Intelligent Resource Prediction for HPC and Scientific Workflows

Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these resource requirements would benefit HPC users in many ways, by reducing job failures and queue times on traditional HPC platforms and reducing costs on cloud computing platforms. To that end, we present Tesseract, a semi-automated tool that predicts resource usage for any application on any computing platform, from historical data, with minimal input from the user. We employ Tesseract to predict runtime, memory usage, and disk usage for a diverse set of scientific workflows, and in particular we show how these resource estimates can prevent under-provisioning. Finally, we leverage this core prediction capability to develop solutions for the related challenges of anomaly detection, cross-platform runtime prediction, and cost prediction

Improving the resilience of cyber-physical systems under strategic adversaries

Renewable energy resources challenge traditional energy system operations by substituting the stability and predictability of fossil fuel based generation with the unreliability and uncertainty of wind and solar power. Rising demand for green energy drives grid operators to integrate sensors, smart meters, and distributed control to compensate for this uncertainty and improve the operational efficiency of the grid. Real-time negotiations enable producers and consumers to adjust power loads during shortage periods, such as an unexpected outage or weather event, and to adapt to time-varying energy needs. While such systems improve grid performance, practical implementation challenges can derail the operation of these distributed cyber-physical systems. Network disruptions introduce instability into control feedback systems, and strategic adversaries can manipulate power markets for financial gain. This dissertation analyzes the impact of these outages and adversaries on cyber-physical systems and provides methods for improving resilience, with an emphasis on distributed energy systems. First, a financial model of an interdependent energy market lays the groundwork for profit-oriented attacks and defenses, and a game theoretic strategy optimizes attack plans and defensive investments in energy systems with multiple independent actors. Then attacks and defenses are translated from a theoretical context to a real-time energy market via denial of service (DoS) outages and moving target defenses. Analysis on two market mechanisms shows how adversaries can disrupt market operation, destabilize negotiations, and extract profits by attacking network links and disrupting communication. Finally, a low-cost DoS defense technique demonstrates a method that energy systems may use to defend against attacks

Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

An Application of Gaussian Processes for Analysis in Chemical Engineering

Industry 4.0 is transforming the chemical engineering industry. With it, machine learning (ML) is exploding, and a large variety of complex algorithms are being developed. One particularly popular ML algorithm is the Gaussian Process (GP), which is a full probabilistic, non-parametric, Bayesian model. As a blackbox function, the GP encapsulates an engineering system in a cheaper framework known as a surrogate model. GP surrogate models can be confidently used to investigate chemical engineering scenarios. The research conducted in this thesis explores the application of GPs to case studies in chemical engineering. In many chemical engineering scenarios, it is critical to understand how input uncertainty impacts an important output. A sensitivity analysis does this by characterising the input-output relationship of a system. ML encapsulates a large system into a cheaper framework, enabling a Global Sensitivity Analysis (GSA) to be conducted. The GSA considers the model behaviour over the entire range of inputs and outputs. The Sobol’ indices are recognised as the benchmark GSA method. To achieve a satisfactory precision level, the variance-based decomposition method requires a significant computational burden. Thus, one exciting application of GPs is to reduce the number of model evaluations required and efficiently calculate the Sobol’ indices for large GSA studies. The first three case studies used GPs to perform GSA’s in chemical engineering. The first examined the effects of thermal runaway (TR) abuse on lithium-ion batteries. To calculate time-dependent Sobol’ indices, this study created an accurate surrogate model by training individual GPs at each time step. This work used GPs to help develop a complex chemical engineering simulation model. The second GSA calibrated a high-shear wet granulation model using experimental data. This work developed a methodology, linking two GSA studies, to substantially reduce the experimental effort required for model-driven design and scale-up of model processes. This enabled the creation of a targeted experimental design that reduced the experimental effort by 42%. The third case study developed a novel reduced order model (ROM) for predicting gaseous uptake of metal-organic framework (MOF) structures using GPs. Based on previous GSA research, the Active Subspaces were located using the Sobol’ indices of each pore property for the MOF structures. The novel ROM was shown to be a viable tool to identify the top-performing MOF structures showing its potential to be a universal MOF exploration model. The final two case studies applied GPs as a tool in novel techniques that combined ML algorithms. First, GPs are seldom used for mid-term electricity price forecasting because of their inaccuracy when extrapolating data. This research aimed to improve GP prediction accuracy by developing a GP-based clustering hybridisation method. The proposed hybridisation method outperformed other GP-based forecasting techniques, as demonstrated by the Diebold-Mariano hypothesis test. In the final case study, ML models were used to develop an effective maintenance strategy. The work compares ML algorithms for predictive maintenance and maintenance time estimation on a cyber-physical process plant to find the best for the maintenance workflow. The best algorithms for this case study were the Quadratic Discriminant Analysis model and the GP. The overall plant maintenance costs were found to be reduced by combining predictive maintenance with maintenance time estimation into a workflow. This research could help improve maintenance tasks in Industry 4.0. This thesis focused on using GPs to enhance collaborative efforts and demonstrate the enormous impact that ML can have in both research and industry. By proposing several novel ideas and applications, it is shown that GPs can be an efficient and effective tool for the analysis of chemical engineering systems

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer