Ubuntu Pro FIPS 22.04 LTS on Azure: Secure, compliant, and optimized for regulated industries
Organizations across government (including local and federal agencies and their contractors), finance, healthcare, and other regulated industries running workloads on Microsoft Azure now have a streamlined path to meet rigorous FIPS 140-3 compliance requirements. Canonical is pleased to announce the availability of Ubuntu Pro FIPS 22.04 LTS on the Azure Marketplace, featuring newly certified cryptographic modules. This offering extends the stability and comprehensive security features of Ubuntu Pro, tailored for state agencies, federal contractors, and industries requiring a FIPS-validated foundation on Azure. It provides the enterprise-grade Ubuntu experience, optimized for performance on Azure in collaboration with Microsoft, and enhanced with critical compliance capabilities. For instance, if you are building a Software as a Service (SaaS) application on Azure that requires FedRAMP authorization, utilizing Ubuntu Pro FIPS 22.04 LTS can help you meet specific controls like SC-13 (Cryptographic Protection), as FIPS 140-3 validated modules are a foundational requirement. This significantly streamlines your path to achieving FedRAMP compliance. What is FIPS 140-3 and why does it matter? FIPS 140-3 is the latest iteration of the benchmark U.S. government standard for validating cryptographic module implementations, superseding FIPS 140-2. Managed by NIST, it's essential for federal agencies and contractors and is a recognized best practice in many regulated industries like finance and healthcare. Using FIPS-validated components helps ensure cryptography is implemented correctly, protecting sensitive data in transit and at rest. Ubuntu Pro FIPS 22.04 LTS includes FIPS 140-3 certified versions of the Linux kernel and key cryptographic libraries (like OpenSSL, Libgcrypt, GnuTLS) pre-enabled, which are drop-in replacements for the standard packages, greatly simplifying deployment for compliance needs. The importance of security updates (fips-updates) A FIPS certificate applies to a specific module version at its validation time. Over time, new vulnerabilities (CVEs) are discovered in these certified modules. Running code with known vulnerabilities poses a significant security risk. This creates a tension between strict certification adherence and maintaining real-world security. Recognizing this, Canonical provides security fixes for the FIPS modules via the fips-updates stream, available through Ubuntu Pro. We ensure these security patches do not alter the validated cryptographic functions. This approach aligns with modern security thinking, including recent FedRAMP guidance, which acknowledges the greater risk posed by unpatched vulnerabilities compared to solely relying on the original certified binaries. Canonical strongly recommends all users enable the fips-updates repository to ensure their systems are both compliant and secure against the latest threats. FIPS 140-3 vs 140-2 The new FIPS 140-3 standard includes modern ciphers such as TLS v1.3, as well as deprecating older algorithms like MD5. If you are upgrading systems and workloads to FIPS 140-3, it will be necessary to perform rigorous testing to ensure that applications continue to work correctly. Compliance tooling Included Ubuntu Pro FIPS also includes access to Canonical's Ubuntu Security Guide (USG) tooling, which assists with automated hardening and compliance checks against benchmarks like CIS and DISA-STIG, a key requirement for FedRAMP deployments. How to get Ubuntu Pro FIPS on Azure You can leverage Ubuntu Pro FIPS 22.04 LTS on Azure in two main ways: Deploy the Marketplace Image: Launch a new VM directly from the dedicated Ubuntu Pro FIPS 22.04 LTS listing on the Azure Marketplace. This image comes with the FIPS modules pre-enabled for immediate use. Enable on an Existing Ubuntu Pro VM: If you already have an Ubuntu Pro 22.04 LTS VM running on Azure, you can enable the FIPS modules using the Ubuntu Pro Client (pro enable fips-updates). Upgrading standard Ubuntu: If you have a standard Ubuntu 22.04 LTS VM on Azure, you first need to attach Ubuntu Pro to it. This is a straightforward process detailed in the Azure documentation for getting Ubuntu Pro. Once Pro is attached, you can enable FIPS as described above. Learn More Ubuntu Pro FIPS provides a robust, maintained, and compliant foundation for your sensitive workloads on Azure. Watch Joel Sisko from Microsoft speak with Ubuntu experts in this webinar Explore all features of Ubuntu Pro on Azure Read details on the FIPS 140-3 certification for Ubuntu 22.04 LTS Official NIST certification linkAzure Image Testing for Linux (AITL)
