Deep Learning and Monitoring Metrics to Image Encoding for Detecting Applications in HPC systems

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Knowing the knowledge of applications in HPC systems not only allows designing better resource-aware scheduling policies to improve system efficiency, but also provides the opportunity to detect and prohibit malicious programs, such as bit-coin mining and password cracking applications. To this end, in our previous research, we proposed a method to detect and identify applications based on statistical features extracted from the resource consumption metrics. However, our previous experimental results showed that the model using the random forest algorithm with statistical features only worked relatively well on some specific applications; the overall accuracy was not promising. In this seminar, I will talk about our recent efforts to enhance the detection model. Specifically, we are exploring encoding time-series monitoring metrics into images and taking advantage of the deep Convolutional Neural Networks (CNNs) to classify two-dimensional images. Using encoded time-series data and CNNs within a unified framework is expected to boost the performance of the application detection and classification. Our results show that the proposed methodology achieves an average accuracy of 87%; For some specific applications, it achieves an accuracy of more than 95%.

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