On Improving Deep Learning Trace Analysis with System Call Arguments

Fournier, Quentin; Aloise, Daniel; Azhari, Seyed Vahid; Tetreault, François

doi:10.1109/msr52588.2021.00025

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…However, this algorithm has some operations with a quadratic complexity. In our example, we use the code presented by Fournier et al 51 to run and analyze its execution and its efficiency. This model runs on sequences of system calls issued by a stack of web applications.…”

Section: Resultsmentioning

confidence: 99%

Visualization of profiling and tracing in CPU‐GPU programs

Fiorini

Dagenais

2022

Concurrency and Computation

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Summary As the complexity of the toolchain increases for heterogeneous CPU‐GPU systems, the needs for comprehensive tracing and debugging tools also grows. Heterogeneous platforms bring new possibilities but also new performance issues that are hard to detect. Some techniques that were used on CPU programs are now adapted to GPUs. However, there are some concepts specific to GPUs, like SIMD processing, and the effects of the close interactions between the CPUs and the GPUs, with shared virtual memory and user‐level queues. Multiple sources of data need to be extracted and correlated to obtain a more global view of the performance. In this article, we introduce a novel approach for measuring and visualizing performance defects inside CPU‐GPU programs by combining kernel events, compute kernel events, user API calls and memory transfers. We created two new views that combine this information, to help provide a global view. This framework uses the open source user queue system described in the HSA standard. It can easily be adapted to any user queue system for heterogeneous computing devices. We compare this framework with current existing tools and test it against the Rodinia benchmark. We look at how the execution behavior affects the tracing and profiling overhead and we use Trace Compass to visualize the resulting trace.

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Section: Resultsmentioning

confidence: 99%

Visualization of profiling and tracing in CPU‐GPU programs

Fiorini

Dagenais

2022

Concurrency and Computation

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“…In the latter, anomaly detection will be based on the dissimilarity of the log messages in the test and training data. This approach has been demonstrated by previous work [10], [4] and is sometimes referred as "novelty detection" [11].…”

Section: Introductionmentioning

confidence: 86%

How to Configure Masked Event Anomaly Detection on Software Logs?

Nyyssala

Mäntylä

Varela

2022

2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Software Log anomaly event detection with masked event prediction has various technical approaches with countless configurations and parameters. Our objective is to provide a baseline of settings for similar studies in the future. The models we use are the N-Gram model, which is a classic approach in the field of natural language processing (NLP), and two deep learning (DL) models long short-term memory (LSTM) and convolutional neural network (CNN). For datasets we used four datasets Profilence, BlueGene/L (BGL), Hadoop Distributed File System (HDFS) and Hadoop. Other settings are the size of the sliding window which determines how many surrounding events we are using to predict a given event, mask position (the position within the window we are predicting), the usage of only unique sequences, and the portion of data that is used for training. The results show clear indications of settings that can be generalized across datasets. The performance of the DL models does not deteriorate as the window size increases while the N-Gram model shows worse performance with large window sizes on the BGL and Profilence datasets. Despite the popularity of Next Event Prediction, the results show that in this context it is better not to predict events at the edges of the subsequence, i.e., first or last event, with the best result coming from predicting the fourth event when the window size is five. Regarding the amount of data used for training, the results show differences across datasets and models. For example, the N-Gram model appears to be more sensitive toward the lack of data than the DL models. Overall, for similar experimental setups we suggest the following general baseline: Window size 10, mask position second to last, do not filter out non-unique sequences, and use a half of the total data for training.

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“…Fourthly, other deep learning-based and NLP-based approaches ignore events arguments in their modeling. Event arguments such as process name, message, and event type contain beneficial details that increase detection quality [39]. We use these arguments to train our model.…”

Section: Previous Workmentioning

confidence: 99%

Anomaly detection in microservice environments using distributed tracing data analysis and NLP

et al. 2022

Self Cite

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In recent years DevOps and agile approaches like microservice architectures and Continuous Integration have become extremely popular given the increasing need for flexible and scalable solutions. However, several factors such as their distribution in the network, the use of different technologies, their short life, etc. make microservices prone to the occurrence of anomalous system behaviours. In addition, due to the high degree of complexity of small services, it is difficult to adequately monitor the security and behavior of microservice environments. In this work, we propose an NLP (natural language processing) based approach to detect performance anomalies in spans during a given trace, besides locating release-over-release regressions. Notably, the whole system needs no prior knowledge, which facilitates the collection of training data. Our proposed approach benefits from distributed tracing data to collect sequences of events that happened during spans. Extensive experiments on real datasets demonstrate that the proposed method achieved an F_score of 0.9759. The results also reveal that in addition to the ability to detect anomalies and release-over-release regressions, our proposed approach speeds up root cause analysis by means of implemented visualization tools in Trace Compass.

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On Improving Deep Learning Trace Analysis with System Call Arguments

Cited by 7 publications

References 17 publications

Visualization of profiling and tracing in CPU‐GPU programs

Visualization of profiling and tracing in CPU‐GPU programs

How to Configure Masked Event Anomaly Detection on Software Logs?

Anomaly detection in microservice environments using distributed tracing data analysis and NLP

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