Getting Started with the SUSE Multi-Linux Manager MCP Server and GitHub Copilot
Enterprise Linux environments are heterogeneous. That's not a problem statement - it's just the truth. SUSE, Ubuntu, RHEL, and their downstream variants coexist in every data center I've seen, and increasingly across Azure subscriptions too. AI assistants like GitHub Copilot can already connect to these machines, run commands, troubleshoot issues, apply patches one box at a time. But if you're managing a fleet of hundreds or thousands of systems across distributions, the gap isn't whether AI can touch your infrastructure. It's whether it can work through the centralized management tooling where your inventory, patch orchestration, RBAC, and audit trails actually live. SUSE just took a meaningful step to close that gap. Their Multi-Linux Manager MCP Server, built on the open source Uyuni project gives AI agents like GitHub Copilot a structured, authenticated interface to your existing management platform. Not the individual boxes. The management plane where your centralized inventory, CVE auditing, cross-distribution patch scheduling, and RBAC already live. Not a rip-and-replace. Not a new console to learn. A way to talk to the infrastructure management you've already built. This post walks through what the MCP server does, why it matters in an Azure context, and how to get it wired up with GitHub Copilot so you can start working with it today. The Model Context Protocol (MCP) is an open standard that defines how AI models connect to external tools and data sources. Think of it as the USB-C of AI integrations - a common interface so that different clients (GitHub Copilot, Claude Desktop, Gemini CLI) can talk to different servers (Azure, SUSE, databases, APIs) without bespoke glue code for every combination. Why This Matters for Azure Customers If you are running Linux workloads on Azure - whether for SAP, HPC, or traditional enterprise applications - the Multi-Linux Manager MCP server provides a conversational interface for your infrastructure without requiring you to change tools. Management-plane depth, not just infrastructure inventory. Azure and Copilot already give you fleet-wide visibility into your VMs. The SUSE MCP server adds the layer underneath: patch scheduling state, erratum tracking, cross-distribution CVE audits, and system group management that lives in your Multi-Linux Manager instance. A single pane of glass. Pair this with the Azure MCP Server and your AI assistant can move between Azure resource operations and OS-level fleet management in one conversation, across the distributions Multi-Linux Manager supports, without switching tools or contexts. What You Can Actually Do With It The MCP server exposes over 20 practical tools for day-to-day infrastructure operations. Instead of relying on a generic knowledge base, Copilot queries your actual infrastructure. Inventory and Inspection: You can list active systems across your fleet or pull detailed event histories for specific machines. Patch Management and CVE Response: Copilot can rapidly audit all systems for pending updates or identify specific machines vulnerable to a new CVE. Operational Actions: You can list system groups, register new systems, or schedule server reboots. The Security Model: Human-in-the-Loop Letting an AI agent touch production infrastructure raises the obvious question: what keeps it from doing something destructive? SUSE has been deliberate about this by designing the MCP server with a default "human-in-the-loop" security model. Read-Only by Default: The server ships with all write actions disabled (UYUNI_MCP_WRITE_TOOLS_ENABLED=false). Explicit Confirmation: If you enable write tools, Copilot is required to ask for your explicit confirmation before executing state-changing actions like applying patches or scheduling reboots. Enterprise Authentication: The server supports OAuth 2.0, ensuring the AI agent authenticates through your identity provider. Layered Governance: Combined with Multi-Linux Manager’s role-based access control (RBAC) and the principle of least privilege for the service account, you get layered governance without bolting on a separate approval system. AI-assisted operations that bypass human judgment won't get adopted in enterprises. AI-assisted operations that make the human faster while keeping them in control, that's the model that actually ships. Architecture on Azure Here's the topology we're working with: SUSE Multi-Linux Manager - Running on an Azure VM, managing your Linux fleet across distributions. This is the control plane for your systems - inventory, patching, configuration. Available on Azure Marketplace. MCP Server - Runs as a container (Docker/Podman), either locally alongside your dev environment or