A Survey of Fault-Tolerance Techniques for Embedded Systems from the Perspective of Power, Energy, and Thermal Issues

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones

On Dynamic Monitoring Methods for Networks-on-Chip

Rapid ongoing evolution of multiprocessors will lead to systems with hundreds of processing cores integrated in a single chip. An emerging challenge is the implementation of reliable and efficient interconnection between these cores as well as other components in the systems. Network-on-Chip is an interconnection approach which is intended to solve the performance bottleneck caused by traditional, poorly scalable communication structures such as buses. However, a large on-chip network involves issues related to congestion problems and system control, for instance. Additionally, faults can cause problems in multiprocessor systems. These faults can be transient faults, permanent manufacturing faults, or they can appear due to aging. To solve the emerging traffic management, controllability issues and to maintain system operation regardless of faults a monitoring system is needed. The monitoring system should be dynamically applicable to various purposes and it should fully cover the system under observation. In a large multiprocessor the distances between components can be relatively long. Therefore, the system should be designed so that the amount of energy-inefficient long-distance communication is minimized. This thesis presents a dynamically clustered distributed monitoring structure. The monitoring is distributed so that no centralized control is required for basic tasks such as traffic management and task mapping. To enable extensive analysis of different Network-on-Chip architectures, an in-house SystemC based simulation environment was implemented. It allows transaction level analysis without time consuming circuit level implementations during early design phases of novel architectures and features. The presented analysis shows that the dynamically clustered monitoring structure can be efficiently utilized for traffic management in faulty and congested Network-on-Chip-based multiprocessor systems. The monitoring structure can be also successfully applied for task mapping purposes. Furthermore, the analysis shows that the presented in-house simulation environment is flexible and practical tool for extensive Network-on-Chip architecture analysis.Siirretty Doriast

Contribution à l’ordonnancement dynamique, tolérant aux fautes, de tâches pour les systèmes embarqués temps-réel multiprocesseurs

The thesis is concerned with online mapping and scheduling of tasks on multiprocessor embedded systems in order to improve the reliability subject to various constraints regarding e.g. time, or energy. To evaluate system performances, the number of rejected tasks, algorithm complexity and resilience assessed by injecting faults are analysed. The research was applied to: (i) the primary/backup approach technique, which is a fault tolerant one based on two task copies, and (ii) the scheduling algorithms for small satellites called CubeSats. The chief objective for the primary/backup approach is to analyse processor allocation strategies, devise novel enhancing scheduling methods and to choose one, which significantly reduces the algorithm run-time without worsening the system performances. Regarding CubeSats, the proposed idea is to gather all processors built into satellites on one board and design scheduling algorithms to make CubeSats more robust as to the faults. Two real CubeSat scenarios are analysed and it is found that it is useless to consider systems with more than six processors and that the presented algorithms perform well in a harsh environment and with energy constraints.La thèse se focalise sur le placement et l’ordonnancement dynamique des tâches sur les systèmes embarqués multiprocesseurs pour améliorer leur fiabilité tout en tenant compte des contraintes telles que le temps réel ou l’énergie. Afin d’évaluer les performances du système, le nombre de tâches rejetées, la complexité de l’algorithme et la résilience estimée en injectant des fautes sont principalement analysés. La recherche est appliquée (i) à l’approche de « primary/backup » qui est une technique de tolérance aux fautes basée sur deux copies d’une tâche et (ii) aux algorithmes de placement pour les petits satellites appelés CubeSats. Quant à l’approche de « primary/backup », l’objectif principal est d’étudier les stratégies d’allocation des processeurs, de proposer de nouvelles méthodes d’amélioration pour l’ordonnancement et d’en choisir une qui diminue considérablement la durée de l’exécution de l’algorithme sans dégrader les performances du système. En ce qui concerne les CubeSats, l’idée est de regrouper tous les processeurs à bord et de concevoir des algorithmes d’ordonnancement afin de rendre les CubeSats plus robustes. Les scénarios provenant de deux CubeSats réels sont étudiés et les résultats montrent qu’il est inutile de considérer les systèmes ayant plus de six processeurs et que les algorithmes proposés fonctionnent bien même avec des capacités énergétiques limitées et dans un environnement hostile

