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Опубліковано: 14 січня 2023

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Статті в журналах з теми "Model at runtime":

1

Dokulil, Jiri. "Consistency model for runtime objects in the Open Community Runtime." Journal of Supercomputing 75, no. 5 (November 14, 2018): 2725–60. http://dx.doi.org/10.1007/s11227-018-2681-2.

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2

Maoz, Shahar. "Using Model-Based Traces as Runtime Models." Computer 42, no. 10 (October 2009): 28–36. http://dx.doi.org/10.1109/mc.2009.336.

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3

Al-Sayeh, Hani, Stefan Hagedorn, and Kai-Uwe Sattler. "A gray-box modeling methodology for runtime prediction of Apache Spark jobs." Distributed and Parallel Databases 38, no. 4 (March 10, 2020): 819–39. http://dx.doi.org/10.1007/s10619-020-07286-y.

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Abstract Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused.
4

Zhao, Yuhong, and Franz Rammig. "Model-based Runtime Verification Framework." Electronic Notes in Theoretical Computer Science 253, no. 1 (October 2009): 179–93. http://dx.doi.org/10.1016/j.entcs.2009.09.035.

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5

Bouhamed, Mohammed Mounir, Gregorio Díaz, Allaoua Chaoui, Oussama Kamel, and Radouane Nouara. "Models@Runtime: The Development and Re-Configuration Management of Python Applications Using Formal Methods." Applied Sciences 11, no. 20 (October 19, 2021): 9743. http://dx.doi.org/10.3390/app11209743.

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Models@runtime (models at runtime) are based on computation reflection. Runtime models can be regarded as a reflexive layer causally connected with the underlying system. Hence, every change in the runtime model involves a change in the reflected system, and vice versa. To the best of our knowledge, there are no runtime models for Python applications. Therefore, we propose a formal approach based on Petri Nets (PNs) to model, develop, and reconfigure Python applications at runtime. This framework is supported by a tool whose architecture consists of two modules connecting both the model and its execution. The proposed framework considers execution exceptions and allows users to monitor Python expressions at runtime. Additionally, the application behavior can be reconfigured by applying Graph Rewriting Rules (GRRs). A case study using Service-Level Agreement (SLA) violations is presented to illustrate our approach.
6

Ricks, Trenton M., Thomas E. Lacy, Brett A. Bednarcyk, Annika Robens-Radermacher, Evan J. Pineda, and Steven M. Arnold. "Solution of the Nonlinear High-Fidelity Generalized Method of Cells Micromechanics Relations via Order-Reduction Techniques." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3081078.

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The High-Fidelity Generalized Method of Cells (HFGMC) is one technique, distinct from traditional finite-element approaches, for accurately simulating nonlinear composite material behavior. In this work, the HFGMC global system of equations for doubly periodic repeating unit cells with nonlinear constituents has been reduced in size through the novel application of a Petrov-Galerkin Proper Orthogonal Decomposition order-reduction scheme in order to improve its computational efficiency. Order-reduced models of an E-glass/Nylon 12 composite led to a 4.8–6.3x speedup in the equation assembly/solution runtime while maintaining model accuracy. This corresponded to a 21–38% reduction in total runtime. The significant difference in assembly/solution and total runtimes was attributed to the evaluation of integration point inelastic field quantities; this step was identical between the unreduced and order-reduced models. Nonetheless, order-reduced techniques offer the potential to significantly improve the computational efficiency of multiscale calculations.
7

Ji-Wei, Liu, and Mao Xin-Jun. "Towards Dynamic Evolution of Runtime Variability Based on Computational Reflection." International Journal of Software Engineering and Knowledge Engineering 28, no. 03 (March 2018): 259–85. http://dx.doi.org/10.1142/s0218194018500092.

