The Experts below are selected from a list of 53040 Experts worldwide ranked by ideXlab platform
Peter Denno - One of the best experts on this subject based on the ideXlab platform.
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Architecture Definition in Complex System Design Using Model Theory
IEEE Systems Journal, 2021Co-Authors: Charles E. Dickerson, Michael Wilkinson, Eugenie Hunsicker, Yves Bernard, Graham Bleakley, Peter DennoAbstract:Architecture Definition, which is central to system design, is one of the two most used technical processes in the practice of model-based systems engineering. In this article, a fundamental approach to Architecture Definition is presented and demonstrated. The success of its application to engineering problems depends on a precise but practical Definition of the term Architecture. In the standard for Architecture description, ISO/IEC/IEEE 42010:2011, a Definition was adopted that has been subsumed into later standards. In 2018, the working group JTC1/SC7/WG42 on system Architecture began a review of the adopted Definition, holding sessions late in the year. This article extends and complements a position paper submitted during the meetings, in which Tarski model theory and ISO/IEC 24707:2018 (logic-based languages) were used to better understand relationships between system models and concepts related to Architecture. Independent from the working group, it now contributes intuitive fundamental Definitions of the terms Architecture and system that are used to specify a mathematically based technical process for Architecture Definition. The engineering utility and benefits to complex system design are demonstrated in a diesel engine emissions reduction case study.
Charles E. Dickerson - One of the best experts on this subject based on the ideXlab platform.
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Architecture Definition in Complex System Design Using Model Theory
IEEE Systems Journal, 2021Co-Authors: Charles E. Dickerson, Michael Wilkinson, Eugenie Hunsicker, Yves Bernard, Graham Bleakley, Peter DennoAbstract:Architecture Definition, which is central to system design, is one of the two most used technical processes in the practice of model-based systems engineering. In this article, a fundamental approach to Architecture Definition is presented and demonstrated. The success of its application to engineering problems depends on a precise but practical Definition of the term Architecture. In the standard for Architecture description, ISO/IEC/IEEE 42010:2011, a Definition was adopted that has been subsumed into later standards. In 2018, the working group JTC1/SC7/WG42 on system Architecture began a review of the adopted Definition, holding sessions late in the year. This article extends and complements a position paper submitted during the meetings, in which Tarski model theory and ISO/IEC 24707:2018 (logic-based languages) were used to better understand relationships between system models and concepts related to Architecture. Independent from the working group, it now contributes intuitive fundamental Definitions of the terms Architecture and system that are used to specify a mathematically based technical process for Architecture Definition. The engineering utility and benefits to complex system design are demonstrated in a diesel engine emissions reduction case study.
Iman Poernomo - One of the best experts on this subject based on the ideXlab platform.
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reliability prediction for component based software Architectures
Journal of Systems and Software, 2003Co-Authors: Ralf Reussner, Heinz Schmidt, Iman PoernomoAbstract:One of the motivations for specifying software Architectures explicitly is the use of high level structural design information for improved control and prediction of software system quality attributes. In this paper, we present an approach for determining the reliability of component-based software Architectures.Our method is based on rich Architecture Definition language (RADL) oriented towards modem industrial middleware platforms, such as Microsoft's. NET and Sun's EJB. Our methods involve parameterised contractual specifications based on state machines and thus permits efficient static analysis.We show how RADL allows software architects to predict component reliability through compositional analysis of usage profiles and of environment component reliability. We illustrate our approach with an e-commerce example and report about empirical measurements which confirm our analytical reliability prediction through monitoring in our reliability test-bed. Our evaluation confirms that prediction accuracy for software components necessitates modelling the behaviour of binary components and the dependency of provided services on required components. Fortunately, our measurements also show that an abstract protocol view of that behaviour is sufficient to predict reliability with high accuracy. The reliability of a component most strongly depends on its environment. Therefore, we advocate a reliability model parameterized by required component reliability in a deployment context.
Xi Yang - One of the best experts on this subject based on the ideXlab platform.
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Software-Defined Network for End-to-end Networked Science at the Exascale
'Elsevier BV', 2020Co-Authors: Monga Inder, Guok Chin, Macauley John, Sim Alex, Newman Harvey, Balcas Justas, Demar Phil, Winkler Linda, Lehman Tom, Xi YangAbstract:Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based Architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an Architecture Definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence
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Software-Defined Network for End-to-end Networked Science at the Exascale
'Elsevier BV', 2020Co-Authors: Monga Inder, Guok Chin, Macauley John, Sim Alex, Newman Harvey, Balcas Justas, Demar Phil, Winkler Linda, Lehman Tom, Xi YangAbstract:Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based Architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an Architecture Definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.Comment: To appear in the journal of Future Generation Computer System
F Osnato - One of the best experts on this subject based on the ideXlab platform.
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mimo ofdm physical layer real time prototyping
Wireless Communications and Networking Conference, 2008Co-Authors: Lo D Iacono, Marco Ronchi, L D Torre, F OsnatoAbstract:Emerging wireless communication systems are rapidly moving toward Multiple-Input Multiple-Output (MIMO) technologies to guarantee either robustness and throughput as required by current broadband applications. Targeting a solution easy to integrate, MIMO technology can be conveniently adopted in combination with the largely known Orthogonal Frequency Division Multiplexing (OFDM), a modulation scheme combining high spectral efficiency with low complexity. MIMO and OFDM are the Physical (PHY) layer technologies selected by IEEE to drive the evolution of current Wireless-LAN 802.11 standards. Starting from a template Architecture Definition, a flexible MIMO W-LAN PHY layer based on IEEE 802.11n draft standard has been implemented and validated on a commercial FPGA-based prototyping platform embedding RF cards to prove on-air real-time capabilities. The overall RTL design has been then synthesized for the STMicroelectronics CMOS low-power 65 nm technology.