Lessons learned from spatial and temporal correlation of node failures in high performance computers

In this paper we study the correlation of node failures in time and space. Our study is based on measurements of a production high performance computer over an 8-month time period. We draw possible types of correlations between node failures and show that, in many cases, there are direct correlations between observed node failures. The significance of such a study is twofold: achieving a clearer understanding of correlations between node failures and enabling failure detection as early as possible. The results of this study are aimed at helping the system administrators minimize (or even prevent) the destructive effects of correlated node failures

SHIP COLLISION RISK ASSESSMENT MODEL FOR QINZHOU PORT BASED ON EVENT SEQUENCE DIAGRAM

Qinzhou Port is one of the most important ports in the “Beibu Gulf” of China. It is also the main hub port of the "21st century maritime silk road" strategy. Based on a basic collision risk assessment approach, an Event Sequence Diagram (ESD) model that explains the four-stage collision avoidance decision-making procedure is proposed from the perspectives of perception, cognition, decision, and execution. Using the historical data derived from collision accident reports from the Qinzhou Port waters from 2013 to 2017, as well as the data elicited from expert knowledge, a quantitative evaluation of probability distributions of different collision failure modes is performed. The results are also compared with relevant results from other types of navigation waters to analyse collision risk level of Qinzhou waters. At the same time, the main failures paths of collision avoidance decision making are identified. The proposed model can provide with an overall collision risk picture from a macro perspective

A systematic review of integrated frameworks for resilience and sustainability assessments for critical infrastructures

There is a growing tendency to assess resilience and sustainability of critical infrastructures (CI), given the significant increment in high-impact natural hazard events affecting socio-economic welfare. Historically, these assessments have been conducted separately due to the independent evolution of each concept. However, recent contributions tend to integrate them. This paper provides a state-of-the-art review of integrated assessments for resilience and sustainability in CI, examining concepts, indicators, frameworks, and methodologies. Additionally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was performed to gain further insights into the prospects of integrated assessments. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, eligibility criteria were established, leading to the selection of twelve studies. These works were compared based on five dimensions (economic, environmental, social, technical, and governance) to highlight the differences in the indicators used. While all studies considered the social, environmental, and economic dimensions, some did not further analyze sufficient indicators to evaluate environmental and social effects, with governance often neglected. This study emphasizes the relevance of establishing common metrics for a convergent frame for the resilience and sustainability assessment. The findings presented suggest that integrated assessments lead to a more strategic use of resources toward more resilient CIPortuguese Foundation for Science and Technology (FCT) through grant number PD/ 2020.07208.BD, and by FEDER funds through the Competitivity Factors Oper-ational Programme—COMPETE and by national funds through FCT (Foundation for Science and Technology) within the scope of the project POCI-01-0247- FEDER-039555. It was also partly financed by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/ 2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/ 0112/202

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

Breaking the habit: measuring and predicting departures from routine in individual human mobility

Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated

Outstanding challenges in the transferability of ecological models

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their ‘transferability’) undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions

Understanding and Managing Wildfire Risks to Residential Communities and Supply Chain Networks

Wildfire has become an increasing threat to humans, the built environment, and ecosystems in the United States. Several factors contribute to such an increase in wildfire risk, including climate change, rapid population growth and infrastructure development at the wildland-urban interface, and accumulated fuels from past wildfire management practices. Increases in wildfire activity have resulted in substantial human and economic losses in the past decade. For example, the 2023 Hawaii wildfires razed more than 2,200 homes and businesses while tragically claiming the lives of at least 115 individuals. A series of California wildfires in 2015, 2017, and 2018 resulted in direct economic losses of $4.8 billion,$18 billion, and $26.3 billion, respectively. In addition to the direct economic losses, the 2018 California wildfires disrupted many supply chain systems, which propagated across the regional economy and induced$88.6 billion in direct losses. These recent wildfires have underscored the urgent need for understanding, assessing, and managing wildfire risks to residential communities and supply chains. To this end, this dissertation aims at understanding and managing wildfire risks to humans, properties, and the regional economy, with a particular focus on residential communities and supply chain networks. To advance our understanding of various proactive and emergency activities, this dissertation begins by examining homeowners’ decisions on wildfire-related proactive actions, such as home hardening, vegetation treatment, and homeowners insurance, through an online survey and subsequently assesses the effect of these actions on the process of housing recovery. Next, this dissertation shifts its focus towards individual behaviors during wildfire events, encompassing their preferences and decisions made during wildfire evacuations. This entails the study of factors like evacuation triggers and timing, as well as a series of en-route decisions made by residents in wildfire-prone areas, all gathered through an online survey. Based on the survey results, data-driven models are developed for predicting evacuees’ behaviors during wildfires. Furthermore, this dissertation integrates these data-driven predictive models with wildfire simulations, vulnerability assessment, and traffic simulation to construct a comprehensive agent-based modeling (ABM) framework for wildfire evacuations under damaged transportation settings. The framework is designed to simulate traffic conditions during a wildfire evacuation and identifies the critical parts of the transportation network for pre-fire risk mitigation actions aimed at improving mobility during a wildfire evacuation.To assess wildfire risk to a supply chain network, this dissertation also proposes a probabilistic wildfire risk assessment framework. It provides rigorous probabilistic descriptions of wildfire ignition likelihood and growth, interaction between supply chain components and wildfire, consequent component damage, and network-level performance reduction. Then, a hypothetical forest-residuals-to-sustainable-aviation-fuel supply chain network is utilized as an illustrative example to demonstrate the capability and applicability of the proposed framework. The proposed framework can be used as a planning tool to evaluate network performance subject to a set of what-if scenarios and the effect of pre- and post-wildfire risk mitigation measures.Overall, this dissertation provides valuable insights for understanding the inherent drivers of individual’s preference on both wildfire proactive actions and evacuation decisions. This information can serve as a foundation for increasing community resilience by helping policymakers and stakeholders to increase participation rates in proactive actions and the responsiveness to evacuation orders. Moreover, the simulation tools and quantitative frameworks developed in this dissertation provide valuable support for stakeholders and policymakers in forecasting post-wildfire performance and implementing more effective pre-event mitigation strategies. These adaptable tools and frameworks show potential for broader applications across various domains, including water distribution networks, transportation systems, and electric power grids, making them valuable assets in addressing the complex challenges posed by dynamic and interconnected systems