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Given the frequently changing nature of the user requirements and environments in software systems, runtime variability in today’s software systems should be capable of evolving during execution. Computational reflection is required to facilitate accessing and customizing runtime variability during this evolution process. However, realizing this computational reflection includes various practical complexities since the runtime variability is typically neither explicitly represented in software systems nor changeable during runtime. To address this problem, this paper proposes a software architecture to support computational reflection of runtime variability, along with a corresponding causal-connection mechanism to realize the introspection and intercession (i.e. representing runtime variability model, and adding, removing, replacing variability elements and their relations). The proposed software architecture consists of a meta level that represents runtime variability model using objectification, and a base level that organizes and manipulates the implementation of variability elements via reconfiguration. The causal-connection mechanism integrated in our proposed model is designed to synchronize the representation and the implementation. Further, we developed a Reflective Runtime Variability Framework (R2VF) to support the development and operation of the systems with the reflection of runtime variability. The effectiveness and applicability of our approach has been evaluated by applying R2VF to Personal Data Resource Network.
8

Li, Qiuying, Minyan Lu, Tingyang Gu, and Yumei Wu. "Runtime Software Architecture-Based Reliability Prediction for Self-Adaptive Systems." Symmetry 14, no. 3 (March 16, 2022): 589. http://dx.doi.org/10.3390/sym14030589.

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Modern software systems need to autonomously adapt their behavior at runtime in order to maintain their utility in response to continuous environmental changes. Most studies on models at runtime focus on providing suitable techniques to manage the complexity of software at runtime but neglect reliability caused by adaptation activities. Therefore, adaptive behaviors may lead to a decrease in reliability, which may result in severe financial loss or life damage. Runtime software architecture (RSA) is an abstract of a running system, which describes the elements of the current system, the states of these elements and the relation between the elements and their states at runtime. The main difference between RSA and software architecture at design time (DSA) is that RSA has a causal connection with the running system, whereas DSA does not. However, RSA and DSA have both symmetry and asymmetry in software architecture. To ensure that architecture-centric software can provide reliable services after adaptation adjustment, a method is proposed to analyze the impact of changes caused by adaptation strategy on the overall software reliability, which will be predicted at the runtime architecture model layer. Based on a Java platform, through non-intrusive monitoring, an RSA behavioral model is obtained followed by runtime reliability analysis model. Following this, reliability prediction results are obtained through a discrete-time Markov chain (DTMC). Finally, an experiment is conducted to verify the feasibility of the proposed method.
9

Yu, Haichao, Haoxiang Li, Humphrey Shi, Thomas S. Huang, and Gang Hua. "Any-Precision Deep Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10763–71. http://dx.doi.org/10.1609/aaai.v35i12.17286.

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We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-widths, by truncating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learning models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures and applicable to multiple vision tasks. Our code is released at https://github.com/SHI-Labs/Any-Precision-DNNs.
10

Búr, Márton, Gábor Szilágyi, András Vörös, and Dániel Varró. "Distributed graph queries over models@run.time for runtime monitoring of cyber-physical systems." International Journal on Software Tools for Technology Transfer 22, no. 1 (September 26, 2019): 79–102. http://dx.doi.org/10.1007/s10009-019-00531-5.

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Abstract Smart cyber-physical systems (CPSs) have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor, manage and control autonomous behavior. First, we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed, typed and attributed graphs as high-level knowledge representation. The runtime model is distributed among the participating nodes, and it is consistently kept up to date in a continuously evolving environment by a time-triggered model management protocol. Our runtime models offer a (domain-specific) model query and manipulation interface over the reliable communication middleware of the Data Distribution Service (DDS) standard widely used in the CPS domain. Then, we propose to carry out distributed runtime monitoring by capturing critical properties of interest in the form of graph queries, and design a distributed graph query evaluation algorithm for evaluating such graph queries over the distributed runtime model. As the key innovation, our (1) distributed runtime model extends existing publish–subscribe middleware (like DDS) used in real-time CPS applications by enabling the dynamic creation and deletion of graph nodes (without compile time limits). Moreover, (2) our distributed query evaluation extends existing graph query techniques by enabling query evaluation in a real-time, resource-constrained environment while still providing scalable performance. Our approach is illustrated, and an initial scalability evaluation is carried out on the MoDeS3 CPS demonstrator and the open Train Benchmark for graph queries.
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Статті в журналах Дисертації

Дисертації з теми "Model at runtime":

1

Werner, Christopher, Hendrik Schön, Thomas Kühn, Sebastian Götz, and Uwe Aßmann. "Role-based Runtime Model Synchronization." IEEE, 2018. https://tud.qucosa.de/id/qucosa%3A75310.

