Journal articles: 'Model at runtime'

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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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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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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.

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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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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.

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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.

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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.

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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.

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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.

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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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Incki, Koray, and Ismail Ari. "Model-Based Runtime Monitoring of Smart City Systems." Procedia Computer Science 134 (2018): 75–82. http://dx.doi.org/10.1016/j.procs.2018.07.146.

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Chen, Xing, Aipeng Li, Xue’e Zeng, Wenzhong Guo, and Gang Huang. "Runtime model based approach to IoT application development." Frontiers of Computer Science 9, no. 4 (June 6, 2015): 540–53. http://dx.doi.org/10.1007/s11704-015-4362-0.

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Haustein, Stefan, and Joerg Pleumann. "A model-driven runtime environment for Web applications." Software & Systems Modeling 4, no. 4 (June 15, 2005): 443–58. http://dx.doi.org/10.1007/s10270-005-0093-2.

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Ma, Nan, Yingxing Lin, and Xiang Zhou. "WiFi Sensor Network Management Based On Runtime Model." International Journal of Future Generation Communication and Networking 9, no. 2 (February 28, 2016): 269–80. http://dx.doi.org/10.14257/ijfgcn.2016.9.2.27.

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Elmas, Tayfun, and Serdar Tasiran. "VyrdMC: Driving Runtime Refinement Checking with Model Checkers." Electronic Notes in Theoretical Computer Science 144, no. 4 (May 2006): 41–56. http://dx.doi.org/10.1016/j.entcs.2006.02.003.

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Guimarães, Felipe Pontes, Genaína Nunes Rodrigues, Raian Ali, and Daniel Macêdo Batista. "Planning runtime software adaptation through pragmatic goal model." Data & Knowledge Engineering 109 (May 2017): 25–40. http://dx.doi.org/10.1016/j.datak.2017.03.003.

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Rudy, Jarosław. "Dynamic Random-Access Stored-Program Machine for Runtime Code Modification." International Journal of Foundations of Computer Science 26, no. 04 (June 2015): 441–63. http://dx.doi.org/10.1142/s0129054115500240.

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This paper is concerned with the study of possibility of performing changes to existing running programs with the use of the RAM and RASP models of computation. A new model of computation is defined with the capability of performing runtime changes. Theoretical properties, including time and space complexities, of the defined models are presented and proven. A number of simple empirical tests are conducted in order to prove the ability to perform runtime changes as well as support obtained theoretical results. The paper concludes that the defined model has virtually no affect on performance when there are no changes and the performance with changes is easily manageable. Moreover, the results can be used to develop runtime change capabilities for a wide range of programming languages and paradigms.

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Jabla, Roua, Maha Khemaja, Félix Buendia, and Sami Faiz. "Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments." Applied Sciences 11, no. 22 (November 15, 2021): 10770. http://dx.doi.org/10.3390/app112210770.

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Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

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Schmitz, Oliver, Elga Salvadore, Lien Poelmans, Johannes van der Kwast, and Derek Karssenberg. "A framework to resolve spatio-temporal misalignment in component-based modelling." Journal of Hydroinformatics 16, no. 4 (December 6, 2013): 850–71. http://dx.doi.org/10.2166/hydro.2013.180.

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Process-based spatio-temporal component models simulate real world processes, using encapsulated process representations that operate at individual spatial and temporal discretisations. These component models act as building blocks in the construction of multi-disciplinary, multi-scale integrated models. Coupling these independent component models, however, involves aggregation or disaggregation of the exchanged variables at model runtime, since each of the component models exposes potentially different spatial and temporal discretisations. Although conceptual methodologies for spatial and temporal scaling are available, dedicated tools that assist modellers to implement dynamic spatial and temporal scaling operations are rare. We present the accumulator, a programmable general-purpose model building block executing custom scaling operations at model runtime. We therefore characterise runtime information of input and output variables required for the implementation of scaling operations between component models with different discretisations. The accumulator is a component of an integrated modelling framework and can be completed by the modeller with custom operations for spatial and temporal scaling. To illustrate the applicability of the accumulators an integrated model is developed that couples an existing land use change model and hydrological component models at different spatial and temporal scales. The accumulators as building blocks allow modellers to construct multi-scale integrated models in a flexible manner.

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Zhou, Ge, Chunzheng Yang, Peng Lu, and Xi Chen. "Runtime verification in uncertain environment based on probabilistic model learning." Mathematical Biosciences and Engineering 19, no. 12 (2022): 13607–27. http://dx.doi.org/10.3934/mbe.2022635.

