Optimizing soft error reliability through scheduling on heterogeneous multicore processors

Reliability to soft errors is an increasingly important issue as technology continues to shrink. In this paper, we show that applications exhibit different reliability characteristics on big, high-performance cores versus small, power-efficient cores, and that there is significant opportunity to improve system reliability through reliability-aware scheduling on heterogeneous multicore processors. We monitor the reliability characteristics of all running applications, and dynamically schedule applications to the different core types in a heterogeneous multicore to maximize system reliability. Reliability-aware scheduling improves reliability by 25.4 percent on average (and up to 60.2 percent) compared to performance-optimized scheduling on a heterogeneous multicore processor with two big cores and two small cores, while degrading performance by 6.3 percent only. We also introduce a novel system-level reliability metric for multiprogram workloads on (heterogeneous) multicores. We provide a trade-off analysis among reliability-, power- and performance-optimized scheduling, and evaluate reliability-aware scheduling under performance constraints and for unprotected L1 caches. In addition, we also extend our scheduling mechanisms to multithreaded programs. The hardware cost in support of our reliability-aware scheduler is limited to 296 bytes per core

Reliability-aware and energy-efficient system level design for networks-on-chip

2015 Spring.Includes bibliographical references.With CMOS technology aggressively scaling into the ultra-deep sub-micron (UDSM) regime and application complexity growing rapidly in recent years, processors today are being driven to integrate multiple cores on a chip. Such chip multiprocessor (CMP) architectures offer unprecedented levels of computing performance for highly parallel emerging applications in the era of digital convergence. However, a major challenge facing the designers of these emerging multicore architectures is the increased likelihood of failure due to the rise in transient, permanent, and intermittent faults caused by a variety of factors that are becoming more and more prevalent with technology scaling. On-chip interconnect architectures are particularly susceptible to faults that can corrupt transmitted data or prevent it from reaching its destination. Reliability concerns in UDSM nodes have in part contributed to the shift from traditional bus-based communication fabrics to network-on-chip (NoC) architectures that provide better scalability, performance, and utilization than buses. In this thesis, to overcome potential faults in NoCs, my research began by exploring fault-tolerant routing algorithms. Under the constraint of deadlock freedom, we make use of the inherent redundancy in NoCs due to multiple paths between packet sources and sinks and propose different fault-tolerant routing schemes to achieve much better fault tolerance capabilities than possible with traditional routing schemes. The proposed schemes also use replication opportunistically to optimize the balance between energy overhead and arrival rate. As 3D integrated circuit (3D-IC) technology with wafer-to-wafer bonding has been recently proposed as a promising candidate for future CMPs, we also propose a fault-tolerant routing scheme for 3D NoCs which outperforms the existing popular routing schemes in terms of energy consumption, performance and reliability. To quantify reliability and provide different levels of intelligent protection, for the first time, we propose the network vulnerability factor (NVF) metric to characterize the vulnerability of NoC components to faults. NVF determines the probabilities that faults in NoC components manifest as errors in the final program output of the CMP system. With NVF aware partial protection for NoC components, almost 50% energy cost can be saved compared to the traditional approach of comprehensively protecting all NoC components. Lastly, we focus on the problem of fault-tolerant NoC design, that involves many NP-hard sub-problems such as core mapping, fault-tolerant routing, and fault-tolerant router configuration. We propose a novel design-time (RESYN) and a hybrid design and runtime (HEFT) synthesis framework to trade-off energy consumption and reliability in the NoC fabric at the system level for CMPs. Together, our research in fault-tolerant NoC routing, reliability modeling, and reliability aware NoC synthesis substantially enhances NoC reliability and energy-efficiency beyond what is possible with traditional approaches and state-of-the-art strategies from prior work

