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2. Site reliability engineering - Wikipedia

   en.wikipedia.org/wiki/Site_reliability_engineering

   Site Reliability Engineering (SRE) is a subset of web development that uses elements of software engineering, IT infrastructure, and operations [1] to assist with reliability. SRE is similar to DevOps as both aim to improve the reliability and availability of systems.

3. Service-level objective - Wikipedia

   en.wikipedia.org/wiki/Service-level_objective

   A service-level objective (SLO), as per the O'Reilly Site Reliability Engineering book, is a "target value or range of values for a service level that is measured by an SLI." [1] An SLO is a key element of a service-level agreement (SLA) between a service provider and a customer. SLOs are agreed upon as a means of measuring the performance of ...

4. Chaos engineering - Wikipedia

   en.wikipedia.org/wiki/Chaos_engineering

   Founded in 2019, Steadybit popularized pre-production chaos and reliability engineering. [26] Its open-source Reliability Hub extends Steadybit. [27] [28] Proofdock can inject infrastructure, platform, and application failures on Microsoft Azure DevOps. [26] Gremlin is a "failure-as-a-service" platform. [29]

5. Reliability Engineering and Risk Analysis - Wikipedia

   en.wikipedia.org/wiki/Reliability_Engineering...

   Reliability Engineering and Risk Analysis: A Practical Guide (ISBN 9781498745871) is a textbook on techniques for analysis of reliability and risk, written by Mohammad Modarres, Mark Kaminskiy, and Vasiliy Krivtsov.

6. Observability (software) - Wikipedia

   en.wikipedia.org/wiki/Observability_(software)

   Observability is foundational to site reliability engineering, as it is the first step in triaging a service outage. One of the goals of observability is to minimize the amount of prior knowledge needed to debug an issue.

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8. Physics of failure - Wikipedia

   en.wikipedia.org/wiki/Physics_of_failure

   A science-based approach to reliability that uses modeling and simulation to design-in reliability. It helps to understand system performance and reduce decision risk during design and after the equipment is fielded. This approach models the root causes of failure such as fatigue, fracture, wear, and corrosion.

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