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<abstract><p>Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem. To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the $ true $, $ false $ and $ inconclusive $ in three-valued semantics. They cannot give accurate quantitative description of correctness when $ inconclusive $ is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such a method, one can give early warning once the probability of correctness is lower than a pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.</p></abstract>

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Zhang, Jie, Cong Tian, Zhenhua Duan, and Liang Zhao. "RTPDroid: Detecting Implicitly Malicious Behaviors Under Runtime Permission Model." IEEE Transactions on Reliability 70, no. 3 (September 2021): 1295–308. http://dx.doi.org/10.1109/tr.2021.3078628.

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LIU, Tao, Bin FAN, Cheng-Yong WU, and Zhao-Qing ZHANG. "Dataflow-Style Java Parallel Programming Model and Runtime Optimization." Journal of Software 19, no. 9 (September 20, 2008): 2181–90. http://dx.doi.org/10.3724/sp.j.1001.2008.02181.

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Sarikaya, Ruhi, Canturk Isci, and Alper Buyuktosunoglu. "Runtime Application Behavior Prediction Using a Statistical Metric Model." IEEE Transactions on Computers 62, no. 3 (March 2013): 575–88. http://dx.doi.org/10.1109/tc.2012.25.

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Li, X., X. Qiu, L. Wang, X. Chen, Z. Zhou, L. Yu, and J. Zhao. "UML interaction model-driven runtime verification of Java programs." IET Software 5, no. 2 (2011): 142. http://dx.doi.org/10.1049/iet-sen.2009.0009.

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Pezzé, Mauro, and Jochen Wuttke. "Model-driven generation of runtime checks for system properties." International Journal on Software Tools for Technology Transfer 18, no. 1 (June 24, 2014): 1–19. http://dx.doi.org/10.1007/s10009-014-0325-2.

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Yu, X., and T. Zhang. "Convergence and Runtime of an Ant Colony Optimization Model." Information Technology Journal 8, no. 3 (March 15, 2009): 354–59. http://dx.doi.org/10.3923/itj.2009.354.359.

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Zhao, Y., S. Oberthür, M. Kardos, and F. J. Rammig. "Model-based Runtime Verification Framework for Self-optimizing Systems." Electronic Notes in Theoretical Computer Science 144, no. 4 (May 2006): 125–45. http://dx.doi.org/10.1016/j.entcs.2006.02.008.

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Anda, Amal Ahmed, and Daniel Amyot. "Goal and Feature Model Optimization for the Design and Self-Adaptation of Socio-Cyber-Physical Systems." Journal of Integrated Design and Process Science 25, no. 2 (May 30, 2022): 141–77. http://dx.doi.org/10.3233/jid210022.

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Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.

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Xiang, Chengcheng, Zhengwei Qi, and Walter Binder. "Flexible and Extensible Runtime Verification for Java (Extended Version)." International Journal of Software Engineering and Knowledge Engineering 25, no. 09n10 (November 2015): 1595–609. http://dx.doi.org/10.1142/s0218194015400343.

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Runtime verification validates the correctness of a program’s execution trace. Much work has been done on improving the expressiveness and efficiency of runtime verification. However, current approaches require static deployment of the verification logic and are often restricted to a limited set of events that can be captured and analyzed, hindering the adoption of runtime verification in production systems. A popular system for runtime verification in Java, JavaMOP (Monitor-Oriented Programming in Java), suffers from the aforementioned limitations due to its dependence on AspectJ, which supports neither dynamic weaving nor an extensible join-point model. In this article, we extend the JavaMOP framework with a dynamic deployment API and a new MOP specification translator, which targets the domain-specific aspect language DiSL instead of AspectJ; DiSL offers an open join-point model that allows for extensions. A case study on lambda expressions in Java8 demonstrates the extensibility of our approach. Moreover, in comparison with JavaMOP using load-time weaving, our implementation reduces runtime overhead by 32%, and heap memory usage by 13%, on average.

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Kern, Bastian, and Patrick Jöckel. "A diagnostic interface for the ICOsahedral Non-hydrostatic (ICON) modelling framework based on the Modular Earth Submodel System (MESSy v2.50)." Geoscientific Model Development 9, no. 10 (October 13, 2016): 3639–54. http://dx.doi.org/10.5194/gmd-9-3639-2016.

