1. Predictive Maintenance in Data Centers with Self-Learning AI for NTT (Britta Hilt and Kentaro Ono)

NTT FACILITIES provides critical facility management services for more than 7,000 data centers worldwide. Cost-expensive action is taken to minimize risk of air con failure, like replacing critical components prior to their actual life time end. Activities were started to use self-learning multi-layer AI with the objective to maximize compressor run time, to decrease replacement and to avoid additional backups. This Japanese project was executed with the support of the German company IS Predict. The reliability of the solution was beyond NTT FACILITIES´ expectations who had already executed similar AI project. Accuracy in failure prediction for air condition system compressors of 98% was realized.

2. Artificial Intelligence for Beyond5G Network Management – Challenges and Opportunities (Rachana Desai & Edwin Yaqub)

Global rise in demand has exacerbated the limitations of current 4G networks. Therefore, 5G and Beyond (B5G) networks are being designed to deliver the performance levels expected by the next generation of applications. In the EU research project Ariadne, the goal is to employ Machine Learning and Artificial Intelligence techniques in various B5G deployment scenarios, with the objective to reconfigure network resources as required to ensure continuous reliable high-bandwidth connectivity.

3. Using Machine Learning for Root Issue Analysis in Telco’s Networks (Silvia Veronese)

Root issue identification in large scale network is a key component to proactive fault analysis. In this presentation we describe a methodology which integrates multiple data domains for telco networks driven by big data sets. We aim to automate the intelligence required for operations, networking, services and care/support. This starts with predicting service impacting failures (broken devices, sub-optimal performance, changes in utilization, etc.) primarily in real-time. With this capability for the NOC or SOC, we strive to deliver reduced MTTR ( Mean time to repair) as a business value but in an actionable (and later in an automated) manner. These analytics capabilities extend to the subscriber who can be enabled to solve their own device/service issues. This self-care is a holy grail for CSPs (Cable Service Providers) as they endeavor to dramatically reduce their support costs.