Concurrent Specification of Embedded Systems: An Insight into the Flexibility vs Correctness Trade-Off

Diseases & disorder

DRAGON: Decentralized fault tolerance in edge federations

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively

Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Mitigation of Human Supervisory Control Wait Times through Automation Strategies

The application of network centric operations principles to human supervisory control (HSC) domains means that humans are increasingly being asked to manage multiple simultaneous HSC processes. However, increases in the number of available information sources, volume of information and operational tempo, all which place higher cognitive demands on operators, could become constraints limiting the success of network centric processes. In time-pressured scenarios typical of networked command and control scenarios, efficiently allocating attention between a set of dynamic tasks is crucial for mission success. Inefficient attention allocation leads to system wait times, which could eventually lead to critical events such as missed times on targets and degraded overall mission success. One potential solution to mitigating wait times is the introduction of automated decision support in order to relieve operator workload. However, it is not obvious what automated decision support is appropriate, as higher levels of automation may result in a situation awareness decrement and other problems typically associated with excessive automation such as automation bias. To assess the impact of increasing levels of automation on human and system performance in a time-critical HSC multiple task management context, an experiment was run in which an operator simultaneously managed four highly autonomous unmanned aerial vehicles (UAVs) executing an air tasking order, with the overall goal of destroying a pre-determined set of targets within a limited time period. Four increasing levels automated decision support were investigated as well as high and low operational replanning tempos. The highest level of automation, management-byexception, had the best performance across several metrics but had a greater number of catastrophic events during which a UAV erroneously destroyed a friendly target. Contrary to expectations, the collaborative level of decision support, which provided predictions for possible periods of task overload as well as possible courses of action to relieve the high workload, produced the worst performance. This is attributable to an unintended consequence of the automation where the graphical visualization of the computer’s predictions caused users to try to globally optimize the schedules for all UAVs instead of locally optimizing schedules in the immediate future, resulting in them being overwhelmed. Total system wait time across both experimental factors was dominated by wait time caused by lack of situation awareness, which is difficult to eliminate, implying that there will be a clear upper limit on the number of vehicles that any one person can supervise because of the need to stay cognitively aware of unfolding events.Prepared for Boeing, Phantom Work

THE POLICY FOLLOWS: A NEW THEORY FOR DEVELOPING A VALUE-BASED, POST- PANDEMIC HEALTH CARE SYSTEM IN THE UNITED STATES

Health care policy making, particularly as it relates to technology and innovation, is extraordinarily complex and often fraught with unforeseen consequences. This thesis explores the intricate political history and economic underpinnings of the American health care system, which have created the most expensive, and many would argue— most inefficient—system in the world. More specifically, it examines the impact of technology and innovation on the evolution of that system. The Policy Follows approach to health care policy making introduced in this thesis, provides a clear and forward- thinking approach to integrating research, evidence, and expertise into the creation of informed and impactful health policy. Recent, relevant case studies illustrate the pitfalls of aggressive or poorly-informed health care technology policies advanced by political or industry agendas without the guidance of adequate scientific support. I examine the impact of the COVID-19 pandemic on the health technology landscape, with particular attention to the precipitous expansion of telehealth and virtual care services as a means of addressing the associated challenges, and discuss the imminent policy and regulatory questions facing the health care system as it emerges from this unprecedented national state of emergency. Prior to the pandemic, the growth and adoption of telehealth across the country was greatly inhibited by a several key barriers, particularly state-by-state variation in policies, the conflicting incentives of a fee-for- service based system, and an overall lack of rigorous research to guide development. The Policy Follows approach elucidates the path forward, guided by research and expertise, to developing evidence-based health technology policies that will facilitate the post-pandemic transformation of health care in the U.S. into a more equitable, efficient, cost-effective, and integrated system