as a standalone HTTP service. The MCP Server container is available in SUSE Registry and is backed by a secure, trusted software supply chain. GitHub Copilot - In VS Code or the CLI. Configured to use the MCP server as a tool source. Sends natural language requests, receives structured responses from your infrastructure. Your Linux fleet on Azure - Whatever Multi-Linux Manager manages for you. The MCP server doesn't care about the distribution mix; that's the whole point of Multi-Linux Manager. Getting Started: Step by Step Prerequisites A running SUSE Multi-Linux Manager instance managing your Linux estate Docker or Podman installed on your workstation (for local deployment) or network access to a remote MCP server instance GitHub Copilot with agent mode enabled (VS Code or CLI) Step 1: Stand up the MCP Server For local deployment, pull the container and point it at your Multi-Linux Manager instance following the project documentation. For remote/team deployments, your administrator can run the server as a standalone HTTP service with OAuth 2.0. Step 2: Configure GitHub Copilot In VS Code, open the Command Palette and type GitHub Copilot: Configure MCP Servers. Add your server to the config: { "mcpServers": { "suse-multi-linux-manager": { "type": "http", "url": "https://your-mcp-server.example.com/mcp" } } } Step 3: Verify the Connection Open GitHub Copilot and try a read-only query: "List all active systems managed by my SUSE Multi-Linux Manager." If your fleet inventory appears, you're connected. Step 4: Start Operating "Are any of my systems affected by CVE-2026-XXXX?" "Show me all systems that have pending but unscheduled security patches." "Which systems need a reboot?" Getting Involved The SUSE Multi-Linux Manager MCP server is open source under the Apache 2.0 license, built on the Uyuni project. The current v0.5 is a tech preview. Feedback goes to uyuni-project/uyuni#10562, bugs to GitHub Issues. The gap in AI-assisted Linux operations was never whether AI could reach your infrastructure. It was whether it could work through the management tooling where your fleet-scale decisions actually get made. SUSE built the bridge to that layer. GitHub Copilot is the conversational interface. Your fleet is already there. Go connect them.From Policy to Practice: Built-In CIS Benchmarks on Azure - Flexible, Hybrid-Ready
Security is more important than ever. The industry-standard for secure machine configuration is the Center for Internet Security (CIS) Benchmarks. These benchmarks provide consensus-based prescriptive guidance to help organizations harden diverse systems, reduce risk, and streamline compliance with major regulatory frameworks and industry standards like NIST, HIPAA, and PCI DSS. In our previous post, we outlined our plans to improve the Linux server compliance and hardening experience on Azure and shared a vision for integrating CIS Benchmarks. Today, that vision has turned into reality. We're now announcing the next phase of this work: Center for Internet Security (CIS) Benchmarks are now available on Azure for all Azure endorsed distros, at no additional cost to Azure and Azure Arc customers. With today's announcement, you get access to the CIS Benchmarks on Azure with full parity to what’s published by the Center for Internet Security (CIS). You can adjust parameters or define exceptions, tailoring security to your needs and applying consistent controls across cloud, hybrid, and on-premises environments - without having to implement every control manually. Thanks to this flexible architecture, you can truly manage compliance as code. How we achieve parity To ensure accuracy and trust, we rely on and ingest CIS machine-readable Benchmark content (OVAL/XCCDF files) as the source of truth. This guarantees that the controls and rules you apply in Azure match the official CIS specifications, reducing drift and ensuring compliance confidence. What’s new under the hood At the core of this update is azure-osconfig’s new compliance engine - a lightweight, open-source module developed by the Azure Core Linux team. It evaluates Linux systems directly against industry-standard benchmarks like CIS, supporting both audit and, in the future, auto-remediation. This enables accurate, scalable compliance checks across large Linux fleets. Here you can read more about azure-osconfig. Dynamic rule evaluation The new compliance engine supports simple fact-checking operations, evaluation of logic operations on them (e.g., anyOf, allOf) and Lua based scripting, which allows to express complex checks required by the CIS Critical Security Controls - all evaluated natively without external scripts. Scalable architecture for large fleets When the assignment is created, the Azure control plane instructs the machine to