December 2025 — OpenNebula/website: Delivered two key features focusing on CI automation and documentation to support AI-ready Kubernetes deployments. These changes improve site reliability, enable faster PR feedback loops, and provide clearer onboarding for AI deployment workflows. No major bugs reported this period; efforts prioritized delivery and documentation improvements, with strong governance and traceability.

November 2025 monthly engineering summary for OpenNebula: Key features delivered: - Appliance platform upgrade (VLLM) and CAPI dependency updates: Introduced a new VLLM appliance, deprecated Ray and Dynamo appliances, and updated CAPI appliance dependencies to improve compatibility and functionality. Notable commits: e46d9bc3c489477f8efa9462dbcd7c28021f9161 (Adds a new vllm appliance) and 2e1cf8faba0980192e9c5e3d19e18fbd8d68d959 (Updates CAPI appliance dependencies versions). - Alpine Linux version deprecation: Deprecation of Alpine 318 and 319 due to end-of-life, removing outdated dependencies to keep the codebase secure and up-to-date. Commit: f611e52d8aeb1094e563594bf51214029e8b3c28. - LLM deployment and benchmarking documentation overhaul (OpenNebula/website): Consolidated and enhanced LLM deployment docs, adding a vLLM wiki link to the LLM inference certification, replacing Ray appliance references with the vLLM appliance, and expanding benchmarking guidance with methodology, environments, and metrics. Commits: f9abbe0eb75c32b748d8d9e0de49ca13e0a49137, 7e11fac3e8cbee87c7befe30912b4a7606b2bc7e, 8bb7fd72d37e6edba0da355dfb3bc520eeca6892. - AI workloads on Kubernetes documentation clarity and scheduler details: Clarified Kubernetes-related docs for AI workloads, including separation of AI-Ready Kubernetes and NVIDIA Dynamo docs, and added NVIDIA KAI Scheduler installation and validation guidance. Commits: 890fc1fc0651e3b19b2882e1bd16c268883dc910, cc9eec2110099a1b5f8f5ba3f14f72af39ee4394. Major bugs fixed: - NVIDIA Dynamo documentation fixes (broken links): Fixed broken links in NVIDIA Dynamo documentation to ensure reliable access to resources and instructions for deploying inference graphs. Commit: bbbce8d127314eb902780d943b80e203900f9ee2. Overall impact and accomplishments: - Increased platform stability and security by deprecating EOL Alpine versions and updating CAPI dependencies, reducing maintenance risk and aligning with current security baselines. - Accelerated AI deployment readiness and developer onboarding through improved LLM/DL documentation, clearer Kubernetes guidance, and updated benchmarking methodology. - Strengthened cross-repo collaboration and traceability with concise, committed changes that align with OpenNebula’s appliance strategy and AI tooling roadmap. Technologies and skills demonstrated: - Container/ appliance management (VLLM, CAPI) and dependency versioning - Secure lifecycle management (Alpine EOL deprecation) - LLM tooling and deployment documentation practices - AI workloads on Kubernetes, NVIDIA KAI Scheduler, and benchmarking methodology - Documentation engineering, cross-repo coordination, and changelog discipline

October 2025 (OpenNebula/one-apps) monthly summary focusing on business value and technical outcomes. Key activities included prototyping and introducing local Packer binary management to improve build determinism, followed by a rollback to revert the change and restore the prior build process. This period demonstrated disciplined change management, maintainability, and alignment with build standards. Repos involved: OpenNebula/one-apps.

September 2025 monthly summary for OpenNebula/website focused on stabilizing monitoring and IPv6 support. Delivered a critical bug fix for Prometheus patch_datasources IPv6 resolution (Issue #7107), with commit a65cf5051c236b672c9785c4f18dd112cab45f45. Validation confirmed proper IPv6 resolution and improved metrics reliability for IPv6 configurations, reducing monitoring-related troubleshooting. This work strengthens patch_datasources handling and lays groundwork for future reliability improvements in observability tooling.

