I try and fit components together logically so that they can make the most of what the technology offers. I work predominantly in the OSS world on new access technologies like 5G and implementations like the Internet of Things. I want to achieve not just the deployment of these capabilities but to also to let them operate seamlessly. The following is my view of the opportunity of closed-loop remediation.
For closed-loop remediation there are two main tenets: 1. you can stream all network event data in a machine learning engine and apply an algorithm like K-Nearest Neighbour 2. you can expose remediation APIs on your programmable network.
All of this requires a lot of technology convergence but: What’s actually needed to make everything convergent?
Let’s start with Streaming. Traditionally we used SNMP for event data, traps & alarms and when that didn’t work we deployed physical network probes. Now it’s Kafka stream once implementations where a streams of logs of virtualised infrastructure and virtualised functions are parsed in a data streaming architecture into different big data persistence.
The Machine Learning engine, I’m keenest of FlinkML at the moment, works on the big data persistence providing the largest possible corpus of event data. The ML K-NN can analyse network behaviour and examine patterns that are harder for human operation teams to spot. It can also predict timed usage behaviours and scale the network accordingly.
I am increasingly looking at Openstack and Open Source Mano as a NFVO platform orchestrating available virtualised network functions. The NFVO can expose a customer facing service or underlying RFSs. But to truly operate the ML should have access to the RFS layer. This is the hardest part and is dependent upon the underlying design pattern implementation of the Virtual Network Functions. This though is a topic for another blog post.