As cloud and AI evolve at an unprecedented pace, the need to deliver high-quality, secure, and reliable Linux VM images has never been more essential. Azure Image Testing for Linux (AITL) is a self-service validation tool designed to help developers, ISVs, and Linux distribution partners ensure their images meet Azure’s standards before deployment. With AITL, partners can streamline testing, reduce engineering overhead, and ensure compliance with Azure’s best practices, all in a scalable and automated manner. Let’s explore how AITL is redefining image validation and why it’s proving to be a valuable asset for both developers and enterprises. Before AITL, image validation was largely a manual and repetitive process, engineers were often required to perform frequent checks, resulting in several key challenges: Time-Consuming: Manual validation processes delayed image releases. Inconsistent Validation: Each distro had different methods for testing, leading to varying quality levels. Limited Scalability: Resource constraints restricted the ability to validate a broad set of images. AITL addresses these challenges by enabling partners to seamlessly integrate image validation into their existing pipelines through APIs. By executing tests within their own Azure subscriptions prior to publishing, partners can ensure that only fully validated, high-quality Linux images are promoted to production in the Azure environment. How AITL Works? AITL is powered by LISA, which is a test framework and a comprehensive opensource tool contains 400+ test cases. AITL provides a simple, yet powerful workflow run LISA test cases: Registration: Partners register their images in AITL’s validation framework. Automated Testing: AITL runs a suite of predefined validation tests using LISA. Detailed Reporting: Developers receive comprehensive results highlighting compliance, performance, and security areas. All test logs are available to access. Self-Service Fixes: Any detected issues can be addressed by the partner before submission, eliminating delays and back-and-forth communication. Final Sign-Off: Once tests pass, partners can confidently publish their images, knowing they meet Azure’s quality standards. Benefits of AITL AITL is a transformative tool that delivers significant benefits across the Linux and cloud ecosystem: Self-Service Capability: Enables developers and ISVs to independently validate their images without requiring direct support from Microsoft. Scalable by Design: Supports concurrent testing of multiple images, driving greater operational efficiency. Consistent and Standardized Testing: Offers a unified validation framework to ensure quality and consistency across all endorsed Linux distributions. Proactive Issue Detection: Identifies potential issues early in the development cycle, helping prevent costly post-deployment fixes. Seamless Pipeline Integration: Easily integrates with existing CI/CD workflows to enable fully automated image validation. Use Cases for AITL AITL designed to support a diverse set of users across the Linux ecosystem: Linux Distribution Partners: Organizations such as Canonical, Red Hat, and SUSE can validate their images prior to publishing on the Azure Marketplace, ensuring they meet Azure’s quality and compliance standards. Independent Software Vendors (ISVs): Companies providing custom Linux Images can verify that their custom Linux-based solutions are optimized for performance and reliability on Azure. Enterprise IT Teams: Businesses managing their own Linux images on Azure can use AITL to validate updates proactively, reducing risk and ensuring smooth production deployments. Current Status and Future Roadmap AITL is currently in private preview, with five major Linux distros and select ISVs actively integrating it into their validation workflows. Microsoft plans to expand AITL’s capabilities by adding: Support for Private Test Cases: Allowing partners to run custom tests within AITL securely. Kernel CI Integration: Enhancing low-level kernel validation for more robust testing and results for community. DPDK and Specialized Validation: Ensuring network and hardware performance for specialized SKU (CVM, HPC) and workloads How to Get Started? For developers and partners interested in AITL, following the steps to onboard. Register for Private Preview AITL is currently hidden behind a preview feature flag. You must first register the AITL preview feature with your subscription so that you can then access the AITL Resource Provider (RP). These are one-time steps done for each subscription. Run the “az feature register” command to register the feature: az feature register --namespace Microsoft.AzureImageTestingForLinux --name JobandJobTemplateCrud Sign Up for Private Preview – Contact Microsoft’s Linux Systems Group to request access. Private Preview Sign Up To confirm that your subscription is registered, run the above command and check that properties.state = “Registered” Register the Resource Provider Once the feature registration has been approved, the AITL Resource Provider can be registered by running the “az provider register” command: az provider register --namespace Microsoft.AzureImageTestingForLinux *If your subscription is not registered to Microsoft.Compute/Network/Storage, please do so. These are also prerequisites to using the service. This can be done for each namespace (Microsoft.Compute, Microsoft.Network, Microsoft.Storage) through this command: az provider register --namespace Microsoft.Compute Setup Permissions The AITL RP requires a permission set to create test resources, such as the VM and storage account. The permissions are provided through a custom role that is assigned to the AITL Service Principal named AzureImageTestingForLinux. We provide a script setup_aitl.py to make it simple. It will create a role and grant to the service principal. Make sure the active subscription is expected and download the script to run in a python environment. https://raw.githubusercontent.com/microsoft/lisa/main/microsoft/utils/setup_aitl.py You can run the below command: python setup_aitl.py -s "/subscriptions/xxxx" Before running this script, you should check if you have the permission to create role definition in your subscription. *Note, it may take up to 20 minutes for the permission to be propagated. Assign an AITL jobs access role If you want to use a service principle or registration application to call AITL APIs. The service principle or App should be assigned a role to access AITL jobs. This role should include the following permissions: az role definition create --role-definition '{ "Name": "AITL Jobs Access Role", "Description": "Delegation role is to read and write AITL jobs and job templates", "Actions": [ "Microsoft.AzureImageTestingForLinux/jobTemplates/read", "Microsoft.AzureImageTestingForLinux/jobTemplates/write", "Microsoft.AzureImageTestingForLinux/jobTemplates/delete", "Microsoft.AzureImageTestingForLinux/jobs/read", "Microsoft.AzureImageTestingForLinux/jobs/write", "Microsoft.AzureImageTestingForLinux/jobs/delete", "Microsoft.AzureImageTestingForLinux/operations/read", "Microsoft.Resources/subscriptions/read", "Microsoft.Resources/subscriptions/operationresults/read", "Microsoft.Resources/subscriptions/resourcegroups/write", "Microsoft.Resources/subscriptions/resourcegroups/read", "Microsoft.Resources/subscriptions/resourcegroups/delete" ], "IsCustom": true, "AssignableScopes": [ "/subscriptions/01d22e3d-ec1d-41a4-930a-f40cd90eaeb2" ] }' You can create a custom role using the above command in the cloud shell, and assign this role to the service principle or the App. All set! Please go through a quick start to try AITL APIs. Download AITL wrapper AITL is served by Azure management API. You can use any REST API tool to access it. We provide a Python wrapper for better experience. The AITL wrapper is composed of a python script and input files. It calls “az login” and “az rest” to provide similar experience like the az CLI. The input files are used for creating test jobs. Make sure az CLI and python 3 are installed. Clone LISA code, or only download files in the folder. lisa/microsoft/utils/aitl at main · microsoft/lisa (github.com). Use the command below to check the help text. python -m aitl job –-help python -m aitl job create --help Create a job Job creation consists of two entities: A job template and an image. The quickest way to get started with the AITL service is to create a Job instance with your job template properties in the request body. Replace placeholders with the real subscription id, resource group, job name to start a test job. This example runs 1 test case with a marketplace image using the tier0.json template. You can create a new json file to customize the test job. The name is optional. If it’s not provided, AITL wrapper will generate one. python -m aitl job create -s {subscription_id} -r {resource_group} -n {job_name} -b ‘@./tier0.json’ The default request body is: { "location": "westus3", "properties": { "jobTemplateInstance": { "selections": [ { "casePriority": [ 0 ] } ] } } } This example runs the P0 test cases with the default image. You can choose to add fields to the request, such as image to test. All possible fields are described in the API Specification – Jobs section. The “location” property is a required field that represents the location where the test job