The mixed-criticality toolbox promises system architects a powerful framework for consolidating real-time tasks with different safety properties on a single computing platform. Thanks to the research efforts in the mixed-criticality field, guarantees provided to the highest criticality level are well understood. However, lower-criticality job execution depends on the condition that all high-criticality jobs complete within their more optimistic low-criticality execution time bounds. Otherwise, no guarantees are made. In this paper, we add to the mixed-criticality toolbox by providing a probabilistic analysis method for low-criticality tasks. While deterministic models reduce task behavior to constant numbers, probabilistic analysis captures varying runtime behavior. We introduce a novel algorithmic approach for probabilistic timing analysis, which we call symbolic scheduling. For restricted task sets, we also present an analytical solution. We use this method to calculate per-job success probabilities for low-criticality tasks, in order to quantify, how low-criticality tasks behave in case of high-criticality jobs overrunning their optimistic low-criticality reservation.

In 1994, Alur and Dill introduced timed automata as a simple mathematical model for modelling the behaviour of real-time systems. In this thesis, we extend timed automata with weights. More detailed, we equip both the states and transitions of a timed automaton with weights taken from an appropriate mathematical structure. The weight of a transition determines the weight for taking this transition, and the weight of a state determines the weight for letting time elapse in this state. Since the weight for staying in a state depends on time, this model, called weighted timed automata, has many interesting applications, for instance, in operations research and scheduling. We give characterizations for the behaviours of weighted timed automata in terms of rational expressions and logical formulas. These formalisms are useful for the specification of real-time systems with continuous resource consumption. We further investigate the relation between the behaviours of weighted timed automata and timed automata. Finally, we present important decidability results for weighted timed automata.

The mixed-criticality toolbox promises system architects a powerful framework for consolidating real-time tasks with different safety properties on a single computing platform. Thanks to the research efforts in the mixed-criticality field, guarantees provided to the highest criticality level are well understood. However, lower-criticality job execution depends on the condition that all high-criticality jobs complete within their more optimistic low-criticality execution time bounds. Otherwise, no guarantees are made. In this paper, we add to the mixed-criticality toolbox by providing a probabilistic analysis method for low-criticality tasks. While deterministic models reduce task behavior to constant numbers, probabilistic analysis captures varying runtime behavior. We introduce a novel algorithmic approach for probabilistic timing analysis, which we call symbolic scheduling. For restricted task sets, we also present an analytical solution. We use this method to calculate per-job success probabilities for low-criticality tasks, in order to quantify, how low-criticality tasks behave in case of high-criticality jobs overrunning their optimistic low-criticality reservation.

Modern general purpose programming languages like Java or C# provide a rich feature set and a higher degree of abstraction than conventional real-time programming languages like C/C++ or Ada. Applications developed with these modern languages are typically deployed via platform independent intermediate code. The intermediate code is typically executed by a virtualizing runtime environment. This allows for a high portability. Prominent examples are the Dalvik Virtual Machine of the Android operating system, the Java Virtual Machine as well as Microsoft .NET’s Common Language Runtime. The virtualizing runtime environment executes the instructions of the intermediate code. This introduces additional challenges to real-time software development. One issue is the transformation of the intermediate code instructions to native code instructions. If this transformation interferes with the execution of the real-time application, this might introduce jitter to its execution times. This can degrade the quality of soft real-time systems like augmented reality applications on mobile devices, but can lead to severe problems in hard real-time applications that have strict timing requirements. This thesis examines the possibility to overcome timing issues with intermediate code execution in virtualizing runtime environments. It addresses real-time suitable generation of native code from intermediate code in particular. In order to preserve the advantages of modern programming languages over conventional ones, the solution has to adhere to the following main requirements: - Intermediate code transformation does not interfere with application execution - Portability is not reduced and code transformation is still transparent to a programmer - Comparable performance Existing approaches are evaluated. A concept for real-time suitable code generation is developed. The concept bases on a pre-allocation of the native code and the elimination of indirect references, while considering and optimizing startup time of an application. This concept is implemented by the extension of an existing virtualizing runtime environment, which does not target real-time systems per se. It is evaluated qualitatively and quantitatively. A comparison of the new concept to existing approaches reveals high execution time determinism and good performance and while preserving the portability deployment of applications via intermediate code.

In 1994, Alur and Dill introduced timed automata as a simple mathematical model for modelling the behaviour of real-time systems. In this thesis, we extend timed automata with weights. More detailed, we equip both the states and transitions of a timed automaton with weights taken from an appropriate mathematical structure. The weight of a transition determines the weight for taking this transition, and the weight of a state determines the weight for letting time elapse in this state. Since the weight for staying in a state depends on time, this model, called weighted timed automata, has many interesting applications, for instance, in operations research and scheduling. We give characterizations for the behaviours of weighted timed automata in terms of rational expressions and logical formulas. These formalisms are useful for the specification of real-time systems with continuous resource consumption. We further investigate the relation between the behaviours of weighted timed automata and timed automata. Finally, we present important decidability results for weighted timed automata.

