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

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Data-Dependency Formalism for Developing Peer-to-Peer Applications

Developing peer-to-peer (P2P) applications became increasingly important in software development. Nowadays, a large number of organizations from many different sectors and sizes depend more and more on collaboration between actors to perform their tasks. These P2P applications usually have a recursive behavior that many modeling approaches cannot describe and analyze (e.g. finite-state approaches). In this paper, we present an approach that combines component-based development with well-understood methods and techniques from the field of Attribute Grammars and Data-Flow Analysis in order to construct an abstract representation (i.e. Data-Dependency Graph) for P2P applications, and then perform data-flow analyzes on it. This approach embodies a formalism called DDF (Data-Dependency Formalism) to capture the behavior of P2P applications and construct their Data-Dependency Graphs. Various properties can be inferred and computed at the proposed level of data abstraction, including some properties that model checking cannot compute if the system presents a recursive behavior. As examples, we present two algorithms: one to resolve the deadlock problem and another for dominance analysis

Ninth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, Aarhus, Denmark, October 20-22, 2008

This booklet contains the proceedings of the Ninth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, October 20-22, 2008. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop0

NEGOSEIO: framework for the sustainability of model-oriented enterprise interoperability

Dissertation to obtain the degree of Doctor of Philosophy in Electrical and Computer Engineering(Industrial Information Systems)This dissertation tackles the problematic of Enterprise Interoperability in the current globally connected world. The evolution of the Information and Communication Technologies has endorsed the establishment of fast, secure and robust data exchanges, promoting the development of networked solutions. This allowed the specialisation of enterprises (particularly SMEs) and favoured the development of complex and heterogeneous provider systems. Enterprises are abandoning their self-centrism and working together on the development of more complete solutions. Entire business solutions are built integrating several enterprises (e.g., in supply chains, enterprise nesting) towards a common objective. Additionally, technologies, platforms, trends, standards and regulations keep evolving and demanding enterprises compliance. This evolution needs to be continuous, and is naturally followed by a constant update of each networked enterprise’s interfaces, assets, methods and processes. This unstable environment of perpetual change is causing major concerns in both SMEs and customers as the current interoperability grounds are frail, easily leading to periods of downtime, where business is not possible. The pressure to restore interoperability rapidly often leads to patching and to the adoption of immature solutions, contributing to deteriorate even more the interoperable environment. This dissertation proposes the adoption of NEGOSEIO, a framework that tackles interoperability issues by developing strong model-based knowledge assets and promoting continuous improvement and adaptation for increasing the sustainability of interoperability on enterprise systems. It presents the research motivations and the developed framework’s main blocks, which include model-based knowledge management, collaboration service-oriented architectures implemented over a cloud-based solution, and focusing particularly on its negotiation core mechanism to handle inconsistencies and solutions for the detected interoperability problems. It concludes by validating the research and the proposed framework, presenting its application in a real business case of aerospace mission design on the European Space Agency (ESA).FP7 ENSEMBLE, UNITE, MSEE and IMAGINE project