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Found 14 papers, 5 papers with code
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.
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments
With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments.
Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics
Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments.
Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning
Distributed file systems are widely used nowadays, yet using their default configurations is often not optimal.
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.
Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation
Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data analytics.
Evaluation of Load Prediction Techniques for Distributed Stream Processing
We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs.
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.
Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Operation and maintenance of large distributed cloud applications can quickly become unmanageably complex, putting human operators under immense stress when problems occur.
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services
Deployment, operation and maintenance of large IT systems becomes increasingly complex and puts human experts under extreme stress when problems occur.