One of the key challenges in service-oriented systems engineering is the prediction and assurance of non-functional properties, such as the reliability and the availability of composite interorganizational services. Such systems are often characterized by a variety of inherent uncertainties, which must be addressed in the modeling and the analysis approach. The different relevant types of uncertainties can be categorized into (1) epistemic uncertainties due to incomplete knowledge and (2) randomization as explicitly used in protocols or as a result of physical processes. In this report, we study a probabilistic timed model which allows us to quantitatively reason about nonfunctional properties for a restricted class of service-oriented real-time systems using formal methods. To properly motivate the choice for the used approach, we devise a requirements catalogue for the modeling and the analysis of probabilistic real-time systems with uncertainties and provide evidence that the uncertainties of type (1) and (2) in the targeted systems have a major impact on the used models and require distinguished analysis approaches. The formal model we use in this report are Interval Probabilistic Timed Automata (IPTA). Based on the outlined requirements, we give evidence that this model provides both enough expressiveness for a realistic and modular specifiation of the targeted class of systems, and suitable formal methods for analyzing properties, such as safety and reliability properties in a quantitative manner. As technical means for the quantitative analysis, we build on probabilistic model checking, specifically on probabilistic time-bounded reachability analysis and computation of expected reachability rewards and costs. To carry out the quantitative analysis using probabilistic model checking, we developed an extension of the Prism tool for modeling and analyzing IPTA. Our extension of Prism introduces a means for modeling probabilistic uncertainty in the form of probability intervals, as required for IPTA. For analyzing IPTA, our Prism extension moreover adds support for probabilistic reachability checking and computation of expected rewards and costs. We discuss the performance of our extended version of Prism and compare the interval-based IPTA approach to models with fixed probabilities. / Eine der wichtigsten Herausforderungen in der Entwicklung von Service-orientierten Systemen ist die Vorhersage und die Zusicherung von nicht-funktionalen Eigenschaften, wie Ausfallsicherheit und Verfügbarkeit von zusammengesetzten, interorganisationellen Diensten. Diese Systeme sind oft charakterisiert durch eine Vielzahl von inhärenten Unsicherheiten, welche sowohl in der Modellierung als auch in der Analyse eine Rolle spielen. Die verschiedenen relevanten Arten von Unsicherheiten können eingeteilt werden in (1) epistemische Unsicherheiten aufgrund von unvollständigem Wissen und (2) Zufall als Mittel in Protokollen oder als Resultat von physikalischen Prozessen. In diesem Bericht wird ein probabilistisches, Zeit-behaftetes Modell untersucht, welches es ermöglicht quantitative Aussagen über nicht-funktionale Eigenschaften von einer eingeschränkten Klasse von Service-orientierten Echtzeitsystemen mittels formaler Methoden zu treffen. Zur Motivation und Einordnung wird ein Anforderungskatalog für probabilistische Echtzeitsysteme mit Unsicherheiten erstellt und gezeigt, dass die Unsicherheiten vom Typ (1) und (2) in den untersuchten Systemen einen Ein uss auf die Wahl der Modellierungs- und der Analysemethode haben. Als formales Modell werden Interval Probabilistic Timed Automata (IPTA) benutzt. Basierend auf den erarbeiteten Anforderungen wird gezeigt, dass dieses Modell sowohl ausreichende Ausdrucksstärke für eine realistische und modulare Spezifikation als auch geeignete formale Methoden zur Bestimmung von quantitativen Sicherheits- und Zuverlässlichkeitseigenschaften bietet. Als technisches Mittel für die quantitative Analyse wird probabilistisches Model Checking, speziell probabilistische Zeit-beschränkte Erreichbarkeitsanalyse und Bestimmung von Erwartungswerten für Kosten und Vergütungen eingesetzt. Um die quantitative Analyse mittels probabilistischem Model Checking durchzuführen, wird eine Erweiterung des Prism-Werkzeugs zur Modellierung und Analyse von IPTA eingeführt. Die präsentierte Erweiterung von Prism ermöglicht die Modellierung von probabilistischen Unsicherheiten mittelsWahrscheinlichkeitsintervallen, wie sie für IPTA benötigt werden. Zur Verifikation wird probabilistische Erreichbarkeitsanalyse und die Berechnung von Erwartungswerten durch das Werkzeug unterstützt. Es wird die Performanz der Prism-Erweiterung untersucht und der Intervall-basierte IPTA-Ansatz mit Modellen mit festen Wahrscheinlichkeitswerten verglichen.

Modern general purpose programming languages like Java or C# provide a rich feature set and a higher degree of abstraction than conventional real-time programming languages like C/C++ or Ada. Applications developed with these modern languages are typically deployed via platform independent intermediate code. The intermediate code is typically executed by a virtualizing runtime environment. This allows for a high portability. Prominent examples are the Dalvik Virtual Machine of the Android operating system, the Java Virtual Machine as well as Microsoft .NET’s Common Language Runtime. The virtualizing runtime environment executes the instructions of the intermediate code. This introduces additional challenges to real-time software development. One issue is the transformation of the intermediate code instructions to native code instructions. If this transformation interferes with the execution of the real-time application, this might introduce jitter to its execution times. This can degrade the quality of soft real-time systems like augmented reality applications on mobile devices, but can lead to severe problems in hard real-time applications that have strict timing requirements. This thesis examines the possibility to overcome timing issues with intermediate code execution in virtualizing runtime environments. It addresses real-time suitable generation of native code from intermediate code in particular. In order to preserve the advantages of modern programming languages over conventional ones, the solution has to adhere to the following main requirements: - Intermediate code transformation does not interfere with application execution - Portability is not reduced and code transformation is still transparent to a programmer - Comparable performance Existing approaches are evaluated. A concept for real-time suitable code generation is developed. The concept bases on a pre-allocation of the native code and the elimination of indirect references, while considering and optimizing startup time of an application. This concept is implemented by the extension of an existing virtualizing runtime environment, which does not target real-time systems per se. It is evaluated qualitatively and quantitatively. A comparison of the new concept to existing approaches reveals high execution time determinism and good performance and while preserving the portability deployment of applications via intermediate code.

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μ W/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μ W/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μ W/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μ W/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.