Methodologies synthesis

This deliverable deals with the modelling and analysis of interdependencies between critical infrastructures, focussing attention on two interdependent infrastructures studied in the context of CRUTIAL: the electric power infrastructure and the information infrastructures supporting management, control and maintenance functionality. The main objectives are: 1) investigate the main challenges to be addressed for the analysis and modelling of interdependencies, 2) review the modelling methodologies and tools that can be used to address these challenges and support the evaluation of the impact of interdependencies on the dependability and resilience of the service delivered to the users, and 3) present the preliminary directions investigated so far by the CRUTIAL consortium for describing and modelling interdependencies

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Quantitative Resilience Assessment of Critical Infrastructures using High-Performance Simulations

Accessing the resilience of large cyber-physical systems (LCPS) is essential for ensuring the continuity of operations and minimising the impact of disruptions caused by natural disasters, cyberattacks, and other stressful events. Recent empirical studies of LCPS have demonstrated the usefulness of modelling and simulation in assessing properties that emerge from component interactions, including resilience. However, the sheer complexity of CIs poses challenges for modellers: 1) Resilience assessment requires high-fidelity models that include a probabilistic model of the system and adverse events of interest, such as accidental failures or malicious activities, and a physics simulation model of LCPS processes, such as power/liquid/gas flows. 2) Assessing resilience with high statistical significance requires a systematic exploration of the space of possible adverse events and recovery from their effects. Exploring this space requires a significant amount of effort. This work offers solutions intended to help modellers overcome these difficulties by using the recent advances in modelling LCPSs and high-performance computing: i) It offers a new modelling methodology for building agent-based hybrid hierarchical stochastic models using a new domain-specific language. The new modelling approach allows easy integration of a) a variety of modelling formalisms used to model cyber-attacks on CI/LCPS; and b) a set of deterministic models, as needed by the chosen level of fidelity and specific for the modelled CI. However, the deterministic models are not the focus of this work. Such models are assumed to exist in software available from third-party vendors. ii) It presents a set of tools to support this methodology: the visual modeller and an extensible Monte Carlo simulation engine designed to utilise high-performance and cloud computing capabilities. The engine and the editor utilise modern development practices and technologies to provide a state-of-the-art solution. This thesis provides a survey of the relevant literature, summarises the progress with the modelling methodology, and presents the results published to date with case studies based on an extended Nordic32, a reference architecture of a power transmission network with the SCADA subsystem. The studies explore the effects caused by adversaries targeting IT infrastructure and demonstrate the application of a defence-in-depth approach to reduce the effects of these attacks

