Improving System Reliability for Cyber-Physical Systems

Cyber-physical systems (CPS) are systems featuring a tight combination of, and coordination between, the system's computational and physical elements. Cyber-physical systems include systems ranging from critical infrastructure such as a power grid and transportation system to health and biomedical devices. System reliability, i.e., the ability of a system to perform its intended function under a given set of environmental and operational conditions for a given period of time, is a fundamental requirement of cyber-physical systems. An unreliable system often leads to disruption of service, financial cost and even loss of human life. An important and prevalent type of cyber-physical system meets the following criteria: processing large amounts of data; employing software as a system component; running online continuously; having operator-in-the-loop because of human judgment and an accountability requirement for safety critical systems. This thesis aims to improve system reliability for this type of cyber-physical system. To improve system reliability for this type of cyber-physical system, I present a system evaluation approach entitled automated online evaluation (AOE), which is a data-centric runtime monitoring and reliability evaluation approach that works in parallel with the cyber-physical system to conduct automated evaluation along the workflow of the system continuously using computational intelligence and self-tuning techniques and provide operator-in-the-loop feedback on reliability improvement. For example, abnormal input and output data at or between the multiple stages of the system can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop. The operator can then take actions and make changes to the system based on the alerts in order to achieve minimal system downtime and increased system reliability. One technique used by the approach is data quality analysis using computational intelligence, which applies computational intelligence in evaluating data quality in an automated and efficient way in order to make sure the running system perform reliably as expected. Another technique used by the approach is self-tuning which automatically self-manages and self-configures the evaluation system to ensure that it adapts itself based on the changes in the system and feedback from the operator. To implement the proposed approach, I further present a system architecture called autonomic reliability improvement system (ARIS). This thesis investigates three hypotheses. First, I claim that the automated online evaluation empowered by data quality analysis using computational intelligence can effectively improve system reliability for cyber-physical systems in the domain of interest as indicated above. In order to prove this hypothesis, a prototype system needs to be developed and deployed in various cyber-physical systems while certain reliability metrics are required to measure the system reliability improvement quantitatively. Second, I claim that the self-tuning can effectively self-manage and self-configure the evaluation system based on the changes in the system and feedback from the operator-in-the-loop to improve system reliability. Third, I claim that the approach is efficient. It should not have a large impact on the overall system performance and introduce only minimal extra overhead to the cyberphysical system. Some performance metrics should be used to measure the efficiency and added overhead quantitatively. Additionally, in order to conduct efficient and cost-effective automated online evaluation for data-intensive CPS, which requires large volumes of data and devotes much of its processing time to I/O and data manipulation, this thesis presents COBRA, a cloud-based reliability assurance framework. COBRA provides automated multi-stage runtime reliability evaluation along the CPS workflow using data relocation services, a cloud data store, data quality analysis and process scheduling with self-tuning to achieve scalability, elasticity and efficiency. Finally, in order to provide a generic way to compare and benchmark system reliability for CPS and to extend the approach described above, this thesis presents FARE, a reliability benchmark framework that employs a CPS reliability model, a set of methods and metrics on evaluation environment selection, failure analysis, and reliability estimation. The main contributions of this thesis include validation of the above hypotheses and empirical studies of ARIS automated online evaluation system, COBRA cloud-based reliability assurance framework for data-intensive CPS, and FARE framework for benchmarking reliability of cyber-physical systems. This work has advanced the state of the art in the CPS reliability research, expanded the body of knowledge in this field, and provided some useful studies for further research

Vorhersagbares und zur Laufzeit adaptierbares On-Chip Netzwerk für gemischt kritische Echtzeitsysteme