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Model-driven Software Development (MDSD) promotes the use of multiple related models to realize a software system systematically. These models usually contain redundant information but are independently edited. This easily leads to inconsistencies among them. To ensure consistency among multiple models, model synchronizations have to be employed, e.g., by means of model transformations, trace links, or triple graph grammars. Model synchronization poses three main problems for MDSD. First, classical model synchronization approaches have to be manually triggered to perform the synchronization. However, to support the consistent evolution of multiple models, it is necessary to immediately and continuously update all of them. Second, synchronization rules are specified at design time and, in classic approaches, cannot be extended at runtime, which is necessary if metamodels evolve at runtime. Finally, most classical synchronization approaches focus on bilateral model synchronization, i.e., the synchronization between two models. Consequently, for more than two models, they require the definition of pairwise model synchronizations leading to a combinatorial explosion of synchronization rules. To remedy these issues, we propose a role-based approach for runtime model synchronization. In particular, we propose role-based synchronization rules that enable the immediate and continuous propagation of changes to multiple interrelated models (and back again). Additionally, our approach permits adding new and customized synchronization rules at runtime. We illustrate the benefits of role-based runtime model synchronization using the Families to Persons case study from the Transformation Tool Contest 2017.
2

Saller, Karsten. "Model-Based Runtime Adaptation of Resource Constrained Devices." Phd thesis, Universitäts- und Landesbibliothek Darmstadt, 2015. https://tuprints.ulb.tu-darmstadt.de/4322/1/thesis_final_ULB.pdf.

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Dynamic Software Product Line (DSPL) engineering represents a promising approach for planning and applying runtime reconfiguration scenarios to self-adaptive software systems. Reconfigurations at runtime allow those systems to continuously adapt themselves to ever changing contextual requirements. With a systematic engineering approach such as DSPLs, a self-adaptive software system becomes more reliable and predictable. However, applying DSPLs in the vital domain of highly context-aware systems, e.g., mobile devices such as smartphones or tablets, is obstructed by the inherently limited resources. Therefore, mobile devices are not capable to handle large, constrained (re-)configuration spaces of complex self-adaptive software systems. The reconfiguration behavior of a DSPL is specified via so called feature models. However, the derivation of a reconfiguration based on a feature model (i) induces computational costs and (ii) utilizes the available memory. To tackle these drawbacks, I propose a model-based approach for designing DSPLs in a way that allows for a trade-off between pre-computation of reconfiguration scenarios at development time and on-demand evolution at runtime. In this regard, I intend to shift computational complexity from runtime to development time. Therefore, I propose the following three techniques for (1) enriching feature models with context information to reason about potential contextual changes, (2) reducing a DSPL specification w.r.t. the individual characteristics of a mobile device, and (3) specifying a context-aware reconfiguration process on the basis of a scalable transition system incorporating state space abstractions and incremental refinements at runtime. In addition to these optimization steps executed prior to runtime, I introduce a concept for (4) reducing the operational costs utilized by a reconfiguration at runtime on a long-term basis w.r.t. the DSPL transition system deployed on the device. To realize this concept, the DSPL transition system is enriched with non-functional properties, e.g., costs of a reconfiguration, and behavioral properties, e.g., the probability of a change within the contextual situation of a device. This provides the possibility to determine reconfigurations with minimum costs w.r.t. estimated long-term changes in the context of a device. The concepts and techniques contributed in this thesis are illustrated by means of a mobile device case study. Further, implementation strategies are presented and evaluated considering different trade-off metrics to provide detailed insights into benefits and drawbacks.
3