Adaptive Computing Systems for Aerospace

RÉSUMÉ En raison de leur complexité croissante, les systèmes informatiques modernes nécessitent de nouvelles méthodologies permettant d’automatiser leur conception et d’améliorer leurs performances. L’espace, en particulier, constitue un environnement très défavorable au maintien de la performance de ces systèmes : sans protection des rayonnements ionisants et des particules, l’électronique basée sur CMOS peut subir des erreurs transitoires, une dégradation des performances et une usure accélérée causant ultimement une défaillance du système. Les approches traditionnellement adoptees pour garantir la fiabilité du système et prolonger sa durée de vie sont basées sur la redondance, généralement établie durant la conception. En revanche, ces solutions sont coûteuses et parfois inefficaces, puisqu'elles augmentent la taille et la complexité du système, l'exposant à des risques plus élevés de surchauffe et d'erreurs. Les conséquences de ces limites sont d'autant plus importantes lorsqu'elles s’appliquent aux systèmes critiques (e.g., contraintes par le temps ou dont l’accès est limité) qui doivent être en mesure de prendre des décisions sans intervention humaine. Sur la base de ces besoins et limites, le développement en aérospatial de systèmes informatiques avec capacités adaptatives peut être considéré comme la solution la plus appropriée pour les dispositifs intégrés à haute performance. L’informatique auto-adaptative offre un potentiel sans égal pour assurer la création d’une génération d’ordinateurs plus intelligents et fiables. Qui plus est, elle répond aux besoins modernes de concevoir et programmer des systèmes informatiques capables de répondre à des objectifs en conflit. En nous inspirant des domaines de l’intelligence artificielle et des systèmes reconfigurables, nous aspirons à développer des systèmes informatiques auto-adaptatifs pour l’aérospatiale qui répondent aux enjeux et besoins actuels. Notre objectif est d’améliorer l’efficacité de ces systèmes, leur tolerance aux pannes et leur capacité de calcul. Afin d’atteindre cet objectif, une analyse expérimentale et comparative des algorithmes les plus populaires pour l’exploration multi-objectifs de l’espace de conception est d’abord effectuée. Les algorithmes ont été recueillis suite à une revue de la plus récente littérature et comprennent des méthodes heuristiques, évolutives et statistiques. L’analyse et la comparaison de ceux-ci permettent de cerner les forces et limites de chacun et d'ainsi définir des lignes directrices favorisant un choix optimal d’algorithmes d’exploration. Pour la création d’un système d’optimisation autonome—permettant le compromis entre plusieurs objectifs—nous exploitons les capacités des modèles graphiques probabilistes. Nous introduisons une méthodologie basée sur les modèles de Markov cachés dynamiques, laquelle permet d’équilibrer la disponibilité et la durée de vie d’un système multiprocesseur. Ceci est obtenu en estimant l'occurrence des erreurs permanentes parmi les erreurs transitoires et en migrant dynamiquement le calcul sur les ressources supplémentaires en cas de défaillance. La nature dynamique du modèle rend celui-ci adaptable à différents profils de mission et taux d’erreur. Les résultats montrent que nous sommes en mesure de prolonger la durée de vie du système tout en conservant une disponibilité proche du cas idéal. En raison des contraintes de temps rigoureuses imposées par les systèmes aérospatiaux, nous étudions aussi l’optimisation de la tolérance aux pannes en présence d'exigences d’exécution en temps réel. Nous proposons une méthodologie pour améliorer la fiabilité du calcul en présence d’erreurs transitoires pour les tâches en temps réel d’un système multiprocesseur homogène avec des capacités de réglage de tension et de fréquence. Dans ce cadre, nous définissons un nouveau compromis probabiliste entre la consommation d’énergie et la tolérance aux erreurs. Comme nous reconnaissons que la résilience est une propriété d’intérêt omniprésente (par exemple, pour la conception et l’analyse de systems complexes génériques), nous adaptons une définition formelle de celle-ci à un cadre probabiliste dérivé à nouveau de modèles de Markov cachés. Ce cadre nous permet de modéliser de façon réaliste l’évolution stochastique et l’observabilité partielle des phénomènes du monde réel. Nous proposons un algorithme permettant le calcul exact efficace de l’étape essentielle d’inférence laquelle est requise pour vérifier des propriétés génériques. Pour démontrer la flexibilité de cette approche, nous la validons, entre autres, dans le contexte d’un système informatisé reconfigurable pour l’aérospatiale. Enfin, nous étendons la portée de nos recherches vers la robotique et les systèmes multi-agents, deux sujets dont la popularité est croissante en exploration spatiale. Nous abordons le problème de l’évaluation et de l’entretien de la connectivité dans le context distribué et auto-adaptatif de la robotique en essaim. Nous examinons les limites des solutions existantes et proposons une nouvelle méthodologie pour créer des géométries complexes connectées gérant plusieurs tâches simultanément. Des contributions additionnelles dans plusieurs domaines sont résumés dans les annexes, nommément : (i) la conception de CubeSats, (ii) la modélisation des rayonnements spatiaux pour l’injection d’erreur dans FPGA et (iii) l’analyse temporelle probabiliste pour les systèmes en temps réel. À notre avis, cette recherche constitue un tremplin utile vers la création d’une nouvelle génération de systèmes informatiques qui exécutent leurs tâches d’une façon autonome et fiable, favorisant une exploration spatiale plus simple et moins coûteuse.