should be created, it doesn’t affect the location of VMs. AITL supports “westus”, “westus2”, or “westus3”. The image object in the request body json is where the image type to be used for testing is detailed, as well as the CPU architecture and VHD Generation. If the image object is not included, LISA will pick a Linux marketplace image that meets the requirements for running the specified tests. When an image type is specified, additional information will be required based on the image type. Supported image types are VHD, Azure Marketplace image, and Shared Image Gallery. - VHD requires the SAS URL. - Marketplace image requires the publisher, offer, SKU, and version. - Shared Image Gallery requires the gallery name, image definition, and version. Example of how to include the image object for shared image gallery. (<> denotes placeholder): { "location": "westus3", “properties: { <...other properties from default request body here>, "image": { "type": "shared_gallery", "architecture": "x64", "vhdGeneration": 2, "gallery": "<Example: myAzureComputeGallery>", "definition": "<Example: myImage1>", "version": "<Example: 1.0.1>" } } } Check Job Status & Test Results A job is an asynchronous operation that is updated throughout the job’s lifecycle with its operation and ongoing tests status. A job has 6 provisioning states – 4 are non-terminal states and 2 are terminal states. Non-terminal states represent ongoing operation stages and terminal states represent the status at completion. The job’s current state is reflected in the `properties.provisioningState` property located in the response body. The states are described below: Operation States State Type Description Accepted Non-Terminal state Initial ARM state describing the resource creation is being initialized. Queued Non-Terminal state The job has been queued by AITL to run LISA using the provided job template parameters. Scheduling Non-Terminal state The job has been taken off the queue and AITL is preparing to launch LISA. Provisioning Non-Terminal state LISA is creating your VM within your subscription using the default or provided image. Running Non-Terminal state LISA is running the specified tests on your image and VM configuration. Succeeded Terminal state LISA completed the job run and has uploaded the final test results to the job. There may be failed test cases. Failed Terminal state There was a failure during the job’s execution. Test results may be present and reflect the latest status for each listed test. Test results are updated in near real-time and can be seen in the ‘properties.results’ property in the response body. Results will begin to get updated during the “Running” state and the final set of result updates will happen prior to reaching a terminal state (“Completed” or “Failed”). For a complete list of possible test result properties, go to the API Specification – Test Results section. Run below command to get detailed test results. python -m aitl job get -s {subscription_id} -r {resource_group} -n {job_name} The query argument can format or filter results by JMESquery. Please refer to help text for more information. For example, List test results and error messages. python -m aitl job get -s {subscription_id} -r {resource_group} -n {job_name} -o table -q 'properties.results[].{name:testName,status:status,message:message}' Summarize test results. python -m aitl job get -s {subscription_id} -r {resource_group} -n {job_name} -q 'properties.results[].status|{TOTAL:length(@),PASSED:length([?@==`"PASSED"`]),FAILED:length([?@==`"FAILED"`]),SKIPPED:length([?@==`"SKIPPED"`]),ATTEMPTED:length([?@==`"ATTEMPTED"`]),RUNNING:length([?@==`"RUNNING"`]),ASSIGNED:length([?@==`"ASSIGNED"`]),QUEUED:length([?@==`"QUEUED"`])}' Access Job Logs To access logs and read from Azure Storage, the AITL user must have “Storage Blob Data Owner” role. You should check if you have the permission to create role definition in your subscription, likely with your administrator. For information on this role and instructions on how to add this permission, see this Azure documentation. To access job logs, send a GET request with the job name and use the logUrl in the response body to retrieve the logs, which are stored in Azure storage container. For more details on interpreting logs, refer to the LISA documentation on troubleshooting test failures. To quickly view logs online (note that file size limitations may apply), select a .log Blob file and click "edit" in the top toolbar of the Blob menu. To download the log, click the download button in the toolbar. Conclusion AITL represents a forward-looking approach to Linux image validation bringing automation, scalability, and consistency to the forefront. By shifting validation earlier in the development cycle, AITL helps reduce risk, accelerate time to market, and ensure a reliable, high-quality Linux experience on Azure. Whether you're a developer, a Linux distribution partner, or an enterprise managing Linux workloads on Azure, AITL offers a powerful way to modernize and streamline your validation workflows. To learn more or get started with AITL or more details and access to AITL, reach out to Microsoft Linux Systems GroupCanonical Ubuntu 20.04 LTS Reaching End of Standard Support
