AIOps 的实际应用

So, what else can Marvis do for us? Meet Marvis Actions, the proactive side of Marvis. Marvis identifies actions that users can take to improve their user experience. If there is action that can be taken to improve the network, it will be brought to the forefront here.

For our WAN, we see that Marvis has identified a persisting LTE signal quality issue. From here we can drill into the details of the issue and get a better sense of the impacts. Looks like I should take some action and have the antenna adjusted. This is a great example of Marvis helpfully suggesting actions we can take to make the user experience better.

Looking at our recently deployed Cupertino site, we can see that it is not meeting Service Levels. Clicking into the site, we get a closer look at the SLEs. They are broken down into three important health categories that play a role in user experience: the WAN Edge device health, the health of WAN links and paths, and the health of applications themselves. Each SLE is broken down into a simple unit of measure for the user experience called a User Minute.

Simply put, this is telling us what our user experiences on the WAN are per user, per minute. Behind these seemingly simple measurements are the complex and powerful AI models of the Mist Cloud, fed by rich telemetry from the Session Smart Network. For each SLE, we get a breakdown of the root cause of the issues identified. Whenever user experience is poor on the WAN, Mist not only tells us the root cause, but also tells us what was affected, such as the impacted applications, users, links, paths and devices.

Marvis is also ever present in the forefront of the Mist experience. You can ask Marvis questions about the network at any time. You can ask it to help you do things like troubleshoot a device or access documentation. At our our Cupertino site, we know Teams is an important collaboration application.

A particular user at the site has noticed periodic issues with poor Teams calls. Let's ask Marvis to help us out. Marvis quickly responds with a handful of Teams sessions that it determined were calls from our user yesterday. Great.

Let's ask Marvis to troubleshoot one of them. Marvis returns the end-to-end path of the session from client-to-cloud app server. We can see that Marvis points out the WAN as a source of issues that impacted the experience. Going one step further, it shows us the WAN Edges that the session traversed, and it pinpoints high network jitter between the edge devices that impacted the experience.

Think about that for a moment. A simple question. Why was my Teams call bad? A question that would historically need to be answered by top technical operators across different disciplines of expertise.

Going device to device, pouring through logs and packet captures, mountains of monitoring information just to answer where the session went and where it went wrong. A simple question, simply answered by asking Marvis.