Mendonça, Danilo Filgueira. "Dependability verification for contextual/runtime goal modelling." reponame:Repositório Institucional da UnB, 2015. http://dx.doi.org/10.26512/2015.02.D.18158.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.
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Um contexto de operação estático não é a realidade para muitos sistemas de software atualmente. Variações de contextos impõe novos desafios ao desenvolvimento de sistemas seguros, o que inclui a ativação de falhas apenas em contextos específicos de operação. A engenharia de requisitos orientada a objetivos (GORE) explicita o ‘por quê’ dos requisitos de um sistema, isto é, a intencionalidade por trás de objetivos do sistema e os meios de se atingi-los. Um Runtime goal model (RGM) adiciona especificação de comportamento ao modelo de objetivos convencional, enquanto um Contextual goal model (CGM) especifica efeitos de contextos sobre objetivos, meios e métricas de qualidade. Visando uma verificação formal da dependabilidade de um Contextual-Runtime goal model (CRGM), nesse trabalho é proposta uma nova abordagem para a análise de dependabilidade orientada a objetivos baseada na técnica de verificação probabilística de modelos. Em particular, são definidas regras para a transformação de um CRGM para um modelo cadeia de Makov de tempo discreto (DTMC) com o qual se possa verificar a confiabilidade de se satisfazer um ou mais objetivos do sistema. Adicionalmente, para diminuir o esforço de análise e aumentar a usabilidade de nossa proposta, um gerador automatizado de código CRGM para DTMC foi implementado e integrado com sucesso à ferramenta gráfica que dá suporte às fases de modelagem e análise de objetivos da metodologia TROPOS. A verificação contextual de dependabilidade resultante reflete os requisitos no CRGM, que podem representar: o projeto de um sistema, cuja verificação ocorreria em fase de projetos; ou um sistema em execução, cujo comportamento pode ser verificado em tempo de execução como parte de uma análise de auto-adaptação com foco em dependabilidade.
A static and stable operation environment is not a reality for many systems nowadays. Context variations impose many threats to systems safety, including the activation of context specific failures. Goal-oriented requirements engineering (GORE) brings forward the ‘why’ of system requirements, i.e., the intentionality behind system goals and the means to meet then. A runtime goal model adds a behaviour specification layer to a conventional design goal model, and a contextual goal model specifies the context effects over system goals, means and qualitative metrics. In order to formally verify the dependability of a CRGM, we propose a new goal-oriented dependability analysis based on the probabilistic model checking technique. In particular, we define rules for the transformation of a CRGM into a DTMC model that can be verified for the reliability of the fulfilment of one or more system goals. Also, to mitigate the analysis overhead and increase the usability of our proposal, we have successfully implemented and integrated a CRGM to DTMC code generator to the graphical tool that supports the goal modelling and analysis phases of the TROPOS development methodology. The resulting contextual dependability verification reflects the system requirements in a CRGM, which may represent: a system-to-be, whose verification would take place at design-time; or a running system, whose behaviour can be verified at runtime as part of a self-adaptation analysis targeting dependability.
4

Jäkel, Tobias, Martin Weißbach, Kai Herrmann, Hannes Voigt, and Max Leuthäuser. "Position paper: Runtime Model for Role-based Software Systems." IEEE, 2016. https://tud.qucosa.de/id/qucosa%3A75302.

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In the increasingly dynamic realities of today's software systems, it is no longer feasible to always expect human developers to react to changing environments and changing conditions immediately. Instead, software systems need to be self-aware and autonomously adapt their behavior according to their experiences gathered from their environment. Current research provides role-based modeling as a promising approach to handle the adaptivity and self-awareness within a software system. There are established role-based systems e.g., for application development, persistence, and so on. However, these are isolated approaches using the role-based model on their specific layer and mapping to existing non-role-based layers. We present a global runtime model covering the whole stack of a software system to maintain a global view of the current system state and model the interdependencies between the layers. This facilitates building holistic role-based software systems using the role concept on every single layer to exploit its full potential, particularly adaptivity and self-awareness.
5

Nödtvedt, Sebastian. "CM model view transformations To support runtime forward/backward compatibility." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442392.