We’re announcing the upcoming end of standard support for Ubuntu 20.04 LTS (Focal Fossa) on 31 May 2025, as we focus on delivering a more secure and optimized Linux experience. Originally released in April 2020, Ubuntu 20.04 LTS introduced key enhancements like improved UEFI Secure Boot and broader Kernel Livepatch coverage, strengthening security on Azure. You can continue using your existing virtual machines, but after this date, security, features, and maintenance updates will no longer be provided by Canonical, which may impact system security and reliability. Recommended action: It’s important to act before 31 May 2025 to ensure you’re on a supported operating system. Microsoft recommends either migrating to the next Ubuntu LTS release or upgrading to Ubuntu Pro to gain access to expanded security and maintenance from Canonical. Upgrading to Ubuntu 22.04 LTS or Ubuntu 24.04 LTS Transitioning to the latest operating system, such as Ubuntu 24.04 LTS, is important for performance, hardware enablement, new technology benefits, and is recommended for new instances. It may be a complex process for existing deployments and should be properly scoped and tested with your workloads. While there’s no direct upgrade path from Ubuntu 20.04 LTS to Ubuntu 24.04 LTS, you can directly upgrade to Ubuntu 22.04 LTS, and then to Ubuntu 24.04 LTS, or directly install Ubuntu 24.04 LTS. See the Ubuntu Server upgrade guide for more information. Ubuntu Pro – Expanded Security Maintenance to 2030 Ubuntu Pro includes security patching for all Ubuntu packages due to Expanded Security Maintenance (ESM) for Infrastructure and Applications and optional 24/7 phone and ticket support. Ubuntu Pro 20.04 LTS will remain fully supported until May 2030. New virtual machines can be deployed with Ubuntu Pro from the Azure Marketplace. You can also upgrade existing virtual machines to Ubuntu Pro by in-place upgrades via Azure CLI. More Information More information covering Ubuntu 20.04 LTS End of Standard Support can be found here. Refer to the documentation to learn more about handling Ubuntu 20.04 LTS on Azure. You can also check out Canonical’s blog post and watch the webinar here.Automating the Linux Quality Assurance with LISA on Azure
Introduction Building on the insights from our previous blog regarding how MSFT ensures the quality of Linux images, this article aims to elaborate on the open-source tools that are instrumental in securing exceptional performance, reliability, and overall excellence of virtual machines on Azure. While numerous testing tools are available for validating Linux kernels, guest OS images and user space packages across various cloud platforms, finding a comprehensive testing framework that addresses the entire platform stack remains a significant challenge. A robust framework is essential, one that seamlessly integrates with Azure's environment while providing the coverage for major testing tools, such as LTP and kselftest and covers critical areas like networking, storage and specialized workloads, including Confidential VMs, HPC, and GPU scenarios. This unified testing framework is invaluable for developers, Linux distribution providers, and customers who build custom kernels and images. This is where LISA (Linux Integration Services Automation) comes into play. LISA is an open-source tool specifically designed to automate and enhance the testing and validation processes for Linux kernels and guest OS images on Azure. In this blog, we will provide the history of LISA, its key advantages, the wide range of test cases it supports, and why it is an indispensable resource for the open-source community. Moreover, LISA is available under the MIT License, making it free to use, modify, and contribute. History of LISA LISA was initially developed as an internal tool by Microsoft to streamline the testing process of Linux images and kernel validations on Azure. Recognizing the value it could bring to the broader community, Microsoft open-sourced LISA, inviting developers and organizations worldwide to leverage and enhance its capabilities. This move aligned with Microsoft's growing commitment to