Follow this scenario to see first-hand how Selector hones in on data center performance issues reported via Slack to give you immediate insights.
Video length: 2:06
Joe alerts Paul about performance issues [in Slack].
Joe issues a “site status” query to Selector AI to show Paul.
Selector AI responds to Joe’s query.
Paul scans the chart and sees a possible issue in the Chicago DC.
Paul queries Selector AI to get more info on the Chicago DC.
Selector AI acknowledges Paul’s request … and responds back to Paul.
With a contextual summary dashboard pertinent to Chicago.
Selector AI fires an alert for Chicago to the channel
Clicking on the link takes the user to the Selector AI portal.
Paul seamlessly transitions to the Selector AI portal to continue the workflow started in Slack.
Clicking on the alert takes Paul to a context-specific dashboard … which contains a curated set of metrics, powered by ML-based clustering and auto-correlation.
Drilling down into application latency for Chicago, … the measured latency exceeds the ML-generated autobaseline, [as noted by the on-screen labels] measured latency and ML-generated autobaseline. Suspicious!
Paul sees multiple config-change events in Chicago … followed by triggered alerts for Chicago.
Further scanning of the dashboard also reveals something anomalous with BGP status. Several devices have BGP statuses as red.
A few devices had config changes in the past hour. [Event details show] Commit Diff: Config lines deleted (left side) and Commit Diff: Config lines added (right side). [The left side shows] “bgp dampening” was deleted. The config change needs to be reverted.
Paul reverts the errant config change … and lets Joe know that the issue is resolved.
Joe queries Selector AI again to double-check the site status.
All sites [are] green!
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