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

On Personal Storage Systems: Architecture and Design Considerations

Actualment, els usuaris necessiten grans quantitats d’espai d’emmagatzematge remot per guardar la seva informació personal. En aquesta dissertació, estudiarem dues arquitectures emergents de sistemes d’emmagatzematge d’informació personal: els Núvols Personals (centralitzats) i els sistemes d’emmagatzematge social (descentralitzats). A la Part I d'aquesta tesi, contribuïm desvelant l’operació interna d’un Núvol Personal d’escala global, anomenat UbuntuOne (U1), incloent-hi la seva arquitectura, el seu servei de metadades i les interaccions d’emmagatzematge de dades. A més, proporcionem una anàlisi de la part de servidor d’U1 on estudiem la càrrega del sistema, el comportament dels usuaris i el rendiment del seu servei de metadades. També suggerim tota una sèrie de millores potencials al sistema que poden beneficiar sistemes similars. D'altra banda, en aquesta tesi també contribuïm mesurant i analitzant la qualitat de servei (p.e., velocitat, variabilitat) de les transferències sobre les REST APIs oferides pels Núvols Personals. A més, durant aquest estudi, ens hem adonat que aquestes interfícies poden ser objecte d’abús quan són utilitzades sobre els comptes gratuïts que normalment ofereixen aquests serveis. Això ha motivat l’estudi d’aquesta vulnerabilitat, així com de potencials contramesures. A la Part II d'aquesta dissertació, la nostra primera contribució és analitzar la qualitat de servei que els sistemes d’emmagatzematge social poden proporcionar en termes de disponibilitat de dades, velocitat de transferència i balanceig de la càrrega. El nostre interès principal és entendre com fenòmens intrínsecs, com les dinàmiques de connexió dels usuaris o l’estructura de la xarxa social, limiten el rendiment d’aquests sistemes. També proposem nous mecanismes de manegament de dades per millorar aquestes limitacions. Finalment, dissenyem una arquitectura híbrida que combina recursos del Núvol i dels usuaris. Aquesta arquitectura té com a objectiu millorar la qualitat de servei del sistema i deixa als usuaris decidir la quantitat de recursos utilitzats del Núvol, o en altres paraules, és una decisió entre control de les seves dades i rendiment.Los usuarios cada vez necesitan espacios mayores de almacenamiento en línea para guardar su información personal. Este reto motiva a los investigadores a diseñar y evaluar nuevas infraestructuras de almacenamiento de datos personales. En esta tesis, nos centramos en dos arquitecturas emergentes de almacenamiento de datos personales: las Nubes Personales (centralización) y los sistemas de almacenamiento social (descentralización). Creemos que, pese a su creciente popularidad, estos sistemas requieren de un mayor estudio científico. En la Parte I de esta disertación, examinamos aspectos referentes a la operación interna y el rendimiento de varias Nubes Personales. Concretamente, nuestra primera contribución es desvelar la operación interna e infraestructura de una Nube Personal de gran escala (UbuntuOne, U1). Además, proporcionamos un estudio de la actividad interna de U1 que incluye la carga diaria soportada, el comportamiento de los usuarios y el rendimiento de su sistema de metadatos. También sugerimos mejoras sobre U1 que pueden ser de utilidad en sistemas similares. Por otra parte, en esta tesis medimos y caracterizamos el rendimiento del servicio de REST APIs ofrecido por varias Nubes Personales (velocidad de transferencia, variabilidad, etc.). También demostramos que la combinación de REST APIs sobre cuentas gratuitas de usuario puede dar lugar a abusos por parte de usuarios malintencionados. Esto nos motiva a proponer mecanismos para limitar el impacto de esta vulnerabilidad. En la Parte II de esta tesis, estudiamos la calidad de servicio que pueden ofrecer los sistemas de almacenamiento social en términos de disponibilidad de datos, balanceo de carga y tiempos de transferencia. Nuestro interés principal es entender la manera en que fenómenos intrínsecos, como las dinámicas de conexión de los usuarios o la estructura de su red social, limitan el rendimiento de estos sistemas. También proponemos nuevos mecanismos de gestión de datos para mejorar esas limitaciones. Finalmente, diseñamos y evaluamos una arquitectura híbrida para mejorar la calidad de servicio de los sistemas de almacenamiento social que combina recursos de usuarios y de la Nube. Esta arquitectura permite al usuario decidir su equilibrio entre control de sus datos y rendimiento.Increasingly, end-users demand larger amounts of online storage space to store their personal information. This challenge motivates researchers to devise novel personal storage infrastructures. In this thesis, we focus on two popular personal storage architectures: Personal Clouds (centralized) and social storage systems (decentralized). In our view, despite their growing popularity among users and researchers, there still remain some critical aspects to address regarding these systems. In the Part I of this dissertation, we examine various aspects of the internal operation and performance of various Personal Clouds. Concretely, we first contribute by unveiling the internal structure of a global-scale Personal Cloud, namely UbuntuOne (U1). Moreover, we provide a back-end analysis of U1 that includes the study of the storage workload, the user behavior and the performance of the U1 metadata store. We also suggest improvements to U1 (storage optimizations, user behavior detection and security) that can also benefit similar systems. From an external viewpoint, we actively measure various Personal Clouds through their REST APIs for characterizing their QoS, such as transfer speed, variability and failure rate. We also demonstrate that combining open APIs and free accounts may lead to abuse by malicious parties, which motivates us to propose countermeasures to limit the impact of abusive applications in this scenario. In the Part II of this thesis, we study the storage QoS of social storage systems in terms of data availability, load balancing and transfer times. Our main interest is to understand the way intrinsic phenomena, such as the dynamics of users and the structure of their social relationships, limit the storage QoS of these systems, as well as to research novel mechanisms to ameliorate these limitations. Finally, we design and evaluate a hybrid architecture to enhance the QoS achieved by a social storage system that combines user resources and cloud storage to let users infer the right balance between user control and QoS

