TY - CHAP A1 - Oklapi, Erna A1 - Deubzer, Michael A1 - Schmidhuber, Stefan A1 - Lalo, Erjola A1 - Mottok, Jürgen T1 - Optimization of Real-Time Multicore Systems Reached by a Genetic Algorithm Approach for Runnable Sequencing T2 - 2014 International Conference on Applied Electronics, Pilsen, Czech Republic N2 - The deployment of complex real-time systems with everyday increasing demands and possibilities, is a challenging task for engineers when performance and efficiency have to be maximized while cost have to be minimized at the same time. For already designed systems it became necessary to perform different modifications in order to find optimal software architecture configuration by respecting all timing constraints which are essential when speaking of real-time systems. In this work, we present a model-based approach of optimizing the execution sequence of runnables within tasks in order to reduce the system's reaction times by improving the overall signal flow duration. Hereby, a genetic optimization algorithm is used to create and evaluate multiple solutions for the runnable sequencing problem. We conclude by demonstration the efficiency of the presented approach with experimental results. KW - runnable sequencing KW - embedded real-time systems KW - genetic algorithms KW - multi-core KW - optimization Y1 - 2014 U6 - https://doi.org/10.1109/AE.2014.7011709 SP - 233 EP - 238 ER - TY - CHAP A1 - Oklapi, Erna A1 - Deubzer, Michael A1 - Schmidhuber, Stefan A1 - Mottok, Jürgen T1 - Verification of an Evolutionary Optimization Algorithm for Runnable Sequencing Problem T2 - 4th Applied Research Conference, 5th July 2014, Ingolstadt Y1 - 2014 ER - TY - CHAP A1 - Sailer, Andreas A1 - Schmidhuber, Stefan A1 - Deubzer, Michael A1 - Alfranseder, Martin A1 - Mucha, Matthias A1 - Mottok, Jürgen T1 - Optimizing the task allocation step for multi-core processors within autosar T2 - 2013 International Conference on Applied Electronics, IEEE, Pilsen, Czech Republic N2 - We present a model-based optimization approach for the task allocation problem in embedded multi-core systems. The information therefore is obtained from a system description in AUTOSAR and runtime measurements of the runnables in hardware traces. Based on this an initial software partitioning of runnables to tasks is created. We then use a genetic algorithm to create and evaluate solutions to the task allocation problem. Each solution is hereby evaluated using a discrete event-based simulation, which allows the evaluation with regard to real-time properties, resource consumption and data-communication overhead. The significance of our approach is then shown in a case-study. There, we optimize the task allocation of an embedded system, whose complexity is comparable to that of an actual system, on a multi-core processor. Finally, the results of the optimization are transferred to an ECU Configuration Description to enable further development in compliance with the AUTOSAR methodology. KW - AUTOSAR KW - Multi-Objective Optimization KW - Multi-Core KW - Goodness-of-fit KW - Task Allocation Y1 - 2013 UR - https://ieeexplore.ieee.org/document/6636524 ER - TY - CHAP A1 - Mucha, Matthias A1 - Mottok, Jürgen A1 - Deubzer, Michael T1 - Probabilistic worst case response time estimation for multi-core real-time systems T2 - 2015 4th Mediterranean Conference on Embedded Computing (MECO), 14-18 June 2015, Budva, Montenegro N2 - Traditional methods rely on Static Timing Analysis techniques to compute the Worst Case Response Time for tasks in real-time systems. Multi-Core real-time systems are faced up with concurrent task executions, semaphore accesses, and task migrations where it may be difficult to obtain the worst case upper bound. A new three staged probabilistic estimation concept is presented. Worst Case Response Times are estimated for tasksets which consist of tasks with multiple time bases. The concept involves data generation with sample classification and sample size equalization, model fit and Worst Case Response Time estimation on the basis of extreme value distribution models. A Generalized Pareto Distribution model fit method which includes threshold detection and parameter estimation is also presented. Sample classification in combination with the new Generalized Pareto Distribution model fit method allows to estimate Worst Case Response Times with low pessimism ranges compared to estimation methods that uses the Generalized Pareto or the Gumbel max distribution without sample classification. Y1 - 2015 SP - 31 EP - 36 ER - TY - CHAP A1 - Schmidhuber, Stefan A1 - Deubzer, Michael A1 - Mottok, Jürgen ED - Mottok, Jürgen ED - Ziemann, Olaf T1 - Genetic optimization of real-time multicore systems with respect to communication-based metrics T2 - 2nd Applied Research Conference 2012 (ARC 2012) 25./26. June 2012, Nuremberg Y1 - 2012 SN - 978-3-8440-1093-0 SP - 21 EP - 25 PB - Shaker CY - Aachen ER - TY - CHAP A1 - Martin, Felix A1 - Sailer, Andreas A1 - Deubzer, Michael A1 - Mottok, Jürgen T1 - Automatische Multicore-Echtzeitvalidierung – Ein Prozess für modellbasierte Softwareentwicklung T2 - Tagungsband 8th Embedded Software Engineering Kongress (ESE), 30.11. - 04.12.2015, Sindelfingen Y1 - 2015 ER - TY - CHAP A1 - Martin, Felix A1 - Sailer, Andreas A1 - Deubzer, Michael A1 - Mottok, Jürgen T1 - Automation of a Closed Loop Model-Based Development Process T2 - Applied Research Conference 2015 (ARC 2015), 3rd July 2015, Nürnberg Y1 - 2015 ER - TY - CHAP A1 - Sailer, Andreas A1 - Deubzer, Michael A1 - Lüttgen, Gerald A1 - Mottok, Jürgen T1 - Comparing Trace Recordings of Automotive Realtime Software T2 - RTNS '17: Proceedings of the 25th International Conference on Real-Time Networks and Systems, Grenoble, France, IEEE N2 - The process of engineering models of existing real-time system components is often difficult and time consuming, especially when legacy code has to be re-used or information about the exact timing behaviour is needed. In order to tackle this reverse engineering problem, we have developed the tool CoreTAna. CoreTAna derives an AUTOSAR compliant model of a real-time system by conducting dynamic analysis using trace recordings. Motivated by the challenge of assessing the quality of reverse engineered models of real-time software, we present a novel mathematical measure for comparing trace recordings from embedded real-time systems regarding their temporal behaviour. We also introduce a benchmark framework based on this measure, for evaluating reverse engineering tools such as CoreTAna. This considers common system architectures and also includes randomly generated systems and three systems of industrial automotive projects. Finally, an industrial case study demonstrates other use cases of our measure, such as impact analysis. Y1 - 2017 U6 - https://doi.org/10.1145/3139258.3139265 SP - 118 EP - 127 ER - TY - CHAP A1 - Sailer, Andreas A1 - Deubzer, Michael A1 - Lüttgen, Gerald A1 - Mottok, Jürgen T1 - CoreTAna: A Trace Analyzer for Reverse Engineering Real-Time Software T2 - IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), March 14 to March 18 2016, Suita, Osaka, Japan N2 - With the availability of the AUTOSAR standard, model-driven methodologies are becoming established in theautomotive domain. However, the process of creating models ofexisting system components is often difficult and time consuming, especially when legacy code has to be re-used or informationabout the exact timing behavior is needed. In order to tackle thisreverse engineering problem, we present CoreTAna, a novel toolthat derives an AUTOSAR compliant model of a real-time systemfrom a dynamic analysis of its trace recordings. This paper givesan overview of CoreTAna's current features and discusses itsbenefits for reverse engineering. KW - AUTOSAR KW - tracing KW - profiling KW - timing model KW - reverse engineering Y1 - 2016 U6 - https://doi.org/10.1109/SANER.2016.36 ER -