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High efficiency data collection remains a great challenge for HPC reliability and resilience, yet may pave the way to overcome the barrier before fault prediction. Not only for increasing scalability up to exascale systems but even for contemporary supercomputer architectures does it require substantial efforts to efficiently collect and analyze data that contains system fault information within the framework of faults prediction. In this term, the article mainly focuses on efficient data collection and data preprocessing, preferring to optimize an effective framework to improve the efficiency of data collection in petascale system. The core of our framework includes a data collection acceleration layer scheduled by H2FS, a further detailed information get by performance analysis tool, as well as a new method for log template extraction, which all attribute to a more efficient and convenient framework for the real-time data collection. Hereafter, we conducted extensive tests based on a petascale system to verify the solution, and the experimental results demonstrate to be effectiveness and scalability of our framework.

Multivariate time series prediction, with a profound impact on human social life, has been attracting growing interest in machine learning research. However, the task of time series forecasting is very challenging because it is affected by many complex factors. For example, in predicting traffic and solar power generation, weather can bring great trouble. In particular, for strictly periodic time series, if the periodic information can be extracted from the historical sequence data to the maximum, the accuracy of the prediction will be greatly improved. At present, for time series prediction tasks, the sequence models based on RNN have made great progress. However, the sequence models has difficulty in capturing global information, failing to well highlight the periodic characteristics of the time series. But the this problem can be solved by CNN models. So in this paper, we propose a model called Multiple CNNs to solve the problem of periodic multivariate time series prediction. The working process of Multiple CNNs is analyzing the periodicity of time series, extracting the closeness and the long and short periodic information of the predicted target respectively, and finally integrating the characteristics of the three parts to make the prediction. Moreover, the model is highly flexible, which allows users to freely adjust the cycle span set in the model according to their own data characteristics. Tests on two large real-world datasets, show that our model has a strong advantage over other time series prediction methods.