressed class of systems, in particular for loosely interconnected system components (as typically encountered in the electrical or transportation sectors). Effectiveness of the new technique is demonstrated by comparison with a state of the art alternative solution on a representative case study.Source: Internationa Symposium on Software Reliability Engineering, pp. 46–55, Tolosa, Francia, 23-26/10/2017
DOI: 10.1109/issre.2017.17
Metrics:
Chiaradonna S., Di Giandomenico F., Masetti G.
This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, interconnected with each other according to a variety of topologies. A new efficient replication technique is presented and its implementation is discussed. The goal is to improve the performance of simulation solvers with respect to standard approaches, when employed in the modeling of the addressed class of systems, in particular for loosely interconnected system components (as typically encountered in the electrical or transportation sectors). Effectiveness of the new technique is demonstrated by comparison with a state of the art alternative solution on a representative case study.Source: Internationa Symposium on Software Reliability Engineering, pp. 46–55, Tolosa, Francia, 23-26/10/2017
DOI: 10.1109/issre.2017.17
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2017
Conference article
Restricted
Model-based simulation in möbius: An efficient approach targeting loosely interconnected components
Masetti G., Chiaradonna S., Di Giandomenico F.
This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models inside the Möbius modeling framework. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, loosely interconnected with each other (as typically encountered in the electrical or transportation sectors). The approach is based on models that define channels shared among replicas, used to exchange the values of each state variable of a replica with the other replicas that need to use them. The goal is to improve the performance of simulation based solvers with respect to the existing state-sharing approach, when employed in the modeling of the addressed class of systems. Simulation results for the time overheads induced by both channel-sharing and state-sharing approaches for different system scenarios are presented and discussed. They confirm the expected gain in efficiency of the proposed channel-sharing approach in the addressed system context.Source: European Workshop on Performance Engineering, pp. 184–198, Berlino, 7-8/09/2017
DOI: 10.1007/978-3-319-66583-2_12
Metrics:
Masetti G., Chiaradonna S., Di Giandomenico F.
This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models inside the Möbius modeling framework. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, loosely interconnected with each other (as typically encountered in the electrical or transportation sectors). The approach is based on models that define channels shared among replicas, used to exchange the values of each state variable of a replica with the other replicas that need to use them. The goal is to improve the performance of simulation based solvers with respect to the existing state-sharing approach, when employed in the modeling of the addressed class of systems. Simulation results for the time overheads induced by both channel-sharing and state-sharing approaches for different system scenarios are presented and discussed. They confirm the expected gain in efficiency of the proposed channel-sharing approach in the addressed system context.Source: European Workshop on Performance Engineering, pp. 184–198, Berlino, 7-8/09/2017
DOI: 10.1007/978-3-319-66583-2_12
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doi.org | link.springer.com | CNR ExploRA
2016
Report
Open Access
Stochastic model-based evaluation of reliable energy-saving rail road switch heating systems
Basile D., Chiaradonna S., Di Giandomenico F., Gnesi S.
Rail road switch heaters are used to avoid the formation of snow and ice on top of rail road switches during the cold season, in order to guarantee their correct functioning. Effective management of the energy consumption of these devices is important to reduce the costs and minimise the environmental impact. While doing so, it is critical to guarantee the reliability of the system. In this work we analyse reliability and energy consumption indicators for a system of (remotely controlled) rail road switch heaters by developing and solving a stochastic model-based approach based on the Stochastic Activity Networks (SAN) formalism. An on-off policy is considered for heating the switches, with parametric thresholds of activation/deactivation of the heaters and considering different classes of priority. A case study has been developed inspired by a real rail road station, to practically demonstrate the application of the proposed approach to understand the impact of different thresholds and priorities on the indicators under analysis (probability of failure and energy consumption).Source: ISTI Technical reports, 2016
Basile D., Chiaradonna S., Di Giandomenico F., Gnesi S.
Rail road switch heaters are used to avoid the formation of snow and ice on top of rail road switches during the cold season, in order to guarantee their correct functioning. Effective management of the energy consumption of these devices is important to reduce the costs and minimise the environmental impact. While doing so, it is critical to guarantee the reliability of the system. In this work we analyse reliability and energy consumption indicators for a system of (remotely controlled) rail road switch heaters by developing and solving a stochastic model-based approach based on the Stochastic Activity Networks (SAN) formalism. An on-off policy is considered for heating the switches, with parametric thresholds of activation/deactivation of the heaters and considering different classes of priority. A case study has been developed inspired by a real rail road station, to practically demonstrate the application of the proposed approach to understand the impact of different thresholds and priorities on the indicators under analysis (probability of failure and energy consumption).Source: ISTI Technical reports, 2016
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ISTI Repository | CNR ExploRA
2016
Conference article
Open Access
Quantification of the effectiveness of medium voltage control policies in smart grids
Chiaradonna S., Di Giandomenico F., Xiao J.