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Abstract. Numerical climate and weather models have advanced to finer scales, accompanied by large amounts of output data. The model systems hit the input and output (I/O) bottleneck of modern high-performance computing (HPC) systems. We aim to apply diagnostic methods online during the model simulation instead of applying them as a post-processing step to written output data, to reduce the amount of I/O. To include diagnostic tools into the model system, we implemented a standardised, easy-to-use interface based on the Modular Earth Submodel System (MESSy) into the ICOsahedral Non-hydrostatic (ICON) modelling framework. The integration of the diagnostic interface into the model system is briefly described. Furthermore, we present a prototype implementation of an advanced online diagnostic tool for the aggregation of model data onto a user-defined regular coarse grid. This diagnostic tool will be used to reduce the amount of model output in future simulations. Performance tests of the interface and of two different diagnostic tools show, that the interface itself introduces no overhead in form of additional runtime to the model system. The diagnostic tools, however, have significant impact on the model system's runtime. This overhead strongly depends on the characteristics and implementation of the diagnostic tool. A diagnostic tool with high inter-process communication introduces large overhead, whereas the additional runtime of a diagnostic tool without inter-process communication is low. We briefly describe our efforts to reduce the additional runtime from the diagnostic tools, and present a brief analysis of memory consumption. Future work will focus on optimisation of the memory footprint and the I/O operations of the diagnostic interface.

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Chiu, Yuan-Shyi Peter, Yunsen Wang, Tsu-Ming Yeh, and Singa Wang Chiu. "Fabrication runtime decision for a hybrid system incorporating probabilistic breakdowns, scrap, and overtime." International Journal of Industrial Engineering Computations 13, no. 3 (2022): 293–308. http://dx.doi.org/10.5267/j.ijiec.2022.4.001.

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Manufacturers today need to optimize their fabrication runtime decision by meeting short customer order due dates externally and managing the potentially unreliable machines and manufacturing processes internally. Outsourcing and overtime are commonly utilized strategies to expedite fabricating time. Additionally, detailed analyses and necessary actions on inevitable product defects (i.e., removal of scraps) and equipment breakdowns (such as machine repairing) are prerequisites to fabrication runtime planning. Motivated by assisting today’s manufacturers decide the best batch runtime plan under the situations mentioned above, this study applies mathematical modeling to a hybrid fabrication problem that incorporates partial overtime and outsourcing, inevitable product defects, and a Poisson-distributed breakdown. We develop a model to accurately represent the problem’s characteristics. Formulations and detailed model analyses allow us to find the cost function first. Differential equations and algorithms help us confirm the gain function’s convexity and find the best runtime decision. Lastly, we use numerical illustrations to show our study’s applicability by revealing in-depth crucial managerial information of the studied problem.

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ARBELAEZ, ALEJANDRO, CHARLOTTE TRUCHET, and PHILIPPE CODOGNET. "Using sequential runtime distributions for the parallel speedup prediction of SAT local search." Theory and Practice of Logic Programming 13, no. 4-5 (July 2013): 625–39. http://dx.doi.org/10.1017/s1471068413000392.

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AbstractThis paper presents a detailed analysis of the scalability and parallelization of local search algorithms for the Satisfiability problem. We propose a framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequential version. Indeed, by approximating the runtime distribution of the sequential process with statistical methods, the runtime behavior of the parallel process can be predicted by a model based on order statistics. We apply this approach to study the parallel performance of two SAT local search solvers, namely Sparrow and CCASAT, and compare the predicted performances to the results of an actual experimentation on parallel hardware up to 384 cores. We show that the model is accurate and predicts performance close to the empirical data. Moreover, as we study different types of instances (random and crafted), we observe that the local search solvers exhibit different behaviors and that their runtime distributions can be approximated by two types of distributions: exponential (shifted and non-shifted) and lognormal.

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LI, CHEN, MANFRED REICHERT, and ANDREAS WOMBACHER. "THE MINADEPT CLUSTERING APPROACH FOR DISCOVERING REFERENCE PROCESS MODELS OUT OF PROCESS VARIANTS." International Journal of Cooperative Information Systems 19, no. 03n04 (September 2010): 159–203. http://dx.doi.org/10.1142/s0218843010002139.

