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AIOps 的实际应用

总结 通过使用 Marvis Action 仪表板、服务级别预期和 Marvis 对话助手探索主动和被动故障排除的示例,更深入地了解人工智能原生运维。

让我们看看运维主管 Oscar 在日常工作中如何使用瞻博网络 Mist 门户来预测和响应问题。


在了解 Ocar 的经历时,您将在瞻博网络 Mist 门户中获得许多功能的简要介绍。本指南稍后将获得更深入的信息。

使用 Marvis 操作仪表板开始新的一天

奥斯卡总是从查看 Marvis Actions 仪表板开始新的一天。在此仪表板上,Marvis 确定可以改善用户体验的操作。通过遵循这些建议,Oscar 可以在用户报告影响 之前解决问题


要查找操作仪表板,请从瞻博网络 Mist 门户的左侧菜单中选择 Marvis > Marvis 操作

今天,奥斯卡注意到了美联社的八个问题。只需单击一下,他就能看到高级根本原因分析:5 个处于离线状态,1 个未通过运行状况检查,1 个存在覆盖漏洞。

Example: Marvis Actions Dashboard with AP Issues Expanded

单击“覆盖洞”项。在页面底部,Marvis 向他展示了问题发生的时间和地点。Marvis 还提供了解决此问题的建议。

Example: Coverage Hole Details and Recommended Action

奥斯卡点击查看更多信息。对于此类问题,Marvis 会显示平面图。奥斯卡准确地看到了这个AP的位置。有了这些信息,他就可以了解问题和影响,并可以跟进以确保足够的覆盖范围。

Example: AP Coverage Hole on Floorplan

Marvis Actions 视频演示

在此视频演示中,Marvis 针对信号强度不佳推荐了相关措施。

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.


接下来,Oscar 转到服务级别仪表板。这些仪表板显示可能影响用户体验的关键因素 (SLE) 的成功和失败。

在无线仪表板上,颜色编码将 Oscar 的注意力吸引到低 SLE 覆盖范围上。在左侧,他可以看到每个服务级别的总体成功率。覆盖率只有67%。在页面右侧,Oscar 看到了高级根本原因分析(右侧)。在不成功的用户体验中,90%是由于信号微弱。

Example: Low SLE

奥斯卡点击仔细观察。在“根本原因分析”页面上,单击“弱信号”以查看更多信息。他可以看到,77% 的用户和 88% 的接入点都存在信号问题。

Example: Root Cause Analysis for Coverage Issues


  • 时间线 - 问题发生的时间?

  • 分布 — 问题发生在网络中的哪个位置?

  • 受影响的项目 - 涉及哪些用户、设备和应用程序?

  • 位置 - 出现问题的平面图在哪里?

SLE 视频演示

此视频演示展示了如何针对 WAN 问题排除低 SLE 故障。

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.

从 Marvis 对话助手获取帮助

午饭后,奥斯卡遇到了一位同事罗伯塔,她提到那天早上她的微软团队电话很糟糕。奥斯卡决定使用聊天功能向马维斯寻求帮助。他点击了屏幕左下角的 Marvis 图标。

在弹出窗口中,他输入: 对团队进行故障排除

Marvis VNA Example: Entering Text

当 Marvis 提出指导性问题和 Oscar 回复时,Marvis 提供了最近 Teams 通话的列表。

Example: VNA Displays List of Calls


Marvis 显示有线网络上存在问题。从这里,奥斯卡可以点击查看更多信息。

Marvis 对话助手视频演示

在此视频演示中,Marvis 帮助解决 Microsoft Teams 的问题。

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.


您还可以使用 Marvis 查询语言输入结构化查询。有关更多信息,请参阅 Marvis 查询语言