pull the latest Policy package via the Machine Configuration agent. Azure-osconfig’s compliance engine is integrated as a light-weight library to the package and called by Machine Configuration agent for evaluation – which happens every 15-30minutes. This ensures near real-time compliance state without overwhelming resources and enables consistent evaluation across thousands of VMs and Azure Arc-enabled servers. Future-ready for remediation and enforcement While the Public Preview starts with audit-only mode, the roadmap includes per-rule remediation and enforcement using technologies like eBPF for kernel-level controls. This will allow proactive prevention of configuration drift and runtime hardening at scale. Please reach out if you interested in auto-remediation or enforcement. Extensibility beyond CIS Benchmarks The architecture was designed to support other security and compliance standards as well and isn’t limited to CIS Benchmarks. The compliance engine is modular, and we plan to extend the platform with STIG and other relevant industry benchmarks. This positions Azure as a platform for a place where you can manage your compliance from a single control-plane without duplicating efforts elsewhere. Collaboration with the CIS This milestone reflects a close collaboration between Microsoft and the CIS to bring industry-standard security guidance into Azure as a built-in capability. Our shared goal is to make cloud-native compliance practical and consistent, while giving customers the flexibility to meet their unique requirements. We are committed to continuously supporting new Benchmark releases, expanding coverage with new distributions and easing adoption through built-in workflows, such as moving from your current Benchmark version to a new version while preserving your custom configurations. Certification and trust We can proudly announce that azure-osconfig has met all the requirements and is officially certified by the CIS for Benchmark assessment, so you can trust compliance results as authoritative. Minor benchmark updates will be applied automatically, while major version will be released separately. We will include workflows to help migrate customizations seamlessly across versions. Key Highlights Built-in CIS Benchmarks for Azure Endorsed Linux distributions Full parity with official CIS Benchmarks content and certified by the CIS for Benchmark Assessment Flexible configuration: adjust parameters, define exceptions, tune severity Hybrid support: enforce the same baseline across Azure, on-prem, and multi-cloud with Azure Arc Reporting format in CIS tooling style Supported use cases Certified CIS Benchmarks for all Azure Endorsed Distros - Audit only (L1/L2 server profiles) Hybrid / On-premises and other cloud machines with Azure Arc for the supported distros Compliance as Code (example via Github -> Azure OIDC auth and API integration) Compatible with GuestConfig workbook What’s next? Our next mission is to bring the previously announced auto-remediation capability into this experience, expand the distribution coverage and elevate our workflows even further. We’re focused on empowering you to resolve issues while honoring the unique operational complexity of your environments. Stay tuned! Get Started Documentation link for this capability Enable CIS Benchmarks in Machine Configuration and select the “Official Center for Internet Security (CIS) Benchmarks for Linux Workloads” then select the distributions for your assignment, and customize as needed. In case if you want any additional distribution supported or have any feedback for azure-osconfig – please open an Azure support case or a Github issue here Relevant Ignite 2025 session: Hybrid workload compliance from policy to practice on Azure Connect with us at Ignite Meet the Linux team and stop by the Linux on Azure booth to see these innovations in action: Session Type Session Code Session Name Date/Time (PST) Theatre THR 712 Hybrid workload compliance from policy to practice on Azure Tue, Nov 18/ 3:15 PM – 3:45 PM Breakout BRK 143 Optimizing performance, deployments, and security for Linux on Azure Thu, Nov 20/ 1:00 PM – 1:45 PM Breakout BRK 144 Build, modernize, and secure AKS workloads with Azure Linux Wed, Nov 19/ 1:30 PM – 2:15 PM Breakout BRK 104 From VMs and containers to AI apps with Azure Red Hat OpenShift Thu, Nov 20/ 8:30 AM – 9:15 AM Theatre THR 701 From Container to Node: Building Minimal-CVE Solutions with Azure Linux Wed, Nov 19/ 3:30 PM – 4:00 PM Lab Lab 505 Fast track your Linux and PostgreSQL migration with Azure Migrate Tue, Nov 18/ 4:30 PM – 5:45 PM PST Wed, Nov 19/ 3:45 PM – 5:00 PM PST Thu, Nov 20/ 9:00 AM – 10:15 AM PSTAzure 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 GroupMicrosoft at SUSECON 2025: Join us!