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During the last years a new generation of adaptive Process-Aware Information Systems (PAIS) has emerged, which enables dynamic process changes at runtime, while preserving PAIS robustness and consistency. Such adaptive PAIS allow authorized users to add new process activities, to delete existing activities, or to change pre-defined activity sequences during runtime. Both this runtime flexibility and process configurations at build-time, lead to a large number of process variants being derived from the same process model, but slightly differing in structure due to the applied changes. Generally, process variants are expensive to configure and difficult to maintain. This paper presents selected results from our MinAdept project. In particular, we provide a clustering algorithm that fosters learning from past process changes by mining a collection of process variants. As mining result we obtain a process model for which average distance to the process variant models becomes minimal. By adopting this process model as reference model in the PAIS, need for future process configuration and adaptation decreases. We have validated our clustering algorithm by means of a case study as well as comprehensive simulations. Altogether, our vision is to enable full process lifecycle support in adaptive PAIS.

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Keogh, Kathleen, and Liz Sonenberg. "Designing Multi-Agent System Organisations for Flexible Runtime Behaviour." Applied Sciences 10, no. 15 (August 2, 2020): 5335. http://dx.doi.org/10.3390/app10155335.

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We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.

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Dong, Zhijiang, Yujian Fu, and Yue Fu. "Runtime Verification on Robotics Systems." International Journal of Robotics Applications and Technologies 3, no. 1 (January 2015): 23–40. http://dx.doi.org/10.4018/ijrat.2015010102.

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Runtime verification is a technique for generating monitors from formal specification of expected behaviors for the underlying system. It can be applied to automatically evaluate system execution, either on-line or off-line, analyzing extracted execution traces; or it can be used online during operation, potentially steering the application back to a safety region if a property is violated. As a so-called light-weighted formal method, runtime verification bridges the gap between system design and implementation and shorten the distance of software quality assurance between the software testing and model checking and theorem proving. Runtime verification is considered as a highly scalable and automatic technique. Most of current runtime verification research are endeavored on the program context, in other words, on the program side and falls in the implementation level. These applications limited the benefits of runtime verification that bridges the gap among types of applications. With the proliferation of embedded systems and mobile device, dynamically verifying the firmware and mobile apps becomes a new emerging area. Due to the characteristics of runtime verification technique and limitations of the robotics systems, so far, very few research and project are located in the runtime verification on the firmware of embedded systems, which appear in most of robotics systems. Robotics systems are programmed on the firmware and only observed on device. In this paper, the authors first discussed the current runtime verifications on the embedded systems with limitations. After that, a layered runtime verification framework will be presented for the firmware verification. The case study is applied on the commonly recognized educational toolkit – LEGO Mindstorm robotics systems.

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36

Park, Jin-Hyeok, Khurshedjon Farkhodov, Suk-Hwan Lee, and Ki-Ryong Kwon. "Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation." Applied Sciences 12, no. 7 (March 22, 2022): 3220. http://dx.doi.org/10.3390/app12073220.

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The complexity of object tracking models among hardware applications has become a more in-demand task to accomplish with multifunctional algorithm skills in various indeterminable environment tracking conditions. Experimenting with the virtual realistic simulator brings new dependencies and requirements, which may cause problems while experimenting with runtime processing. The goal of this paper is to present an object tracking framework that differs from the most advanced tracking models by experimenting with virtual environment simulation (Aerial Informatics and Robotics Simulation—AirSim, City Environ) using one of the Deep Reinforcement Learning Models named as Deep Q-Learning algorithms. Our proposed network examines the environment using a deep reinforcement learning model to regulate activities in the virtual simulation environment and utilizes sequential pictures from the realistic VCE (Virtual City Environ) model as inputs. Subsequently, the deep reinforcement network model was pretrained using multiple sequential training image sets and fine-tuned for adaptability during runtime tracking. The experimental results were outstanding in terms of speed and accuracy. Moreover, we were unable to identify any results that could be compared to the state-of-the-art methods that use deep network-based trackers in runtime simulation platforms, since this testing experiment was conducted on the two public datasets VisDrone2019 and OTB-100, and achieved better performance among compared conventional methods.

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37

Xu, Da, Yuting Ye, Chuanwei Ruan, and Bo Yang. "Towards Robust Off-Policy Learning for Runtime Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10101–9. http://dx.doi.org/10.1609/aaai.v36i9.21249.