open-source collaboration, fostering innovation and shared growth within the industry. LISA serves as a robust solution to validate and certify that Linux images meet the stringent requirements of modern cloud environments. By integrating LISA into the development and deployment pipeline, teams can: Enhance Quality Assurance: Catch and resolve issues early in the development cycle. Reduce Time to Market: Accelerate deployment by automating repetitive testing tasks. Build Trust with Users: Deliver stable and secure applications, bolstering user confidence. Collaborate and Innovate: Leverage community-driven improvements and share insights. Benefits of Using LISA Scalability: Designed to run large-scale test cases, from 1 test case to 10k test cases in one command. Multiple platform orchestration: LISA is created with modular design, to support run the same test cases on various platforms including Microsoft Azure, Windows HyperV, BareMetal, and other cloud-based platforms. Customization: Users can customize test cases, workflow, and other components to fit specific needs, allowing for targeted testing strategies. It’s like building kernels on-the-fly, sending results to custom database, etc. Community Collaboration: Being open source under the MIT License, LISA encourages community contributions, fostering continuous improvement and shared expertise. Extensive Test Coverage: It offers a rich suite of test cases covering various aspects of compatibility of Azure and Linux VMs, from kernel, storage, networking to middleware. How it works Infrastructure LISA is designed to be componentized and maximize compatibility with different distros. Test cases can focus only on test logic. Once test requirements (machines, CPU, memory, etc) are defined, just write the test logic without worrying about environment setup or stopping services on different distributions. Orchestration. LISA uses platform APIs to create, modify and delete VMs. For example, LISA uses Azure API to create VMs, run test cases, and delete VMs. During the test case running, LISA uses Azure API to collect serial log and can hot add/remove data disks. If other platforms implement the same serial log and data disk APIs, the test cases can run on the other platforms seamlessly. Ensure distro compatibility by abstracting over 100 commands in test cases, allowing focus on validation logic rather than distro compatibility. Pre-processing workflow assists in building the kernel on-the-fly, installing the kernel from package repositories, or modifying all test environments. Test matrix helps one run to test all. For example, one run can test different vm sizes on Azure, or different images, even different VM sizes and different images together. Anything is parameterizable, can be tested in a matrix. Customizable notifiers enable the saving of test results and files to any type of storage and database. Agentless and low dependency LISA operates test systems via SSH without requiring additional dependencies, ensuring compatibility with any system that supports SSH. Although some test cases require installing extra dependencies, LISA itself does not. This allows LISA to perform tests on systems with limited resources or even different operating systems. For instance, LISA can run on Linux, FreeBSD, Windows, and ESXi. Getting Started with LISA Ready to dive in? Visit the LISA project at aka.ms/lisa to access the documentation. Install: Follow the installation guide provided in the repository to set up LISA in your testing environment. Run: Follow the instructions to run LISA on local machine, Azure or existing systems. Extend: Follow the documents to extend LISA by test cases, data sources, tools, platform, workflow, etc. Join the Community: Engage with other users and contributors through forums and discussions to share experiences and best practices. Contribute: Modify existing test cases or create new ones to suit your needs. Share your contributions with the community to enhance LISA's capabilities. Conclusion LISA offers open-source collaborative testing solutions designed to operate across diverse environments and scenarios, effectively narrowing the gap between enterprise demands and community-led innovation. By leveraging LISA, customers can ensure their Linux deployments are reliable and optimized for performance. Its comprehensive testing capabilities, combined with the flexibility and support of an active community, make LISA an indispensable tool for anyone involved in Linux quality assurance and testing. Your feedback is invaluable, and we would greatly appreciate your insights.How Microsoft Ensures the Quality of Linux VM Images and Platform Experiences on Azure?