Microsoft Azure is thrilled to participate in SUSECON 2025, where we look forward to sharing insights, learning, and collaborating with everyone attending. Discover why Microsoft Azure is a trusted and proven cloud platform and explore the benefits of Azure-optimized solutions co-developed by Microsoft and SUSE for your business-critical Linux workloads. Here's what you can look forward to: Engaging Sessions: Microsoft experts will be delivering both in-person and virtual in-depth sessions on a variety of topics, including SAP, SQL Server, Kubernetes, AI and more. Keynote session | Wednesday, March 12 th @ 9:00 AM Technical breakout session | Tuesday, March 11 th @ 1:30 PM EDT : Unlocking Synergies: SUSE & Microsoft High Availability Innovations for SAP solutions Technical breakout session | Thursday, March 13 th @ 11:00 AM EDT Harnessing SQL Server 2025 and SUSE Rancher for a Unified Data Platform Across Containers, Physical, and Virtual Machines with DH2i's DxOperator Virtual session: How to Build a Secure and Resilient Production Environment for Your AI Applications Virtual session: Streamlining SAP Workloads on Azure using openQA Meet the Experts: Take advantage of the opportunity to interact with Azure experts at our booth. Discuss your unique challenges and explore the best practices for maximizing the potential of your SUSE workloads in Azure. Interactive Demos: Visit our booth (#9-12) to see live demos of the latest technologies and solutions for running business-critical SUSE workloads on Azure. Xbox Raffle and Swag: Visit our booth or attend one of our in-person sessions to enter our raffle for a chance to win an Xbox Series. Plus, don’t miss out on the opportunity to grab some great swag at the booth! Also, there’s still time to take advantage of the Linux promotional offer! With the offer, you can save up to 15% in addition to the existing one-year Azure Reserved VM Instances discount for select Linux VMs for a limited period. Read the blog to learn more. We look forward to seeing you at SUSECON! Learn more Open Source at Microsoft Linux on Azure SUSE on AzureLinux and Open Source on Azure Quarterly Update - February 2025
As we venture into 2025, it's exhilarating to reflect on the astonishing strides we've made in the domain of Linux and Open Source Software (OSS) on Azure. Let us dive into another edition of the quarterly update to learn more! Microsoft Ignite 2024 Linux on Azure took center stage at Microsoft Ignite 2024 with dedicated session and a meet-up booth. Our breakout session, theater session, and lab session drew over 500 attendees. This engagement is a testament to the enthusiasm and interest in Linux-based solutions on Azure. Check out the on-demand recording available on the Ignite website: What’s new in Linux: How we’re collaborating to help shape its future We announced that the Azure security baseline through Azure Policy and Machine Configuration for Linux has moved to public preview, and we are expanding the capabilities with built-in auto-remediation feature (limited public preview). Red Hat on Azure announcements at Ignite are captured here. Linux Promotional Offer The promotional offer for the latest Linux VMs in Azure is currently live. For a limited time, you can save an additional 15% on one-year Azure Reserved Virtual Machine (VM) Instances for the latest Linux VMs. This means you could save up to 56% compared to running an Azure VM on a PAYG (pay-as-you-go) basis. This offer is available until March 31, 2025. To learn more, read the blog and refer to the terms and conditions. Azure Linux 3.0 in preview on Azure Kubernetes Service v1.31 We are excited to announce that Azure Linux 3.0, the next major version release of the Azure Linux container host for Azure Kubernetes Service (AKS), is now available in preview on AKS version 1.31. Azure Linux 3.0 offers increased package availability and versions, an updated kernel, and improvements to performance, security, and tooling and developer experience. SUSE LTSS on Azure Marketplace Many of our customers rely on SUSE Linux Enterprise Server (SLES) for running their mission-critical SAP and HPC (high-performance computing) workloads on Azure. We’re excited to share that SUSE Long Term Service Pack Support (LTSS) is available in the Azure Marketplace, providing customers with options for managing the support