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Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interventions and constraints that cause inconsistency between the online and offline setting, which we summarize and term as runtime uncertainty. Such uncertainty cannot be learned from the logged data due to its abnormality and rareness nature. To assert a certain level of robustness, we perturb the off-policy estimators along an adversarial direction in view of the runtime uncertainty. It allows the resulting estimators to be robust not only to observed but also unexpected runtime uncertainties. Leveraging this idea, we bring runtime-uncertainty robustness to three major off-policy learning methods: the inverse propensity score method, reward-model method, and doubly robust method. We theoretically justify the robustness of our methods to runtime uncertainty, and demonstrate their effectiveness using both the simulation and the real-world online experiments.

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38

Fernández-Alvarez, Alberto-Manuel, Daniel Fernández-Lanvin, and Manuel Quintela-Pumares. "Runtime adaptability to domain model changes with efficient constraint checking." Journal of Ambient Intelligence and Smart Environments 8, no. 6 (November 8, 2016): 723–24. http://dx.doi.org/10.3233/ais-160409.

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39

Li, Jianliang, Xiaohai Li, Steven Deeth, Robert Lugg, and Lawrence S. Melvin. "Model based optical proximity correction runtime saving with multisegment solver." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 27, no. 6 (2009): 2972. http://dx.doi.org/10.1116/1.3264667.

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40

Rudy, Jaroslaw. "Dynamic Turing Machine: model and properties for runtime code changes." Computer Science 17, no. 2 (2016): 187. http://dx.doi.org/10.7494/csci.2016.17.2.187.

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41

Mosincat, Adina, Walter Binder, and Mehdi Jazayeri. "Achieving runtime adaptability through automated model evolution and variant selection." Enterprise Information Systems 8, no. 1 (June 21, 2012): 67–83. http://dx.doi.org/10.1080/17517575.2012.691182.

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42

Efremov, Denis Valentinovich, Viktoria Vladimirovna Kopach, Eugeny Valerievich Kornykhin, Viktor Vyacheslavovich Kuliamin, Alexander Konstantinovich Petrenko, Alexey Vladimirovich Khoroshilov, and Ilya Viktorovich Shchepetkov. "Runtime Verification of Operating Systems Based on Abstract Models." Proceedings of the Institute for System Programming of the RAS 33, no. 6 (2021): 15–26. http://dx.doi.org/10.15514/ispras-2021-33(6)-2.

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High complexity of a modern operating system (OS) requires to use complex models and high-level specification languages to describe even separated aspects of OS functionality, e.g., security functions. Use of such models in conformance verification of modeled OS needs to establish rather complex relation between elements of OS implementation and elements of the model. In this paper we present a method to establish and support such a relation, which can be effectively used in testing and runtime verification/monitoring of OS. The method described was used successfully in testing and monitoring of Linux OS core components on conformance to Event-B models.

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43

Yigitbas, Enes, Ivan Jovanovikj, Kai Biermeier, Stefan Sauer, and Gregor Engels. "Integrated model-driven development of self-adaptive user interfaces." Software and Systems Modeling 19, no. 5 (January 27, 2020): 1057–81. http://dx.doi.org/10.1007/s10270-020-00777-7.

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Abstract Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context-of-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

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44

GRELCK, CLEMENS. "Shared memory multiprocessor support for functional array processing in SAC." Journal of Functional Programming 15, no. 3 (May 2005): 353–401. http://dx.doi.org/10.1017/s0956796805005538.

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Classical application domains of parallel computing are dominated by processing large arrays of numerical data. Whereas most functional languages focus on lists and trees rather than on arrays, SAC is tailor-made in design and in implementation for efficient high-level array processing. Advanced compiler optimizations yield performance levels that are often competitive with low-level imperative implementations. Based on SAC, we develop compilation techniques and runtime system support for the compiler-directed parallel execution of high-level functional array processing code on shared memory architectures. Competitive sequential performance gives us the opportunity to exploit the conceptual advantages of the functional paradigm for achieving real performance gains with respect to existing imperative implementations, not only in comparison with uniprocessor runtimes. While the design of SAC facilitates parallelization, the particular challenge of high sequential performance is that realization of satisfying speedups through parallelization becomes substantially more difficult. We present an initial compilation scheme and multi-threaded execution model, which we step-wise refine to reduce organizational overhead and to improve parallel performance. We close with a detailed analysis of the impact of certain design decisions on runtime performance, based on a series of experiments.

