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System logs are a common source of monitoring data for analyzing computing systems' behavior. Due to the complexity of modern computing systems and the large size of collected monitoring data, automated analysis mechanisms are required. Numerous machine learning and deep learning methods are proposed to address this challenge. However, due to the existence of sensitive data in system logs their analysis and storage raise serious privacy concerns. Anonymization methods could be used to clean the monitoring data before analysis. However, anonymized system logs, in general, do not provide adequate usefulness for the majority of behavioral analysis. Content-aware anonymization mechanisms such as PaRS preserve the correlation of system logs even after anonymization. This work evaluates the usefulness of anonymized system logs taken from the Taurus HPC cluster anonymized using PaRS, for behavioral analysis via recurrent neural network models., Comment: 12 pages, 7 main figures, 2 tables, Conference: International Workshop on Data-driven Resilience Research 2022

Failure rates in high performance computers rapidly increase due to the growth in system size and complexity. Hence, failures became the norm rather than the exception. Different approaches on high performance computing (HPC) systems have been introduced, to prevent failures (e. g., redundancy) or at least minimize their impacts (e. g., checkpoint and restart). In most cases, when these approaches are employed to increase the resilience of certain parts of a system, energy consumption rapidly increases, or performance significantly degrades. To address this challenge, we propose on-demand resilience as an approach to achieve adaptive resilience in HPC systems. In this work, the HPC system is considered in its entirety and resilience mechanisms such as checkpointing, isolation, and migration, are activated on-demand. Using the proposed approach, the unavoidable increase in total energy consumption and system performance degradation is decreased compared to the typical checkpoint/restart and redundant resilience mechanisms. Our work aims to mitigate a large number of failures occurring at various layers in the system, to prevent their propagation, and to minimize their impact, all of this in an energy-saving manner. In the case of failures that are estimated to occur but cannot be mitigated using the proposed on-demand resilience approach, the system administrators will be notified in view of performing further investigations into the causes of these failures and their impacts., Comment: 2 pages, to be published in Proceedings of the Work in Progress Session held in connection with the 25th EUROMICRO International Conference on Parallel, Distributed and Network-based Processing, PDP 2017

System logs constitute valuable information for analysis and diagnosis of system behavior. The size of parallel computing systems and the number of their components steadily increase. The volume of generated logs by the system is in proportion to this increase. Hence, long-term collection and storage of system logs is challenging. The analysis of system logs requires advanced text processing techniques. For very large volumes of logs, the analysis is highly time-consuming and requires a high level of expertise. For many parallel computing centers, outsourcing the analysis of system logs to third parties is the only affordable option. The existence of sensitive data within system log entries obstructs, however, the transmission of system logs to third parties. Moreover, the analytical tools for processing system logs and the solutions provided by such tools are highly system specific. Achieving a more general solution is only possible through the access and analysis system of logs of multiple computing systems. The privacy concerns impede, however, the sharing of system logs across institutions as well as in the public domain. This work proposes a new method for the anonymization of the information within system logs that employs de-identification and encoding to provide sharable system logs, with the highest possible data quality and of reduced size. The results presented in this work indicate that apart from eliminating the sensitive data within system logs and converting them into shareable data, the proposed anonymization method provides 25% performance improvement in post-processing of the anonymized system logs, and more than 50% reduction in their required storage space., Comment: 8 pages, 5 figures, for demonstration see https://www.ghiasvand.net/u/hpcmaspa17