----------ABSTRACT Today's computer systems are growing more and more complex at a pace that requires the development of novel and more effective methodologies to automate their design. Space, in particular, represents a challenging environment: without protection from ionizing and particle radiation, CMOS-based electronics are subject to transients faults, performance degradation, accelerated wear, and, ultimately, system failure. Traditional approaches adopted to guarantee reliability and extended lifetime are based on redundancy that is established at design-time. These solutions are expensive and sometimes inefficient, as they increase the complexity and size of a system, exposing it to higher risks of overheating and incurring in radiation-induced errors. Moreover, critical systems---e.g., time-constrained ones and those where access is limited---must be able to cope with pivotal situations without relying on human intervention. Hence, the emerging interest in computer systems with adaptive capabilities as the most suitable solution for novel high-performance embedded devices for aerospace. Self-adaptive computing carries unmatched potential and great promises for the creation of a new generation of smart, more reliable computers, and it addresses the challenge of designing and programming modern and future computer systems that must meet conflicting goals. Drawing from the fields of artificial intelligence and reconfigurable systems, we aim at developing self-adaptive computer systems for aerospace. Our goal is to improve their efficiency, fault-tolerance, and computational capabilities. The first step in this research is the experimental analysis of the most popular multi-objective design-space exploration algorithms for high-level design. These algorithms were collected from the recent literature and include heuristic, evolutionary, and statistical methods. Their comparison provides insights that we use to define guidelines for the choice of the most appropriate optimization algorithms, given the features of the design space. For the creation of a self-managing optimization framework---enabling the adaptive trade-off of multiple objectives---we leverage the tools of probabilistic graphical models. We introduce a mechanism based on dynamic hidden Markov models that balances the availability and lifetime of multiprocessor systems. This is achieved by estimating the occurrence of permanent faults amid transient faults, and by dynamically migrating the computation on excess resources, when failure occurs. The dynamic nature of the model makes it adjustable to different mission profiles and fault rates. The results show that we are able to lead systems to extended lifetimes, while keeping their availability close to ideal. On account of the stringent timing constraints imposed by aerospace systems, we then investigate the optimization of fault-tolerance under real-time requirements. We propose a methodology to improve the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on a homogeneous multiprocessor system with voltage and frequency scaling capabilities. In this framework, we take advantage of probability theory to define a novel trade-off between power consumption and fault-tolerance. As we recognize that resilience is a pervasive property of interest (e.g., for the design and analysis of generic complex systems), we adapt a formal definition of it to one more probabilistic framework derived from hidden Markov models. This allows us to realistically model the stochastic evolution and partial observability of complex real-world environments. Within this framework, we propose an efficient algorithm for the exact computation of the essential inference step required to construct generic property checking. To demonstrate the flexibility of this approach, we validate it in the context, among others, of a self-aware, reconfigurable computing system for aerospace. Finally, we move the scope of our research towards robotics and multi-agent systems: a topic of thriving popularity for space exploration. We tackle the problem of connectivity assessment and maintenance in the distributed and self-adaptive context of swarm robotics. We review the limitations of existing solutions and propose a novel methodology to create connected complex geometries for multiple task coverage. Additional contributions in the areas of (i) CubeSat design, (ii) the modelling of space radiation for FPGA fault-injection, and (iii) probabilistic timing analysis for real-time systems are summarized in the appendices. In the author's opinion, this research provides a number of useful stepping stones for the creation of a new generation of computing systems that autonomously---and reliably---perform their tasks for longer periods of time, fostering simpler and cheaper space exploration

Network-on-Chip Topologies: Potentials, Technical Challenges, Recent Advances and Research Direction

Integration technology advancement has impacted the System-on-Chip (SoC) in which heterogeneous cores are supported on a single chip. Based on the huge amount of supported heterogeneous cores, efficient communication between the associated processors has to be considered at all levels of the system design to ensure global interconnection. This can be achieved through a design-friendly, flexible, scalable, and high-performance interconnection architecture. It is noteworthy that the interconnections between multiple cores on a chip present a considerable influence on the performance and communication of the chip design regarding the throughput, end-to-end delay, and packets loss ratio. Although hierarchical architectures have addressed the majority of the associated challenges of the traditional interconnection techniques, the main limiting factor is scalability. Network-on-Chip (NoC) has been presented as a scalable and well-structured alternative solution that is capable of addressing communication issues in the on-chip systems. In this context, several NoC topologies have been presented to support various routing techniques and attend to different chip architectural requirements. This book chapter reviews some of the existing NoC topologies and their associated characteristics. Also, application mapping algorithms and some key challenges of NoC are considered