Using AI/ML for proactive detection of network overload
This webinar was live on 12th of September at 8:30 PST - fill out the form, and we will send you a link to the recording.
AI/ML has been a trending topic for a while now, yet the application of these techniques to specific challenges remains obscure. In this session, we unveil the seamless integration of AI/ML into the domain of IP core networks to perform traffic flow forecasting (based on time series analysis).
We will present a demo of a solution that might help in predicting and preemptively addressing potential network congestion events. These issues can be caused by many reasons, and we will demonstrate how to detect those caused by various failure types.
Detection of congestion events for various resiliency levels is the first step. Additionally, we will demonstrate how to efficiently mitigate future congestion problems with the help of analytical tools similar to the one presented during our demo.
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Agenda
Introduction to network overload issues
In the first part of the event, our experts will provide an introduction to the issue of network overload.
PoC architecture
Then, we will present a proof of concept for a solution that targets these issues. We will thoroughly explain its architecture which consists of two crucial parts: the ML-based traffic flow forecasting engine and the network emulator engine.
PoC results
In this section, you will see the results our team achieved with this solution.
Conclusions
Then we will talk about possible extensions of the tool and we will give a summary of the business benefits it provides. Finally, we will sum up the webinar and provide you with key takeaways.
Q&A
At the end, you will be able to ask questions directly to the speakers regarding their PoC or any other issues you might have.
About the speakers
Tomasz Janaszka - Solution Architect
Tomasz is a Solution Architect at CodiLime. He is experienced in developing solutions for networks, supporting traffic engineering, network optimization, load balancing, and network capacity planning. He also researches the practical applications of AI/ML techniques in various networking aspects. In his spare time, he plays guitar and learns foreign languages.
Mariusz Budziński - Data Scientist
Mariusz works at CodiLime as a Data Scientist. His past projects have focused on time series analysis, and he is a co-author of a patent application based on the use of meta-learning in time series analysis. His main challenge is to provide advanced AI/ML models for network applications. In his free time, he enjoys cooking and sports.