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The task to implement a solution of handling updates and version discrepancies within a testbeds Configuration Management. The Ericsson 5G testbed is built to support deployment of higher layer functions in a cloud environment. The benefits of using cloud deployments is mainly that it enables elastic application that can grow and shrink its footprint in runtime to adjust the capacity according to the traffic load. Schema data associated with different versions of a document-oriented database within a cloud environment provides dynamic properties but what remains static and cumbersome is updating parts of the system. If one can resolve this is then newer versions of functions can be instantiated in runtime and in parallel with older versions which partially can remove the need for application and system upgrades. However, this puts completely new demands on the architecture and how supportfunctions are designed. One such support function is the configuration managementfunction which in the current 5G testbed system is seen as an infrastructure function that can be replaced and upgraded independently of other running traffic applications.This requires handling of forward and backwards compatibility between the configuration management function and traffic functions that consume the configuration data. In this report a prototype was constructed and tested, the prototype consists of mainly two core components. Firstly a Wizard which handles two different versions of a model and generates a transformation schema, this is then passed to the Transformation which does the needed data transformation for compatibility. The Wizard starts by ensuring the required data is compatible and additionally acts as ainteractive tool for a operator, providing a overview and insight into the datatransformation. A solution within the frames of being a proof of concept was successfully implemented and demonstrated, inherent limitations where taken into account in the design. In conclusion a feasible solution is possible to implement for resolving version management within a system like the 5G testbed, which reduces a otherwise slow and error prone manual process.
6

Kotrajaras, Vishnu. "Towards an improved memory model for Java." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272386.

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7

Zhang, Minjia. "Efficient Runtime Support for Reliable and Scalable Parallelism." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469557197.

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8

Vogel, Thomas, and Holger Giese. "Model-driven engineering of adaptation engines for self-adaptive software : executable runtime megamodels." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6382/.

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The development of self-adaptive software requires the engineering of an adaptation engine that controls and adapts the underlying adaptable software by means of feedback loops. The adaptation engine often describes the adaptation by using runtime models representing relevant aspects of the adaptable software and particular activities such as analysis and planning that operate on these runtime models. To systematically address the interplay between runtime models and adaptation activities in adaptation engines, runtime megamodels have been proposed for self-adaptive software. A runtime megamodel is a specific runtime model whose elements are runtime models and adaptation activities. Thus, a megamodel captures the interplay between multiple models and between models and activities as well as the activation of the activities. In this article, we go one step further and present a modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that considerably eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular for feedback loops. Megamodels are kept explicit and alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops, their runtime models, and adaptation activities explicit at a higher level of abstraction. Moreover, it enables complex solutions where multiple feedback loops interact or even operate on top of each other. Finally, it leverages the co-existence of self-adaptation and off-line adaptation for evolution.
Die Entwicklung selbst-adaptiver Software erfordert die Konstruktion einer sogenannten "Adaptation Engine", die mittels Feedbackschleifen die unterliegende Software steuert und anpasst. Die Anpassung selbst wird häufig mittels Laufzeitmodellen, die die laufende Software repräsentieren, und Aktivitäten wie beispielsweise Analyse und Planung, die diese Laufzeitmodelle nutzen, beschrieben. Um das Zusammenspiel zwischen Laufzeitmodellen und Aktivitäten systematisch zu erfassen, wurden Megamodelle zur Laufzeit für selbst-adaptive Software vorgeschlagen. Ein Megamodell zur Laufzeit ist ein spezielles Laufzeitmodell, dessen Elemente Aktivitäten und andere Laufzeitmodelle sind. Folglich erfasst ein Megamodell das Zusammenspiel zwischen verschiedenen Laufzeitmodellen und zwischen Aktivitäten und Laufzeitmodellen als auch die Aktivierung und Ausführung der Aktivitäten. Darauf aufbauend präsentieren wir in diesem Artikel eine Modellierungssprache für ausführbare Megamodelle zur Laufzeit, EUREMA genannt, die aufgrund eines modellgetriebenen Ansatzes die Entwicklung selbst-adaptiver Software erleichtert. Der Ansatz umfasst eine domänen-spezifische Modellierungssprache und einen Laufzeit-Interpreter für Adaptation Engines, insbesondere für Feedbackschleifen. EUREMA Megamodelle werden über die Spezifikationsphase hinaus explizit zur Laufzeit genutzt, um mittels Interpreter Feedbackschleifen direkt auszuführen. Zusätzlich können Megamodelle zur Laufzeit dynamisch geändert werden, um Feedbackschleifen anzupassen. Daher unterstützt EUREMA die Entwicklung selbst-adaptiver Software durch die explizite Spezifikation von Feedbackschleifen, der verwendeten Laufzeitmodelle, und Adaptionsaktivitäten auf einer höheren Abstraktionsebene. Darüber hinaus ermöglicht EUREMA komplexe Lösungskonzepte, die mehrere Feedbackschleifen und deren Interaktion wie auch die hierarchische Komposition von Feedbackschleifen umfassen. Dies unterstützt schließlich das integrierte Zusammenspiel von Selbst-Adaption und Wartung für die Evolution der Software.
9