In the continuously evolving landscape of cloud computing and AI, the quality and reliability of virtual machines (VMs) plays vital role for businesses running mission-critical workloads. With over 65% of Azure workloads running Linux our commitment to delivering high-quality Linux VM images and platforms remains unwavering. This involves overcoming unique challenges and implementing rigorous validation processes to ensure that every Linux VM image offered on Azure meets the high standards of quality and reliability. Ensuring the quality of Linux images and the overall platform experience on Azure involves addressing the challenges posed by a unique platform stack and the complexity of managing and validating multiple independent release cycles. High-quality Linux VMs are essential for ensuring consistent performance, minimizing downtime and regressions, and enhancing security by addressing vulnerabilities with timely updates. Figure 1: Complexity of Linux VMs in Azure VM Image Updates: Azure's Marketplace offers a diverse array of Linux distributions, each maintained by its respective publishers. These distributions release updates on their own schedules, independent of Azure's infrastructure updates. Package Updates: Within each Linux distribution, numerous packages are maintained and updated separately, adding another layer of complexity to the update and validation process. Extension and Agent Updates: Azure provides over 75+ guest VM extensions to enhance operating system capabilities, security, recovery etc. These extensions are updated independently, requiring careful validation to ensure compatibility and stability. Azure Infrastructure Updates: Azure regularly updates its underlying infrastructure, including components like Azure Boost, to improve reliability, performance, and security. VM SKUs and Sizes: Azure provides thousands of VM sizes with various combinations of CPU, memory, disk, and network configurations to meet diverse customer needs. Managing concurrent updates across all VMs poses significant QA challenges. To address this, Azure uses rigorous testing, gating and validation processes to ensure all components function reliably and meet customer expectations. Azure’s Approach to Overcoming Challenges To address these challenges, we have implemented a comprehensive validation strategy that involves testing at every stage of the image and kernel lifecycle. By adopting a shift-left approach, we execute Linux VM-specific test cases as early as possible. This strategy helps us catch failures close to the source of changes before they are deployed to Azure fleet. Our validation gates integrate with various entry points and provide coverage for a wide variety of scenarios on Azure. Upstream Kernel Validation: As a founding member of Kernel CI, Microsoft validates commits from Linux next and stable trees using Linux VMs in Azure and shares results with the community via Kernel CI DB. This enables us to detect regressions at early stages. Azure-Tuned Kernel Validation: Azure-Tuned Kernels provided by our endorsed distribution partners are thoroughly validated and signed off by Microsoft before it is released to the Azure fleet. Linux Guest Image Validation: The quality team works with endorsed distribution partners for major releases to conduct thorough validation. Each refreshed image, including those from third-party publishers, is validated and certified before being added to the marketplace. Automated pipelines are in place to validate the images once they are available in the Marketplace. Package Validation: Unattended Update: We conduct validation of packages updates with target distro to prevent regression and ensure that only tested snapshots are utilized for updating Linux VM in Azure. Guest Extension Validation: Every Azure-provided extensions undergoes Basic Validation Testing (BVT) across all images and kernel versions to ensure compatibility and functionality amidst any changes. Additionally, comprehensive release testing is conducted for major releases to maintain reliability and compatibility. New VM SKU Validation: Any new VM SKU undergoes validation to confirm it supports Linux before its release to the Azure fleet. This process includes functionality, performance and stress testing across various Linux distributions, and compatibility tests with existing Linux images in the fleet. Azure HostOS & Host Agent Validation: Updates to the Azure Host OS & Agents are thoroughly tested from the Linux guest OS perspective to confirm that changes in the Azure host environment do not result in regressions in compatibility, performance, or