Calibración de un algoritmo de detección de anomalías marítimas basado en la fusión de datos satelitales

La fusión de diferentes fuentes de datos aporta una ayuda significativa en el proceso de toma de decisiones. El presente artículo describe el desarrollo de una plataforma que permite detectar anomalías marítimas por medio de la fusión de datos del Sistema de Información Automática (AIS) para seguimiento de buques y de imágenes satelitales de Radares de Apertura Sintética (SAR). Estas anomalías son presentadas al operador como un conjunto de detecciones que requieren ser monitoreadas para descubrir su naturaleza. El proceso de detección se lleva adelante primero identificando objetos dentro de las imágenes SAR a través de la aplicación de algoritmos CFAR, y luego correlacionando los objetos detectados con los datos reportados mediante el sistema AIS. En este trabajo reportamos las pruebas realizadas con diferentes configuraciones de los parámetros para los algoritmos de detección y asociación, analizamos la respuesta de la plataforma y reportamos la combinación de parámetros que reporta mejores resultados para las imágenes utilizadas. Este es un primer paso en nuestro objetivo futuro de desarrollar un sistema que ajuste los parámetros en forma dinámica dependiendo de las imágenes disponibles.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

ALLBUS-Bibliographie: (32. Fassung, Stand: März 2018)

Die Allgemeine Bevölkerungsumfrage der Sozialwissenschaften (ALLBUS) dient der Erhebung aktueller Daten über Einstellungen, Verhaltensweisen und Sozialstruktur der Bevölkerung in der Bundesrepublik Deutschland. Seit 1980 wird alle zwei Jahre ein repräsentativer Querschnitt der Bevölkerung mit einem teils stetigen, teils variablen Fragenprogramm befragt. Die Daten stehen unmittelbar nach ihrer benutzergerechten Aufbereitung und Dokumentation gegen Ende des betreffenden Erhebungsjahres allen Interessenten für Forschung und Lehre zur Verfügung. Der ALLBUS ist ein Vorhaben von GESIS - Leibniz-Institut für Sozialwissenschaften, das in Kooperation mit einem wissenschaftlichen Beirat, dem ALLBUS-Ausschuss, realisiert wird. Die Arbeitsgruppe ALLBUS in der Abteilung Dauerbeobachtung der Gesellschaft ist für das Forschungsprogramm und das Gesamtdesign des ALLBUS zuständig. Sie bereitet in enger Zusammenarbeit mit dem ALLBUS-Ausschuss die Studien vor und führt sie zusammen mit einem privaten Umfrageinstitut durch. Die Aufbereitung und Kumulation von Datensätzen, die Datendokumentation, der Datenvertrieb und die Archivierung erfolgen durch die Abteilung Datenarchiv für Sozialwissenschaften. Die ALLBUS-Bibliographie dokumentiert Arbeiten mit ALLBUS-Daten, die in Büchern oder Fachzeitschriften veröffentlicht sind oder als "graue Literatur" (in Form von Arbeitsberichten, Vortragsmanuskripten, Diplom-, Magister-, Master- und Bachelorarbeiten usw.) vorliegen. Berücksichtigt werden auch Veröffentlichungen, die auf Daten der ISSP-Plus-Studie (ZA-Nr.2003) beruhen, in der u.a. Items des ALLBUS 1988 repliziert wurden. Die vorliegende 32. Fassung der ALLBUS-Bibliographie enthält 2933 Arbeiten, 135 mehr als die letzte Ausgabe von 2017. Mit Erscheinen der 32. Auflage werden die bibliographischen Angaben der jeweils neu aufgenommenen Arbeiten auch in unserem Internetangebot dokumentiert

Archaeology on the Apulian – Lucanian Border

The broad valley of the Bradano river and its tributary the Basentello separates the Apennine mountains in Lucania from the limestone plateau of the Murge in Apulia in South East Italy. For millennia the valley has functioned both as a cultural and political divide between the two regions, and as a channel for new ideas transmitted from South to North or vice versa depending on the political and economic conditions of the time. Archaeology on the Apulian – Lucanian Border aims to explain how the pattern of settlement and land use changed in the valley over the whole period from Neolithic to Late Medieval, taking account of changing environmental conditions, and setting the changes in a broader political, social and cultural context. There are three levels of focus. The first is on the results of a field survey (1996-2006) in the Basentello valley by teams from the Universities of Alberta, Edinburgh, and Bari, directed by the authors. The second concerns the discoveries of earlier field surveys in the late 1960s and early 1970s undertaken in connection with excavations on Botromagno near Gravina in Puglia. The third is a much broader synthesis of the results of recent scholarship using archaeological, epigraphic and literary sources to reconstruct an archaeological history of the valley and the surrounding area. The creation of a vast imperial estate at Vagnari around the end of the 1st century BC and its long-lasting impact on the pattern of settlement in the area is a significant theme in the later chapters of the book