no code implementations • 22 May 2024 • Thorsten Wittkopp, Philipp Wiesner, Odej Kao
We evaluated our approach on a large-scale production log data set of 44. 3 million log lines, which contains 80 failures, whose root causes were labeled by experts.
no code implementations • 22 Dec 2023 • Thorsten Wittkopp, Alexander Acker, Odej Kao
The realm of AIOps is transforming IT landscapes with the power of AI and ML.
no code implementations • 22 Aug 2023 • Dominik Scheinert, Philipp Wiesner, Thorsten Wittkopp, Lauritz Thamsen, Jonathan Will, Odej Kao
However, big data analytics jobs across users can share many common properties: they often operate on similar infrastructure, using similar algorithms implemented in similar frameworks.
no code implementations • 25 Jan 2023 • Thorsten Wittkopp, Dominik Scheinert, Philipp Wiesner, Alexander Acker, Odej Kao
Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect.
no code implementations • 26 Nov 2021 • Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert, Odej Kao
In this paper, we present a taxonomy for different kinds of log data anomalies and introduce a method for analyzing such anomalies in labeled datasets.
no code implementations • 16 Nov 2021 • Dominik Scheinert, Alireza Alamgiralem, Jonathan Bader, Jonathan Will, Thorsten Wittkopp, Lauritz Thamsen
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important.
no code implementations • 2 Nov 2021 • Thorsten Wittkopp, Philipp Wiesner, Dominik Scheinert, Alexander Acker
With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastructures.
no code implementations • 20 Sep 2021 • Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski, Jasmin Bogatinovski, Dominik Scheinert, Wu Fan, Odej Kao
Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines.
1 code implementation • 29 Jul 2021 • Dominik Scheinert, Lauritz Thamsen, Houkun Zhu, Jonathan Will, Alexander Acker, Thorsten Wittkopp, Odej Kao
First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts.
no code implementations • 1 Feb 2021 • Thorsten Wittkopp, Alexander Acker
Neither original training data nor model parameters need to be transmitted.
no code implementations • 15 Jan 2021 • Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Florian Schmidt, Thorsten Wittkopp, Soeren Becker, Jorge Cardoso, Odej Kao
Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems.
1 code implementation • 7 Jul 2020 • Alexander Acker, Thorsten Wittkopp, Sasho Nedelkoski, Jasmin Bogatinovski, Odej Kao
First, KPI types like CPU utilization or allocated memory are very different and hard to be expressed by the same model.