Master's Theses
Early Warning Model (EWM) for Anomalies in Deutsche Telekom Streaming Data
Description
Through its nationwide communication infrastructure, Deutsche Telekom operates a large variety of services targeted at the needs of customers and their devices. With technological advances reaching many industries, the set of such networked daily-use devices includes not only phones but TV attachments and many more. Naturally, this combination of a high number of users plus the variety of services and devices produces a large amount of heterogeneous data. Unexpected events and anomalous behavior can easily cause service disruptions and even downtime for the system.Therefore, it is important to identify points within the streaming data that indicate deviations from normal system operation. In this context, the thesis aims to evaluate the ability to flag such anomalies early on or even predict them in advance, essentially creating an early warning model (EWM).
Prerequisites
- Knowledge in python programming.
- Familiarity with supervised learning, sensitivity analysis and timeseries.
- Skills in working with data (especially elastic and pandas)
- willingness to self-teach and strong problem-solving skills :)
Supervisor:
RTT-guided Route Servers at IXPs
Description
Problem: BGP is performance-agnostic
Solution: incorporate a delay-related metric into the best-path selection process.
Approach: Estimate the round-trip prop_delay to destinations (/24s) within the routing table of the IXP
Goal: Evaluate if it is possible to outperform BGP’s route selection criterion, in terms of latency, with a measurement-based approach.