stability for Linux VMs. At any stage where regressions or bugs are identified, we block those releases to ensure they never reach customers. All issues are resolved and rigorously retested before images, kernels, or extension updates are made available. Through these robust validation processes, Azure ensures that Linux VMs consistently deliver to customer expectations, delivering a reliable, secure, and high-performance environment for mission-critical workloads. Validation Tools for VM Guest Images and Kernel To ensure the quality and reliability of Linux VM images and kernels on Azure, we leverage open-source kernel testing frameworks like LTP, kselftest, and fstest, along with extensive Azure-specific test cases available in LISA, to comprehensively validate all aspects of the platforms. LISA (Linux Integration Services Automation): Microsoft is committed to open source and that is no different with our testing framework LISA. LISA is an open-source core testing framework designed to meet all Linux validation needs. It includes over 400 tests covering performance, features and security, ensuring comprehensive validation of Linux images on Azure. By automating diverse test scenarios, LISA enables early detection and resolution of issues, enhancing the stability and performance of Linux VMs. Conclusion At Azure, Linux quality is a fundamental aspect of our commitment to delivering reliable VM images and platforms. Through comprehensive testing and strong collaboration with Linux distribution partners, we ensure quality and reliability of VMs while proactively identifying and resolving potential issues. This approach allows us to continually refine our processes and maintain the quality that customers expect from Azure. Quality is a core focus, and we remain dedicated to continuous improvement, delivering world-class Linux environments to businesses and customers. For us, quality is not just a priority—it’s our standard. Your feedback is invaluable, and we would greatly appreciate your insights.
Microsoft Teams for Ubuntu 20.04 will not start
I am trying to get the official MS Teams application to run on my Ubuntu 20.04 machine. No matter how I install it, it will not display any sort of window when ran normally (i.e. clicking the icon in Applications, running teams if installed with apt, or running 'snap run teams' if installed with snap). When I run snap run teams, I get this error: 2022/02/18 10:15:43.002794 cmd_run.go:1039: WARNING: cannot start document portal: Expected portal at "/run/user/197001104/doc", got "/home/username/.cache/doc" When installed with apt and I run 'teams', I get no output and the process ends immediately. The only way I am ever able to get a window to display is if I install with snap, 'snap install teams', then run it with 'snap run --trace-exec teams'. Then it works as expected. When not ran with 'snap run --trace-exec teams', I can see several processes running when I check 'ps aux | grep teams'. These never close themselves and require a 'kill <PID>' to exit. I have tried the following versions with apt/dpkg: 1.4.00.26453 1.3.00.16851 1.2.00.32451 and the version snap installs is: 1.4.00.26453 I have tried uninstalling, reinstalling, clearing my cache using the following script, and any combination of the three. https://gist.github.com/mrcomoraes/c83a2745ef8b73f9530f2ec0433772b7 ~/.config/Microsoft/Microsoft Teams/logs/teams-startup.log A JavaScript error occurred in the main process Uncaught Exception: Error: /usr/share/teams/resources/app.asar.unpacked/node_modules/native-utils/build/Release/native-utils.node: undefined symbol: _ZN5Utils32IsQuietHourOrDoNotDisturbEnabledEv at process.module.(anonymous function) [as dlopen] (ELECTRON_ASAR.js:143:31) at Object.Module._extensions..node (internal/modules/cjs/loader.js:722:18) at Object.module.(anonymous function) [as .node] (ELECTRON_ASAR.js:152:18) at Module.load (internal/modules/cjs/loader.js:602:32) at tryModuleLoad (internal/modules/cjs/loader.js:541:12) at Function.Module._load (internal/modules/cjs/loader.js:533:3) at Module.require (internal/modules/cjs/loader.js:640:17) at require (/usr/share/teams/resources/app.asar/external/v8-compile-cache/v8-compile-cache.js:173:28) at Object.<anonymous> (/usr/share/teams/resources/app.asar/node_modules/native-utils/index.js:1:173) at Object.<anonymous> (/usr/share/teams/resources/app.asar/node_modules/native-utils/index.js:3:3) snap version snap 2.54.3+20.04.1 snapd 2.54.3+20.04.1 series 16 ubuntu 20.04 kernel 5.13.0-28-generic If any other info is needed or would be useful for troubleshooting, please let me know. I am all out of ideas.