The industry of safety-critical and dependable embedded systems calls for even cheaper, high performance platforms that allow flexibility and an efficient verification of safety and real-time requirements. To cope with the increasing complexity of interconnected functions and to reduce the cost and power consumption of the system, multicore systems are used to efficiently integrate different processing units in the same chip. Networks-on-chip (NoCs), as a modular interconnect, are used as a promising solution for such multiprocessor systems on chip (MPSoCs), due to their scalability and performance. For safety-critical systems, a major goal is the avoidance of hazards. For this, safety-critical systems are qualified or even certified to prove the correctness of the functioning under all possible cases. A predictable behaviour of the NoC can help to ease the qualification process of the system. To achieve the required predictability, designers have two classes of solutions: quality of service mechanisms and (formal) analysis. For mixed-criticality systems, isolation and analysis approaches must be combined to efficiently achieve the desired predictability. Traditional NoC analysis and architecture concepts tackle only a subpart of the challenges: they focus on either performance or predictability. Existing, predictable NoCs are deemed too expensive and inflexible to host a variety of applications with opposing constraints. And state-of-the-art analyses neglect certain platform properties to verify the behaviour. Together this leads to a high over-provisioning of the hardware resources as well as adverse impacts on system performance, and on the flexibility of the system. In this work we tackle these challenges and develop a predictable and runtime-adaptable NoC architecture that efficiently integrates mixed-critical applications with opposing constraints. Additionally, we present a modelling and analysis framework for NoCs that accounts for backpressure. This framework enables to evaluate the performance and reliability early at design time. Hence, the designer can assess multiple design decisions by using abstract models and formal approaches.Die Industrie der sicherheitskritischen und zuverlässigen eingebetteten Systeme verlangt nach noch günstigeren, leistungsfähigeren Plattformen, welche Flexibilität und eine effiziente Überprüfung der Sicherheits- und Echtzeitanforderungen ermöglichen. Um der zunehmenden Komplexität der zunehmend vernetzten Funktionen gerecht zu werden und die Kosten und den Stromverbrauch eines Systems zu reduzieren, werden Mehrkern-Systeme eingesetzt. On-Chip Netzwerke werden aufgrund ihrer Skalierbarkeit und Leistung als vielversprechende Lösung für solch Mehrkern-Systeme eingesetzt. Bei sicherheitskritischen Systemen ist die Vermeidung von Gefahren ein wesentliches Ziel. Dazu werden sicherheitskritische Systeme qualifiziert oder zertifiziert, um die Funktionsfähigkeit in allen möglichen Fällen nachzuweisen. Ein vorhersehbares Verhalten des on-Chip Netzwerks kann dabei helfen, den Qualifizierungsprozess des Systems zu erleichtern. Um die erforderliche Vorhersagbarkeit zu erreichen, gibt es zwei Klassen von Lösungen: Quality of Service Mechanismen und (formale) Analyse. Für Systeme mit gemischter Relevanz müssen Isolationsmechanismen und Analyseansätze kombiniert werden, um die gewünschte Vorhersagbarkeit effizient zu erreichen. Traditionelle Analyse- und Architekturkonzepte für on-Chip Netzwerke lösen nur einen Teil dieser Herausforderungen: sie konzentrieren sich entweder auf Leistung oder Vorhersagbarkeit. Existierende vorhersagbare on-Chip Netzwerke werden als zu teuer und unflexibel erachtet, um eine Vielzahl von Anwendungen mit gegensätzlichen Anforderungen zu integrieren. Und state-of-the-art Analysen vernachlässigen bzw. vereinfachen bestimmte Plattformeigenschaften, um das Verhalten überprüfen zu können. Dies führt zu einer hohen Überbereitstellung der Hardware-Ressourcen als auch zu negativen Auswirkungen auf die Systemleistung und auf die Flexibilität des Systems. In dieser Arbeit gehen wir auf diese Herausforderungen ein und entwickeln eine vorhersehbare und zur Laufzeit anpassbare Architektur für on-Chip Netzwerke, welche gemischt-kritische Anwendungen effizient integriert. Zusätzlich stellen wir ein Modellierungs- und Analyseframework für on-Chip Netzwerke vor, das den Paketrückstau berücksichtigt. Dieses Framework ermöglicht es, Designentscheidungen anhand abstrakter Modelle und formaler Ansätze frühzeitig beurteilen