July 2025 (2025-07) monthly summary for OpenNebula/one-apps focusing on ARM64 platform support enhancements. Delivered ARM64 readiness through a new Ubuntu 22.04 ARM64 image (ubuntu2204oneke) and aarch64-specific builds/variables for the CAPI appliance, expanding hardware compatibility and enabling ARM-based deployments. No major bugs fixed were documented this month; primary value delivered comes from feature delivery, improved portability, and groundwork for future ARM64 optimizations.

June 2025 (OpenNebula/one-apps) focused on stabilizing image builds and preventing CI failures through targeted disk-space tuning in the Ubuntu 22.04 image workflow. The work delivered improves reliability and accelerates release readiness for base images.

May 2025 — OpenNebula/one-apps delivered cross-architecture Ray appliance enhancements, introduced a Dynamo web interface, and stabilized networking and model-loading workflows to improve deployment reliability across heterogeneous environments. Key features include Ray appliance enhancements with NVIDIA driver support and aarch64 recipes, and deployment/configuration improvements enabled by a Dynamo web interface. Bug fixes addressed Ubuntu 24.04 netplan conflicts and Ray model loading when no auth token is provided. Impact: smoother multi-arch deployments, faster onboarding, and a web UI for Dynamo workflows. Technologies/skills demonstrated: cross-architecture development, configuration-driven deployment, web UI integration, auth/token handling, and network/config management.

April 2025 performance summary for OpenNebula/one-apps focused on stabilizing OneGate communications, modernizing VM lifecycle operations, and enabling AI inference workflows. Delivered features that improve reliability, readiness, and extensibility of the one-apps stack, delivering visible business value through operational stability and future-ready capabilities.

March 2025 - OpenNebula/one-apps: Key deployment and networking enhancements. Delivered: (1) Increase OneKE Ubuntu image disk to 3G and disable skip_resize_disk for reliable deployments (commit 4906e747b372faca7baae31714c148465364a5fb). (2) Update OneKE Ubuntu ISO references to jammy, ensuring Packer builds use current images (commit 525c32709182b821b91d1288289f61594afd7d1a). (3) Add cluster and service CIDR configuration for Kubernetes in OneKE and wire to RKE2 initialization for master and worker nodes (commit d16e96a26e881ed6e09b368787d9a5cd5a759ef5).

January 2025: OpenNebula/one-apps delivered stability-focused updates for OneKE deployments, including a dedicated Ubuntu 22.04 image variant to address kernel-related stability issues, and expanded storage with a 10GB qcow2 target. Commit 23023972daac3c49a3ea9115235eaa8c67ffbeff pins the image to a stable Ubuntu 22.04 version to avoid kernel bugs. Updated Makefile and Packer templates to ensure reproducible builds and consistent deployment configurations across environments. This work reduces incident risk, improves deployment reliability, and accelerates onboarding for OneKE deployments. Tech stack demonstrated: Linux kernel readiness tuning, image build automation (Packer), and deployment pipelines (Makefile orchestration).

December 2024 monthly summary for OpenNebula/one-apps focused on feature delivery and platform modernization to support scalable AI deployments and improved network management. The month delivered three major capabilities with associated deployment-automation enhancements, addressing API deprecations and platform upgrades to maintain long-term reliability and security.

Nov 2024 performance summary for OpenNebula/one-apps: Implemented cloud-init provisioning improvements by enabling USER_DATA parsing and execution on the updated Amazon Linux 2 image, including support for cloud-config directives via write_files and runcmd. Updated the base image and Ruby to version 3.0 to support cloud-init context parsing, with Ruby modules for processing. Fixed cloud-init parsing gaps to read cloudconfig data from USER_DATA, enabling automated provisioning and startup/shutdown scripting. This work underpins faster, more reliable deployments and consistent environments across tenants, reducing manual intervention and operational risk.