The Linux Foundation ONAP project promises to automate not just the orchestration and lifecycle management (LCM) of network services, but also service assurance through something called closed loop automation. Closed loop automation works as follows:
All monitoring data — events, alarms, logs, metrics, files — go to an analytics engine. A closed loop recipe, i.e. a sequence of big data analytics microservices, process that data. For example, a sustained increase in packet loss may trigger a packet loss event.
The event from the analytics engine goes to a policy engine. The policy engine decides what action to take. For example, the policy engine may decide to do nothing if the packet loss is below a threshold. On the other hand, it may publish an action for the orchestration/LCM side of the house. In the above case, it could trigger a scale-out or configure throttling settings.
However, life is not usually this straightforward where every closed loop can be clearly defined ahead of time. Wouldn't it be nice if an AI/ML microservice was part of the closed loop recipe? This way, we wouldn't have to figure out every possible closed loop recipe permutation and the AI/ML microservice could assist.
Now we can assist you with the above needs. We have partnered with another SF Bay Area startup called Davinci Networks. Davinci Networks entire focus is on enabling intelligent networks through AI/ML microservices built using specialized deep neural networks. These deep neural networks use network internal monitoring data and combine it with external data to improve the quality of intelligence. Through our partnership, we will provide professional services, training, and over time products that span across ONAP and AI/ML microservices.
Curious to learn more? Sign up for one of our joint 1.5 courses. Get a boost in your career by learning about 2 hot technologies: ONAP + AI/ML.