iter8 enables statistically robust continuous experimentation of microservices in your CI/CD pipelines.

For in-depth information about how to use iter8, visit iter8.tools.

• Introduction
• Repositories

Use an iter8 experiment to safely expose competing versions of a service to application traffic, gather in-depth insights about key performance and business metrics for your microservice versions, and intelligently rollout the best version of your service.

Iter8’s expressive model of cloud experimentation supports a variety of CI/CD scenarios. Using an iter8 experiment, you can:

1. Run a performance test with a single version of a microservice.
2. Perform a canary release with two versions, a baseline and a candidate. Iter8 will shift application traffic safely and gradually to the candidate, if it meets the criteria you specify in the experiment.
3. Perform an A/B test with two versions – a baseline and a candidate. Iter8 will identify and shift application traffic safely and gradually to the winner, where the winning version is defined by the criteria you specify in the experiment.
4. Perform an A/B/n test with multiple versions – a baseline and multiple candidates. Iter8 will identify and shift application traffic safely and gradually to the winner.

Under the hood, iter8 uses advanced Bayesian learning techniques coupled with multi-armed bandit approaches to compute a variety of statistical assessments for your microservice versions, and uses them to make robust traffic control and rollout decisions.

The components of iter8 are divided across a few github repositories.

• iter8 This repository containing the kubernetes controller that orchestrates iter8's experiments.
• iter8-analytics The repository containing the iter8-analytics component.
• iter8-trend The repository contains the iter8-trend component.

In addition,

• iter8's extensions to Kiali is contained in kiali, kiali-ui, and k-charted.
• iter8's extensions to Kui is contained in kui.