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    Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives

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    © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, Vol. 53, No. 5, Article 95. Publication date: September 2020. https://doi.org/10.1145/3403956[EN] Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.This work has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the FET-HPC Grant Agreement No. 801137 (RECIPE). Jaume Abella was also partially supported by the Ministry of Economy and Competitiveness of Spain under Contract No. TIN2015-65316-P and under Ramon y Cajal Postdoctoral Fellowship No. RYC-2013-14717, as well as by the HiPEAC Network of Excellence. Ramon Canal is partially supported by the Generalitat de Catalunya under Contract No. 2017SGR0962.Canal, R.; Hernández Luz, C.; Tornero-Gavilá, R.; Cilardo, A.; Massari, G.; Reghenzani, F.; Fornaciari, W.... (2020). Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives. ACM Computing Surveys. 53(5):1-32. https://doi.org/10.1145/3403956S132535Abella, J., Hernandez, C., Quinones, E., Cazorla, F. J., Conmy, P. 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IEEE Internet Com

    Applying Machine Learning to Root Cause Analysis in Agile CI/CD Software Testing Environments

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    This thesis evaluates machine learning classification and clustering algorithms with the aim of automating the root cause analysis of failed tests in agile software testing environments. The inefficiency of manually categorizing the root causes in terms of time and human resources motivates this work. The development and testing environments of an agile team at Ericsson Finland are used as this work's framework. The author of the thesis extracts relevant features from the raw log data after interviewing the team's testing engineers (human experts). The author puts his initial efforts into clustering the unlabeled data, and despite obtaining qualitative correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. The author then carries out a new round of interviews with the testing engineers, which leads to the conceptualization of ground-truth categories for the test failures. With these, the human experts label the dataset accordingly. A collection of artificial neural networks that either classify the data or pre-process it for clustering is then optimized by the author. The best solution comes in the form of a classification multilayer perceptron that correctly assigns the failure category to new examples, on average, 88.9\% of the time. The primary outcome of this thesis comes in the form of a methodology for the extraction of expert knowledge and its adaptation to machine learning techniques for test failure root cause analysis using test log data. The proposed methodology constitutes a prototype or baseline approach towards achieving this objective in a corporate environment

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    High performance computing (HPC) systems frequently suffer errors and failures from hardware components that negatively impact the performance of jobs run on these systems. We analyzed system logs from two HPC systems at Purdue University and created statistical models for memory and hard disk errors. We created a small-scale error injection testbed—using a customized QEMU build, libvirt, and Python—for HPC application programmers to test and debug their programs in a faulty environment so that programmers can write more robust and resilient programs before deploying them on an actual HPC system. The deliverables for this project are the fault injection program, the modified QEMU source code, and the statistical models used for driving the injection

    TOWARDS GENERIC SYSTEM OBSERVATION MANAGEMENT

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    Едно от най-големите предизвикателства на информатиката е да създава правилно работещи компютърни системи. За да се гарантира коректността на една система, по време на дизайн могат де се прилагат формални методи за моделиране и валидация. Този подход е за съжаление труден и скъп за приложение при мнозинството компютърни системи. Алтернативният подход е да се наблюдава и анализира поведението на системата по време на изпълнение след нейното създаване. В този доклад представям научната си работа по въпроса за наблюдение на копютърните системи. Предлагам един общ поглед на три основни страни на проблема: как трябва да се наблюдават компютърните системи, как се използват наблюденията при недетерминистични системи и как се работи по отворен, гъвкав и възпроизводим начин с наблюдения.One of the biggest challenges in computer science is to produce correct computer systems. One way of ensuring system correction is to use formal techniques to validate the system during its design. This approach is compulsory for critical systems but difficult and expensive for most computer systems. The alternative consists in observing and analyzing systems' behavior during execution. In this thesis, I present my research on system observation. I describe my contributions on generic observation mechanisms, on the use of observations for debugging nondeterministic systems and on the definition of an open, flexible and reproducible management of observations.Un des plus grands défis de l'informatique est de produire des systèmes corrects. Une manière d'assurer la correction des systèmes est d'utiliser des méthodes formelles de modélisation et de validation.Obligatoire dans le domaine des systèmes critiques, cette approche est difficile et coûteuse à mettre en place dans la plupart des systèmes informatiques.L'alternative est de vérifier le comportement des systèmes déjà développés en observant et analysant leur comportement à l'exécution.Ce mémoire présente mes contributions autour de l'observation des systèmes. Il discute de la définition de mécanismes génériques d'observation, de l'exploitation des observations pour le débogage de systèmes non déterministes et de la gestion ouverte, flexible et reproductible d'observations

    Towards efficient error detection in large-scale HPC systems

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    The need for computer systems to be reliable has increasingly become important as the dependence on their accurate functioning by users increases. The failure of these systems could very costly in terms of time and money. In as much as system's designers try to design fault-free systems, it is practically impossible to have such systems as different factors could affect them. In order to achieve system's reliability, fault tolerance methods are usually deployed; these methods help the system to produce acceptable results even in the presence of faults. Root cause analysis, a dependability method for which the causes of failures are diagnosed for the purpose of correction or prevention of future occurrence is less efficient. It is reactive and would not prevent the first failure from occurring. For this reason, methods with predictive capabilities are preferred; failure prediction methods are employed to predict the potential failures to enable preventive measures to be applied. Most of the predictive methods have been supervised, requiring accurate knowledge of the system's failures, errors and faults. However, with changing system components and system updates, supervised methods are ineffective. Error detection methods allows error patterns to be detected early to enable preventive methods to be applied. Performing this detection in an unsupervised way could be more effective as changes to systems or updates would less affect such a solution. In this thesis, we introduced an unsupervised approach to detecting error patterns in a system using its data. More specifically, the thesis investigates the use of both event logs and resource utilization data to detect error patterns. It addresses both the spatial and temporal aspects of achieving system dependability. The proposed unsupervised error detection method has been applied on real data from two different production systems. The results are positive; showing average detection F-measure of about 75%
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