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Found 11 papers, 2 papers with code
Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis
The realm of AIOps is transforming IT landscapes with the power of AI and ML.
Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics
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.
PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning
Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect.
A Taxonomy of Anomalies in Log Data
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.
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important.
LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision
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.
A2Log: Attentive Augmented Log Anomaly Detection
Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines.
Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
First, a general model is trained on all the available data for a specific scalable analytics algorithm, hereby incorporating data from different contexts.
Decentralized Federated Learning Preserves Model and Data Privacy
Neither original training data nor model parameters need to be transmitted.
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper
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.
Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction
First, KPI types like CPU utilization or allocated memory are very different and hard to be expressed by the same model.
