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

Systemic Decision Making for Liquidity Risk Management in Banks

The outbreak of the recent financial crisis reveals significant problems in current bank practices in conventional liquidity risk management. To avoid catastrophic consequences, a holistic view, which captures the dynamic interactions between liquidity and other financial variables, should be taken to help banks make business decisions. However, few studies in the literature have addressed this problem. To fill the research gap, we present a Systemic decision making approach for Liquidity Risk Management (SLRM) as a more advanced alternative to Conventional Liquidity Risk Management (CLRM) by capturing dynamic factors, offering logic visibility, and considering rare but fatal events. We show that SLRM can be used to support managerial decisions in developing contingency plans for liquidity management. SLRM is validated by using real data from Washington Mutual, a US bank failed during the 2008 financial tsunami. Further, we demonstrate that SLRM can also help banks conform to regulatory changes in Basel III

FARE: A Framework for Benchmarking Reliability of Cyber-Physical Systems

A cyber-physical system (CPS) is a system featuring a tight combination of, and coordination between, the system's computational and physical elements. System reliability is a critical requirement of cyber-physical systems. An unreliable CPS often leads to system malfunctions, service disruptions, financial losses and even human life. Improving CPS reliability requires an objective measurement, estimation and comparison of the CPS system reliability. This paper describes FARE (Failure Analysis and Reliability Estimation), a framework for benchmarking reliability of cyber-physical systems. Some prior researches have proposed reliability benchmark for some specific CPS such as wind power plant and wireless sensor networks. There were also some prior researches on the components of CPS such as software and some specific hardware. But according to the best of our knowledge, there isn't any reliability benchmark framework for CPS in general. FARE framework provides a CPS reliability model, a set of methods and metrics on the evaluation environment selection, failure analysis and reliability estimation for benchmarking CPS reliability. It not only provides a retrospect evaluation and estimation of the CPS system reliability using the past data, but also provides a mechanism for continuous monitoring and evaluation of CPS reliability for runtime enhancement. The framework is extensible for accommodating new reliability measurement techniques and metrics. It is also generic and applicable to a wide range of CPS applications. For empirical study, we applied the FARE framework on a smart building management system for a large commercial building in New York City. Our experiments showed that FARE is easy to implement, accurate for comparison and can be used for building useful industry benchmarks and standards after accumulating enough data

Rethinking Trends in Instructional Objectives: Exploring the Alignment of Objectives with Activities and Assessment in Higher Education – A Case Study

This study explored higher education level syllabi to identify trends in educational objectives. Bloom’s Taxonomy and various strategic models were used to classify 714 objectives from 114 sections of courses administered through a Midwest teacher education institution in the United States. 1229 verbs and verb phrases were classified through the Taxonomy and differentiated between higher and lower ordered verbs as well as measureable and non-measureable learning outcomes. The results indicated that though learning outcomes the objectives are suggestive of higher ordered skills although the syllabi do not adequately provide information on the expected outcomes of the course

An Autonomic Reliability Improvement System for Cyber-Physical Systems

System reliability is a fundamental requirement of cyber-physical systems. Unreliable systems can lead to disruption of service, financial cost and even loss of human life. Typical cyber-physical systems are designed to process large amounts of data, employ software as a system component, run online continuously and retain an operator-in-the-loop because of human judgment and accountability requirements for safety-critical systems. This paper describes a data-centric runtime monitoring system named ARIS (Autonomic Reliability Improvement System) for improving the reliability of these types of cyber-physical systems. ARIS employs automated online evaluation, working in parallel with the cyber-physical system to continuously conduct automated evaluation at multiple stages in the system workflow and provide real-time feedback for reliability improvement. This approach enables effective evaluation of data from cyber-physical systems. For example, abnormal input and output data can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop, who can then take actions and make changes to the system based on these alerts in order to achieve minimal system downtime and higher system reliability. We have implemented ARIS in a large commercial building cyber-physical system in New York City, and our experiment has shown that it is effective and efficient in improving building system reliability