Name of the participant: Tanja Hagemann
Description of the IT research project: An increasingly networked society striving for an “Internet of Everything” (IoE) in the context of 5G network roll-out will inevitably have to address the question of how to ensure the security and stability of a highly dynamic and data-driven infrastructure in the future. Cloud infrastructures in particular play a key role in this context. The increasing virtualization of services is creating growing complexity in the cloud, making it almost impossible to monitor and holistically manage them without additional support and automation. However, uninterrupted services with guaranteed latency and other QoS parameters are a mandatory requirement for many of the data-driven and autonomous IoE applications.
ALMA’s goal is to design and develop a holistic AIOps module for the automated maintenance of a public-cloud environment. For this purpose novel methods for aggregating different data sources are designed and a combination of rule-based and statistical methods as well as machine learning methods are implemented. Overall, this should enable early detection of relevant performance anomalies and their automated root cause analysis.
Software Campus partners: TU Berlin, Huawei
Implementation period: 02/2020 – 02/2022