lifecycle of their SUSE images in Azure. The blog announcement is here. Linux VM Image Quality on Azure In the continuously evolving landscape of cloud computing and AI, the quality and reliability of virtual machines (VMs) plays a 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. Find out how Microsoft ensures the quality of Linux VM images and platform experiences on Azure. Learn how LISA (an open-source tool) enhances the testing and validation processes for Linux kernels and guest OS images on Azure. MIT Technology Review Article We recently commissioned a sponsored article in collaboration with AMD on the topic of “Accelerating AI innovation through application modernization” published on MIT Technology Review. The article delves into AI driving new requirements for application modernization. Red Hat Summit Connects Microsoft’s sponsorship of the Red Hat Summit Connect global event series proved to be a resounding success. Spanning cities from Melbourne to Mexico City, we engaged with over 6,500 attendees. By partnering with key organizations, we reinforced the strength of our strategic alliance with Red Hat. What’s coming up next Migrate to Innovate Summit This event aims to showcase how cloud migration and modernization can build a platform for AI innovation. In 2.5 hours, the event will feature thought leaders and experts from Microsoft and Intel who will share their perspectives, present real-world case studies, and showcase product demonstrations to help customers accelerate their cloud journey. The event will be live on March 11, 2025. Register to check out the great content! SUSECON 2025 We will be at SUSECON 2025, which will take place in Orlando, Florida, from March 10th – 14th, 2025. We look forward to sharing insights, learning, and collaborating with everyone attending. Discover why Microsoft Azure is a trusted and proven cloud platform and explore the benefits of Azure-optimized solutions co-developed by Microsoft and SUSE for your business-critical Linux workloads. Check out one of the Microsoft sessions and meet with us at our booth. We recently published a recap covering some of Microsoft partners’ latest offerings on Linux and PGSQL. Stay tuned for more updates and thank you for being a part of this journey!Extend the lifecycle of SUSE Linux Enterprise Server in Azure
Many of our customers rely on SUSE Linux Enterprise Server (SLES) for running their mission-critical SAP and HPC (high-performance computing) workloads on Azure. We collaborate closely with SUSE to ensure customers receive the optimal experience for deploying SUSE solutions in Azure, offering integrated support from both Microsoft and SUSE support and jointly developed Azure-optimized solutions. We’re excited to share that SUSE Long Term Service Pack Support (LTSS) is available in the Azure Marketplace, providing customers with options for managing the support lifecycle of their SUSE images in Azure. Introducing SUSE Long Term Service Pack Support (LTSS) on Azure Marketplace SUSE LTSS offers extended support for SLES, SLES for SAP, and SLES HPC, providing an additional 3 years of critical security patches and maintenance updates. This allows you to maintain stability and security without the immediate need for a costly and time-consuming migration. SUSE LTSS is suitable for customers who need to stay on a specific SLES version that is reaching end of support because of application requirements or needing more time to upgrade. For example, customers may choose to use SUSE LTSS to extend the support of SLES 12 SP5, SLES for SAP 12 SP5, and SLES HPC 12 SP5, which reached end of general support on October 31, 2024. For more information on options for dealing with the end of general support of SLES 12 SP5, SLES for SAP 12 SP5, and SLES HPC 12, read the SUSE blog. Key features of SUSE LTSS: Up to 3 years of extended support Backed by Microsoft and SUSE Uninterrupted security patching No need to refactor your application stack Enhanced security with access to expanded software repositories Get Started with SUSE LTSS Today Visit the SUSE LTSS on Azure Marketplace to get started. Learn more Check out the following resources from SUSE: SUSE Product Support Lifecycle SUSE LTSS Documentation SUSE LTSS in the Public Cloud How to add LTSS to a SLES 12? SUSE LTSS FOR PAYG Instances Simplified SUSE LTSS FAQs
