Attendees
Vidyashre
Renuka
Hemashree
| Sl. No. | Topic | Presenter | Notes |
|---|---|---|---|
| 1 | AlgoSelector Update | Supervised Learning:
Started with the implementation. Request for Feedback. Goal For Next Week: Unsupervised Learning. | |
| 2 | Failure Emulation Update | No Breakthrough Yet. Still Trying with stress-ng (interrupts, system-calls, ...) | |
| 3 | FP Model Development Update | Documentation: Pending Demo models/failure_prediction/ *.ipynb, models/failure_prediction/static_htmls, models/failure_prediction/data_preprocessing. Rohit presented his work as part of the 'Final-Presentation' of his Internship. Beth: Were the results expected? Girish/Rohit: Steve: Out of 26 features, only 6 features are used. This choice is based on? Girish/Rohit: Girish: Going ahead, we will try with better data and improve these models. Rohit: Continue to contribute to Thoth. Target date for Whitepaper: October 20th. | |
| 4 | Data Extraction Tool Status | Stuck at Prometheus (tested), Elasticsearch(untested). | |
| 5 | Synthetic Data Generation - GANs | Girish et. al., | Generate Monitoring/logging data using GANs. This is a new project to create synthetic data for testing of operational ML algorithms. The idea is to create this data set to be used across the industry as a reference data set. Hemashree, Vidyashree, Renuka |
| 6 | Exploration: Openstack Log Analysis | Openstack Logs: Existing Approaches: ELK - Kibana + Alarms. Analysis: Common approach is using NLP. Approach: Google's BERT model for Openstack Logs. Problems: Anomaly Detection + Pattern Analysis Deekshita and Rakshita - NLP, Try with Openstack Logs (??) Data: https://github.com/logpai/loghub/tree/master/OpenStack Use of NLP for Openstack Logs has already been tried: https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf . This work will be used as a reference. |
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