Electricity generation from renewable sources is increasing significantly, pushed by the need to meet sustainable energy goals in many countries. Control is a key enabling technology for the deployment of renewable energy systems in smart grids and to guarantee high-performance and reliable operation. To this purpose, a large variety of control strategies have been proposed or are still under investigation, posing the challenge of understanding the effectiveness of the individual solutions and their ability to face operation in critical scenarios, such as in presence of failures. In this paper we focus on the medium voltage grid control and propose a stochastic modeling framework appropriate to represent a variety of voltage control strategies and to quantify their effectiveness in terms of suitably defined metrics to trade among multiple requirements such as imposed grid stability, reliability and cost. We exercise the developed modeling framework on a benchmark grid to show concrete quantification of medium voltage control solutions in interesting grid scenarios.Source: IEEE 17th International Symposium on High Assurance Systems Engineering, pp. 284–291, Orlando, FL, USA, 7-9 January 2016
DOI: 10.1109/hase.2016.42
Project(s): SMARTC2NET
Metrics:
Chiaradonna S., Di Giandomenico F., Xiao J.
Electricity generation from renewable sources is increasing significantly, pushed by the need to meet sustainable energy goals in many countries. Control is a key enabling technology for the deployment of renewable energy systems in smart grids and to guarantee high-performance and reliable operation. To this purpose, a large variety of control strategies have been proposed or are still under investigation, posing the challenge of understanding the effectiveness of the individual solutions and their ability to face operation in critical scenarios, such as in presence of failures. In this paper we focus on the medium voltage grid control and propose a stochastic modeling framework appropriate to represent a variety of voltage control strategies and to quantify their effectiveness in terms of suitably defined metrics to trade among multiple requirements such as imposed grid stability, reliability and cost. We exercise the developed modeling framework on a benchmark grid to show concrete quantification of medium voltage control solutions in interesting grid scenarios.Source: IEEE 17th International Symposium on High Assurance Systems Engineering, pp. 284–291, Orlando, FL, USA, 7-9 January 2016
DOI: 10.1109/hase.2016.42
Project(s): SMARTC2NET
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ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2016
Conference article
Restricted
A stochastic modelling framework to analyze smart grids control strategies
Chiaradonna S., Di Giandomenico F., Masetti G.
Smart grids provide services at the basis of a number of application sectors, several of which are critical from the perspective of human life, environment or financials. It is therefore paramount to be assisted by technologies able to analyze the smart grid behavior in critical scenarios, e.g. where cyber malfunctions or grid disruptions occur. In this paper, we present a stochastic modelling framework to quantitatively assess representative indicators of the resilience and quality of service of the distribution grid, in presence of accidental faults and malicious attacks. The results from the performed analysis can be exploited to understand the dynamics of failures and to identify potential system vulnerabilities, against which appropriate countermeasures should be developed. The features of the proposed analysis framework are discussed, pointing out the strong non-linearity of the involved physics, the developed solutions to deal with control actions and the definition of indicators under analysis. A case study based on a real-world network is also presented.Source: SEGE 2016 - 4th IEEE International Conference on Smart Energy Grid Engineering, pp. 123–130, Oshawa, Canada, 21-24 August 2016
DOI: 10.1109/sege.2016.7589512
Metrics:
Chiaradonna S., Di Giandomenico F., Masetti G.
Smart grids provide services at the basis of a number of application sectors, several of which are critical from the perspective of human life, environment or financials. It is therefore paramount to be assisted by technologies able to analyze the smart grid behavior in critical scenarios, e.g. where cyber malfunctions or grid disruptions occur. In this paper, we present a stochastic modelling framework to quantitatively assess representative indicators of the resilience and quality of service of the distribution grid, in presence of accidental faults and malicious attacks. The results from the performed analysis can be exploited to understand the dynamics of failures and to identify potential system vulnerabilities, against which appropriate countermeasures should be developed. The features of the proposed analysis framework are discussed, pointing out the strong non-linearity of the involved physics, the developed solutions to deal with control actions and the definition of indicators under analysis. A case study based on a real-world network is also presented.Source: SEGE 2016 - 4th IEEE International Conference on Smart Energy Grid Engineering, pp. 123–130, Oshawa, Canada, 21-24 August 2016
DOI: 10.1109/sege.2016.7589512
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doi.org | ieeexplore.ieee.org | CNR ExploRA