Hallou, Nabil. "Runtime optimization of binary through vectorization transformations." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S120/document.

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Les applications ne sont pas toujours optimisées pour le matériel sur lequel elles s'exécutent, comme les logiciels distribués sous forme binaire, ou le déploiement des programmes dans des fermes de calcul. On se concentre sur la maximisation de l'efficacité du processeur pour les extensions SIMD. Nous montrons que de nombreuses boucles compilées pour x86 SSE peuvent être converties dynamiquement en versions AVX plus récentes et plus puissantes. Nous obtenons des accélérations conformes à celles d'un compilateur natif ciblant AVX. De plus, on vectorise en temps réel des boucles scalaires. Nous avons intégré des logiciels libres pour (1) transformer dynamiquement le binaire vers la forme de représentation intermédiaire, (2) abstraire et vectoriser les boucles fréquemment exécutées dans le modèle polyédrique (3) enfin les compiler. Les accélérations obtenues sont proches du nombre d'éléments pouvant être traités simultanément par l'unité SIMD
In many cases, applications are not optimized for the hardware on which they run. This is due to backward compatibility of ISA that guarantees the functionality but not the best exploitation of the hardware. Many reasons contribute to this unsatisfying situation such as legacy code, commercial code distributed in binary form, or deployment on compute farms. Our work focuses on maximizing the CPU efficiency for the SIMD extensions. The first contribution is a lightweight binary translation mechanism that does not include a vectorizer, but instead leverages what a static vectorizer previously did. We show that many loops compiled for x86 SSE can be dynamically converted to the more recent and more powerful AVX; as well as, how correctness is maintained with regards to challenges such as data dependencies and reductions. We obtain speedups in line with those of a native compiler targeting AVX. The second contribution is a runtime auto-vectorization of scalar loops. For this purpose, we use open source frame-works that we have tuned and integrated to (1) dynamically lift the x86 binary into the Intermediate Representation form of the LLVM compiler, (2) abstract hot loops in the polyhedral model, (3) use the power of this mathematical framework to vectorize them, and (4) finally compile them back into executable form using the LLVM Just-In-Time compiler. In most cases, the obtained speedups are close to the number of elements that can be simultaneously processed by the SIMD unit. The re-vectorizer and auto-vectorizer are implemented inside a dynamic optimization platform; it is completely transparent to the user, does not require any rewriting of the binaries, and operates during program execution
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Kabir, Sohag, I. Sorokos, K. Aslansefat, Y. Papadopoulos, Y. Gheraibia, J. Reich, M. Saimler, and R. Wei. "A Runtime Safety Analysis Concept for Open Adaptive Systems." Springer, 2019. http://hdl.handle.net/10454/17416.

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Анотація:
No
In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architecture reconfiguration. Given the safety-critical nature of the applications involved, an approach for addressing safety in the context of reconfiguration impacting functional and non-functional properties at runtime is needed. In this paper, we introduce a concept for runtime safety analysis and decision input for open adaptive systems. We combine static safety analysis and evidence collected during operation to analyse, reason and provide online recommendations to minimize deviation from a system’s safe states. We illustrate our concept via an abstract vehicle platooning system use case.
This conference paper is available to view at http://hdl.handle.net/10454/17415.
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Книги з теми "Model at runtime":

1

RV 2009 (2009 Grenoble, France). Runtime verification: 9th international workshop, RV 2009, Grenoble, France, June 26-28, 2009 : selected papers. Berlin: Springer, 2009.

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2

Modeling And Verification Using Uml Statecharts A Working Guide To Reactive System Design Runtime Monitoring And Executionbased Model Checking. Newnes, 2006.

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3

Drusinsky, Doron. Modeling and Verification Using UML Statecharts: A Working Guide to Reactive System Design, Runtime Monitoring and Execution-Based Model Checking. Elsevier Science & Technology Books, 2006.

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4

Drusinsky, Doron. Modeling and Verification Using UML Statecharts: A Working Guide to Reactive System Design, Runtime Monitoring and Execution-Based Model Checking. Elsevier Science & Technology Books, 2011.

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5

Center, Goddard Space Flight, ed. AEOSS runtime manual for system analysis on Advanced Earth-Orbital Spacecraft Systems. Greenbelt, MD: National Aeronautics and Space Administration, Goddard Space Flight Center, 1990.

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Частини книг з теми "Model at runtime":

1

Legay, Axel, Benoît Delahaye, and Saddek Bensalem. "Statistical Model Checking: An Overview." In Runtime Verification, 122–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16612-9_11.

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2

Leucker, Martin. "Sliding between Model Checking and Runtime Verification." In Runtime Verification, 82–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35632-2_10.

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3

Bulychev, Peter, Alexandre David, Kim G. Larsen, Axel Legay, Guangyuan Li, and Danny Bøgsted Poulsen. "Rewrite-Based Statistical Model Checking of WMTL." In Runtime Verification, 260–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35632-2_25.

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4

Shankar, Saumya, Antoine Rollet, Srinivas Pinisetty, and Yliès Falcone. "Bounded-Memory Runtime Enforcement." In Model Checking Software, 114–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15077-7_7.

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Kejstová, Katarína, Petr Ročkai, and Jiří Barnat. "From Model Checking to Runtime Verification and Back." In Runtime Verification, 225–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67531-2_14.

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6

Hansen, Jeffery P., Sagar Chaki, Scott Hissam, James Edmondson, Gabriel A. Moreno, and David Kyle. "Input Attribution for Statistical Model Checking Using Logistic Regression." In Runtime Verification, 185–200. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46982-9_12.

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7

Desai, Ankush, Tommaso Dreossi, and Sanjit A. Seshia. "Combining Model Checking and Runtime Verification for Safe Robotics." In Runtime Verification, 172–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67531-2_11.

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8

Kyle, David, Jeffery Hansen, and Sagar Chaki. "Statistical Model Checking of Distributed Adaptive Real-Time Software." In Runtime Verification, 269–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23820-3_17.

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9

Nouri, Ayoub, Balaji Raman, Marius Bozga, Axel Legay, and Saddek Bensalem. "Faster Statistical Model Checking by Means of Abstraction and Learning." In Runtime Verification, 340–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11164-3_28.

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Shijubo, Junya, Masaki Waga, and Kohei Suenaga. "Efficient Black-Box Checking via Model Checking with Strengthened Specifications." In Runtime Verification, 100–120. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88494-9_6.

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Тези доповідей конференцій з теми "Model at runtime":

1

Szvetits, Michael, and Uwe Zdun. "Reusable event types for models at runtime to support the examination of runtime phenomena." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338230.

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2

Åkesson, Alfred, Görel Hedin, Niklas Fors, Rene Schöne, and Johannes Mey. "Runtime modeling and analysis of IoT systems." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3417990.3421397.

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3

Kirsch, Christoph M., Luis Lopes, Eduardo R. B. Marques, and Ana Sokolova. "Runtime Programming through Model-Preserving, Scalable Runtime Patches." In 2011 11th International Conference on Application of Concurrency to System Design (ACSD). IEEE, 2011. http://dx.doi.org/10.1109/acsd.2011.28.