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Boukili, Zineb, Hai Nam Tran, and Alain Plantec. "Fine-Grained Runtime Monitoring of Real-Time Embedded Systems." ACM SIGAda Ada Letters 42, no. 1 (December 15, 2022): 105. http://dx.doi.org/10.1145/3577949.3577970.

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Dynamically ensuring the correctness of the functional behavior of a real-time embedded system is tedious, especially in the autonomous domain. Even though the current real-time task model provides sufficient information to perform basic schedulability tests, it is inadequate to be used in runtime monitoring to assert and guarantee the correctness of a system under hardware/software malfunctions or malicious cyber attacks. In this article, we present a runtime monitoring approach based on a fine-grained model of real-time tasks.

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MÁRQUEZ, A., C. GIL, R. BAÑOS, and J. GÓMEZ. "IMPROVING THE PERFORMANCE OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS USING THE ISLAND PARALLEL MODEL." Parallel Processing Letters 17, no. 02 (June 2007): 127–39. http://dx.doi.org/10.1142/s0129626407002922.

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Recently, the research interest in multi-objective optimization has increased remarkably. Most of the proposed methods use a population of solutions that are simultaneously improved trying to approximate them to the Pareto-optimal front. When the population size increases, the quality of the solutions tends to be better, but the runtime is higher. This paper presents how to apply parallel processing to enhance the convergence to the Pareto-optimal front, without increasing the runtime. In particular, we present an island-based parallelization of five multi-objective evolutionary algorithms: NSGAII, SPEA2, PESA, msPESA, and a new hybrid version we propose. Experimental results in some test problems denote that the quality of the solutions tends to improve when the number of islands increases.

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Gao, Tilei, Xiaohui Jia, Rong Jiang, Yuanyuan He, Tao Zhang, and Ming Yang. "Trusted Cloud Service System Based on Block Chain Technology." Wireless Communications and Mobile Computing 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/3704720.

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With the development and popularization of cloud computing technology, more and more users choose cloud services to build their application systems. With the improvement of users’ understanding of software systems, in addition to functionality, trustworthiness has become another key issue concerned by users. Based on the existing research on cloud service trustworthiness measurement and evaluation, this paper proposes a cloud service-trusted delivery model based on asymmetric encryption and hash function and a trusted runtime model of cloud service system based on block chain technology. The proposed models will effectively solve the untrusted problems such as denial and tampering in the process of cloud service acquisition, as well as the system anomaly at runtime. Finally, through targeted experiments to verify the effectiveness and feasibility of the proposed models, and through experimental analysis, this paper expounds on the principle and mechanism of model operation and trustworthiness guarantee.

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Oz, Isil, Muhammad Khurram Bhatti, Konstantin Popov, and Mats Brorsson. "Regression-Based Prediction for Task-Based Program Performance." Journal of Circuits, Systems and Computers 28, no. 04 (March 31, 2019): 1950060. http://dx.doi.org/10.1142/s0218126619500609.

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As multicore systems evolve by increasing the number of parallel execution units, parallel programming models have been released to exploit parallelism in the applications. Task-based programming model uses task abstractions to specify parallel tasks and schedules tasks onto processors at runtime. In order to increase the efficiency and get the highest performance, it is required to identify which runtime configuration is needed and how processor cores must be shared among tasks. Exploring design space for all possible scheduling and runtime options, especially for large input data, becomes infeasible and requires statistical modeling. Regression-based modeling determines the effects of multiple factors on a response variable, and makes predictions based on statistical analysis. In this work, we propose a regression-based modeling approach to predict the task-based program performance for different scheduling parameters with variable data size. We execute a set of task-based programs by varying the runtime parameters, and conduct a systematic measurement for influencing factors on execution time. Our approach uses executions with different configurations for a set of input data, and derives different regression models to predict execution time for larger input data. Our results show that regression models provide accurate predictions for validation inputs with mean error rate as low as 6.3%, and 14% on average among four task-based programs.

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Lee, Euijong, Young-Duk Seo, and Young-Gab Kim. "Self-Adaptive Framework Based on MAPE Loop for Internet of Things." Sensors 19, no. 13 (July 7, 2019): 2996. http://dx.doi.org/10.3390/s19132996.

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The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

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Murakami, Masaki. "A model of runtime transformation for distributed systems based on directed acyclic graph model." Journal of Systems Architecture 50, no. 7 (July 2004): 417–25. http://dx.doi.org/10.1016/j.sysarc.2003.09.009.

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Journal articles on the topic 'Model at runtime'

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