Continuous Monitoring

This practice involves continuous monitoring of both the code in operation and the underlying infrastructure that supports it. A feedback loop that reports on bugs or issues then makes its way back to development. The purpose of continuous monitoring is to ensure that the Solution is observable in production before it is released to end-users.

Practices: There are certain practices in DevOps to make sure we have monitoring capabilities. We need to know what is happening in our production environment.

Full-stack telemetry

To understand how the infrastructure and application are functioning, we require monitoring at all levels of the stack. We collect data on the different levels of the stack so that we may test our hypothesis. Make sure production monitoring supports the collection and analysis of full-stack system performance as well as business-level data. Both represent critical feedback that will be used to evaluate the hypothesis.

Visual displays

We want to make sure that all interested parties can see, access, and consume information. Dashboards transform raw data into easily digestible insights that can help you make better decisions faster.

For this, we use big visible information radiators (BVIR) to display the health of Solutions in prominent locations. Also, Visualize key DevOps Metrics like Time since the last outage, Page response times, Usage analytics, Resource/compute utilization

Federated monitoring

Federated monitoring enables you to mine data and display monitoring and telemetry data from different systems. To see the aggregated data in one location, we require a single collecting point. For this, we use BVIRs to display the aggregated data that provide accessibility and ways to drill down to individual application and infrastructure telemetry

Artificial Intelligence for IT Operations (AIOps)

AIOps has emerged as a solution to the complexity of managing a “monitor everything” strategy. This amount of monitoring produces mountains of data. Analyzing all of this data manually can become a significant bottleneck. AIOps provides infrastructure and capabilities that:

-Aggregate, correlate and analyze events

-Separate meaningful events from the noise

-Identify and predict root causes of issues

-Significantly reduce mean time to restore (MTTR)

Instead of limiting the quantity of monitoring, teams may use AIOps to process this data much more quickly.

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