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4

Krikava, Filip, Romain Rouvoy, and Lionel Seinturier. "Infrastructure as runtime models: Towards Model-Driven resource management." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338240.

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5

Brand, Thomas, and Holger Giese. "Modeling Approach and Evaluation Criteria for Adaptable Architectural Runtime Model Instances." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00006.

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6

Zhou, Liang, and Jian Cao. "Runtime Configurable Service Process Model." In 2010 IEEE 13th International Conference on Computational Science and Engineering (CSE). IEEE, 2010. http://dx.doi.org/10.1109/cse.2010.55.

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7

Jordan, Herbert, Thomas Heller, Philipp Gschwandtner, Peter Zangerl, Peter Thoman, Dietmar Fey, and Thomas Fahringer. "The AllScale Runtime Application Model." In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00088.

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Mayerhofer, Tanja, Philip Langer, and Gerti Kappel. "A runtime model for fUML." In the 7th Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2422518.2422527.

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Werner, Christopher, Hendrik Schon, Thomas Kuhn, Sebastian Gotz, and Uwe Assmann. "Role-Based Runtime Model Synchronization." In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2018. http://dx.doi.org/10.1109/seaa.2018.00057.

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10

Bencomo, Nelly, and Luis H. Garcia Paucar. "RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00005.

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Звіти організацій з теми "Model at runtime":

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
2

Baader, Franz, and Marcel Lippmann. Runtime Verification Using a Temporal Description Logic Revisited. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.203.

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Formulae of linear temporal logic (LTL) can be used to specify (wanted or unwanted) properties of a dynamical system. In model checking, the system’s behaviour is described by a transition system, and one needs to check whether all possible traces of this transition system satisfy the formula. In runtime verification, one observes the actual system behaviour, which at any point in time yields a finite prefix of a trace. The task is then to check whether all continuations of this prefix to a trace satisfy (violate) the formula. More precisely, one wants to construct a monitor, i.e., a finite automaton that receives the finite prefix as input and then gives the right answer based on the state currently reached. In this paper, we extend the known approaches to LTL runtime verification in two directions. First, instead of propositional LTL we use the more expressive temporal logic ALC-LTL, which can use axioms of the Description Logic (DL) ALC instead of propositional variables to describe properties of single states of the system. Second, instead of assuming that the observed system behaviour provides us with complete information about the states of the system, we assume that states are described in an incomplete way by ALC-knowledge bases. We show that also in this setting monitors can effectively be constructed. The (double-exponential) size of the constructed monitors is in fact optimal, and not higher than in the propositional case. As an auxiliary result, we show how to construct Büchi automata for ALC-LTL-formulae, which yields alternative proofs for the known upper bounds of deciding satisfiability in ALC-LTL.
3

Borgwardt, Stefan, and Barbara Morawska. Finding Finite Herbrand Models. Technische Universität Dresden, 2011. http://dx.doi.org/10.25368/2022.182.

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We show that finding finite Herbrand models for a restricted class of first-order clauses is ExpTime-complete. A Herbrand model is called finite if it interprets all predicates by finite subsets of the Herbrand universe. The restricted class of clauses consists of anti-Horn clauses with monadic predicates and terms constructed over unary function symbols and constants. The decision procedure can be used as a new goal-oriented algorithm to solve linear language equations and unification problems in the description logic FL₀. The new algorithm has only worst-case exponential runtime, in contrast to the previous one which was even best-case exponential.
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Chowdhury, Omar, Limin Jia, Deepak Garg, and Anupam Datta. Temporal Mode-Checking for Runtime Monitoring of Privacy Policies. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada609113.

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5

Michalakes, J. Runtime system library for parallel finite difference models with nesting. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/471385.

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Gao, Guang, Benoit Meister, David Padua, and Andres Marquez. Final Project Report, DynAX Innovations in Programming Models, Compilers and Runtime Systems for Dynamic Adaptive Event Driven Execution Models. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1238249.

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