Automating Staged Rollout with Reinforcement Learning

Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultaneously considering multiple product or process metrics explicitly. This paper demonstrates the potential to automate staged rollout with multi-objective reinforcement learning in order to dynamically balance stakeholder needs such as time to deliver new features and downtime incurred by failures due to latent defects

Predicting Resilience with Neural Networks

Resilience engineering studies the ability of a system to survive and recover from disruptive events, which finds applications in several domains. Most studies emphasize resilience metrics to quantify system performance, whereas recent studies propose statistical modeling approaches to project system recovery time after degradation. Moreover, past studies are either performed on data after recovering or limited to idealized trends. Therefore, this paper proposes three alternative neural network (NN) approaches including (i) Artificial Neural Networks, (ii) Recurrent Neural Networks, and (iii) Long-Short Term Memory (LSTM) to model and predict system performance, including negative and positive factors driving resilience to quantify the impact of disruptive events and restorative activities. Goodness-of-fit measures are computed to evaluate the models and compared with a classical statistical model, including mean squared error and adjusted R squared. Our results indicate that NN models outperformed the traditional model on all goodness-of-fit measures. More specifically, LSTMs achieved an over 60\% higher adjusted R squared, and decreased predictive error by 34-fold compared to the traditional method. These results suggest that NN models to predict resilience are both feasible and accurate and may find practical use in many important domains

A Bi-Objective Approach to Evaluate Highway Routing and Regulatory Strategies for Hazardous Materials Transportation

Hazardous materials (hazmat) transportation is of concern to policymakers because of the serious safety, health, and environmental risks associated with the release of hazmat. One effective approach to minimize risks associated with hazmat transport is the prohibition of hazmat transportation on higher risk links that either pose safety hazards or increased exposure by traversing densely populated areas. Because of high risk, there are multiple stakeholders involved in hazmat transportation. While shippers and carriers are directly involved in making routing decisions, regulatory agencies influence this decision by imposing routing restrictions. In this paper, we apply a bi-objective shortest path problem to evaluate routing and regulation plans for hazmat transportation. We characterize the cost objective as the shortest path between an origin and a destination. The risk objective is to minimize the risk of exposure by restricting the link with the highest risk on the best available path from an origin to a destination. We formulate the bi-objective model and apply it to a test network. Solutions consider multiple origin-destination pairs and present a non-dominated frontier to establish routing and regulatory strategies for hazmat transportation

Software Reliability and Security Assessment: Automation and Frameworks

The presentation summarizes the application of SFRAT and next generation SWEEP tool to NASA programs to demonstrate their potential to provide more detailed oversight of software reliability at various stages of development and testing

Principles of performance and reliability modeling and evaluation: essays in honor of Kishor Trivedi on his 70th birthday

This book presents the latest key research into the performance and reliability aspects of dependable fault-tolerant systems and features commentary on the fields studied by Prof. Kishor S. Trivedi during his distinguished career. Analyzing system evaluation as a fundamental tenet in the design of modern systems, this book uses performance and dependability as common measures and covers novel ideas, methods, algorithms, techniques, and tools for the in-depth study of the performance and reliability aspects of dependable fault-tolerant systems. It identifies the current challenges that designers and practitioners must face in order to ensure the reliability, availability, and performance of systems, with special focus on their dynamic behaviors and dependencies, and provides system researchers, performance analysts, and practitioners with the tools to address these challenges in their work. With contributions from Prof. Trivedi's former PhD students and collaborators, many of whom are internationally recognized experts, to honor him on the occasion of his 70th birthday, this book serves as a valuable resource for all engineering disciplines, including electrical, computer, civil, mechanical, and industrial engineering as well as production and manufacturing

A Bi-Objective Approach to Evaluate Highway Routing and Regulatory Strategies for Hazardous Materials Transportation

Hazardous materials (hazmat) transportation is of concern to policymakers because of the serious safety, health, and environmental risks associated with the release of hazmat. One effective approach to minimize risks associated with hazmat transport is the prohibition of hazmat transportation on higher risk links that either pose safety hazards or increased exposure by traversing densely populated areas. Because of high risk, there are multiple stakeholders involved in hazmat transportation. While shippers and carriers are directly involved in making routing decisions, regulatory agencies influence this decision by imposing routing restrictions. In this paper, we apply a bi-objective shortest path problem to evaluate routing and regulation plans for hazmat transportation. We characterize the cost objective as the shortest path between an origin and a destination. The risk objective is to minimize the risk of exposure by restricting the link with the highest risk on the best available path from an origin to a destination. We formulate the bi-objective model and apply it to a test network. Solutions consider multiple origin-destination pairs and present a non-dominated frontier to establish routing and regulatory strategies for hazmat transportation

Architecture-Based Reliability Analysis with Uncertain Parameters

Architecture-based reliability analysis has gained prominence in the recent years as a way to predict the reliability of a software application during the design phase, before an investment is made in any implementation. To apply this analysis, the parameters comprising the architectural model must be estimated using the limited data and knowledge available during the design phase. These estimates, as a result, are inherently uncertain. Contemporary approaches, however, do not consider these uncertainties, and hence, may produce inaccurate reliability results. This paper presents a Bayesian approach to systematically consider parametric uncertainties in architecture-based analysis. The novelty of this approach lies in determining credible intervals for the model parameters as a function of their posterior distributions. By leveraging these intervals, we illustrate how to: (i) quantify the impact of uncertainty in a specific parameter on the system reliability estimate; (ii) evaluate when a sufficient amount of data has been collected to reduce the uncertainty to an acceptable level; and (iii) assess the impact of prior knowledge regarding the parameters in improving the system reliability estimate

Architecture-Based Reliability Analysis with Uncertain Parameters

Architecture-based reliability analysis has gained prominence in the recent years as a way to predict the reliability of a software application during the design phase, before an investment is made in any implementation. To apply this analysis, the parameters comprising the architectural model must be estimated using the limited data and knowledge available during the design phase. These estimates, as a result, are inherently uncertain. Contemporary approaches, however, do not consider these uncertainties, and hence, may produce inaccurate reliability results. This paper presents a Bayesian approach to systematically consider parametric uncertainties in architecture-based analysis. The novelty of this approach lies in determining credible intervals for the model parameters as a function of their posterior distributions. By leveraging these intervals, we illustrate how to: (i) quantify the impact of uncertainty in a specific parameter on the system reliability estimate; (ii) evaluate when a sufficient amount of data has been collected to reduce the uncertainty to an acceptable level; and (iii) assess the impact of prior knowledge regarding the parameters in improving the system reliability estimate