Pipeline Efficiency: Observability and AI Optimization
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## Overview
Pipeline efficiency is an ongoing topic. There are many ways to analyse, refactor, and optimize pipelines to reduce cost, and also leverage AI already to do forecast planning.
### Proposal
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This proposal involves multiple ideas and MVCs
1. Collect CI/CD Observability data (storage, CPU, etc.) as base metrics, job logs, add tracing for CI/CD Observability (https://gitlab.com/gitlab-org/gitlab/-/issues/338943)
- Data from Runner fleet management gitlab~11448658 and ~"group::observability"
1. Train AI models that help optimize pipelines with a focus on cost optimization
- Fixing pipelines fast is covered in this proposal: https://gitlab.com/gitlab-org/gitlab/-/issues/386863+
## Resources
1. @dnsmichi talk "Observability for Efficient DevSecOps Pipelines" https://go.gitlab.com/VDAvMw
- Cloudland feedback https://gitlab.com/gitlab-com/marketing/corporate_marketing/corporate-marketing/-/issues/7506#note_1443211586
1. TAMland https://go.gitlab.com/etwJmr
- PromCon EU 2022: How GitLab Uses Long-Term Monitoring Data For Capacity Forecasting https://go.gitlab.com/yO6NCN
1. Coroot Cost monitoring https://github.com/coroot/coroot/releases/tag/0.15.0
1. OpenCost https://www.opencost.io/
## Suggested Tier
Paid feature, which helps with reducing costs and more efficient pipelines.
gitlab~3207279
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epic