News

Fedora Project Releases Update To Linux Distribution

The Fedora Project, a community open source effort sponsored by Red Hat, last week released the latest version of its namesake Linux distribution. Fedora 11, code-named "Leonidas," includes upgraded virtualization capabilities, improved graphics and sound card compatibility, and support for additional file systems, notably ext4.

While Fedora is not typically used for widespread enterprise rollouts, it often portends features or capabilities under development for Red Hat Enterprise Linux. "It represents the bleeding edge of Linux," said 451Group analyst Jay Lyman. "This doesn’t really compete with the enterprise Linux distributions, but it does hold their feet to the fire."

The improved virtualization features include a new console and an upgraded virtual machine guest creation wizard. Guest machines can run more securely via support for SELinux, the component of the Fedora Linux kernel that implements mandatory access control and role-based access control, according to the Fedora Project.

Also improved in Fedora 11 is the kernel mode setting, that supports more video cards from ATI, Intel, and NVIDIA. Perhaps the most obvious improvement will see as a result of the upgraded kernel mode setting feature is accelerated boot-up time, which the organizers of the volunteer-based Fedora Project said is down to 20 seconds.

According to the Fedora Project, the new release loads fonts and other content faster via the improved PackageKit support that debuted with Fedora 9. The new Fedora 11 supports more finger print readers and offers new inputs for international language content. The added file system support also supports higher device size and file size limits, according to the Fedora Project. Fedora 11 also comes with the Minimalist GNU (MinGW) cross-compiler tool for building Windows executables. A complete list of features is accessible here .

Along with the release of Fedora 11, the Fedora Project has released the beta of a new developer portal aimed at providing an improved interface for community members to contribute code and share information. The new customizable dashboard tracks contribution and discussions and is available for community comment.

About the Author

Jeffrey Schwartz is editor of Redmond magazine and also covers cloud computing for Virtualization Review's Cloud Report. In addition, he writes the Channeling the Cloud column for Redmond Channel Partner. Follow him on Twitter @JeffreySchwartz.

comments powered by Disqus

Featured

  • VS Code v1.99 Is All About Copilot Chat AI, Including Agent Mode

    Agent Mode provides an autonomous editing experience where Copilot plans and executes tasks to fulfill requests. It determines relevant files, applies code changes, suggests terminal commands, and iterates to resolve issues, all while keeping users in control to review and confirm actions.

  • Windows Community Toolkit v8.2 Adds Native AOT Support

    Microsoft shipped Windows Community Toolkit v8.2, an incremental update to the open-source collection of helper functions and other resources designed to simplify the development of Windows applications. The main new feature is support for native ahead-of-time (AOT) compilation.

  • New 'Visual Studio Hub' 1-Stop-Shop for GitHub Copilot Resources, More

    Unsurprisingly, GitHub Copilot resources are front-and-center in Microsoft's new Visual Studio Hub, a one-stop-shop for all things concerning your favorite IDE.

  • Mastering Blazor Authentication and Authorization

    At the Visual Studio Live! @ Microsoft HQ developer conference set for August, Rockford Lhotka will explain the ins and outs of authentication across Blazor Server, WebAssembly, and .NET MAUI Hybrid apps, and show how to use identity and claims to customize application behavior through fine-grained authorization.

  • Linear Support Vector Regression from Scratch Using C# with Evolutionary Training

    Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric value. A linear SVR model uses an unusual error/loss function and cannot be trained using standard simple techniques, and so evolutionary optimization training is used.

Subscribe on YouTube