DevOps & Linux
Delivery pipelines, infrastructure, operational reliability and Linux systems research.
Research-design drafts identify proposed methods and limitations. They do not claim completed experiments.
Does GitOps Reduce Configuration Drift?
GitOps can reconcile declared and actual state, but reconciliation alone does not prove that the desired state is correct. A useful evaluation separates unintended drift, approved exceptions and a controller restoring an obsolete configuration.
Kubernetes Autoscaling Under Bursty AI Workloads
CPU utilisation is not automatically a good proxy for queued inference work. The research question is which scaling signal keeps a defined service responsive without paying for unnecessary capacity.
Database Migration Rollback: When the Previous Release Cannot Return
An application rollback is not a safe recovery plan when the previous release cannot read the new database state. Compatibility is a relationship between versions, not a property of one deployment script.
Platform Engineering: Faster Delivery or Another Bottleneck?
A golden path is useful when it removes recurring work without making unusual but legitimate needs impossible. Adoption counts alone cannot show whether a platform improves delivery.
Observability Cost Versus Diagnostic Value
More telemetry is not the same as more useful evidence. The practical question is which retained signals help explain a known failure, and what each signal costs to collect and query.
Linux Hardening Versus Operational Friction
A hardening control needs an operating context. Restricting a service may reduce an exposure while also disrupting maintenance, backups or debugging; those consequences should be measured rather than treated as surprises.
When Kubernetes Is the Wrong Architecture
The smallest workable architecture can be the better decision for a bounded product. Kubernetes should earn its operational cost through requirements such as repeatable scheduling, isolation or scaling, not through familiarity with a fashionable tool.
CI Pipeline Speed Versus Defect Detection
A fast pipeline is valuable only if it retains the checks needed for the release decision. Test selection should be evaluated against missed defects, not just minutes saved.
Chaos Engineering for Small Teams
A small team can learn from controlled failure without creating a large chaos programme. The useful starting point is one service assumption and a recovery procedure that can be exercised safely.
What DORA Metrics Miss About Delivery
Delivery speed and stability describe important parts of software work, but they do not establish that the team delivered a useful product. Pair delivery measures with outcomes rather than replacing one with the other.