Unleashing GitHub Copilot for Infrastructure as Code
Introduction In the world of managing infrastructure, things are always changing. People really want solutions that work, can handle big tasks, and won't let them down. Now, as more companies switch to using cloud-based systems and start using Infrastructure as Code (IaC), the job of folks who handle infrastructure is getting even more important. They're facing new problems in setting up and keeping everything running smoothly. The Challenges faced by Infrastructure Professionals Complexity of IaC: Managing infrastructure through code introduces a layer of complexity. Infrastructure professionals often grapple with the intricate syntax and structure required by tools like Terraform and PowerShell. This complexity can lead to errors, delays, and increased cognitive load. Consistency Across Environments: Achieving consistency across multiple environments—development, testing, and production—poses a significant challenge. Maintaining uniformity in configurations is crucial for ensuring the reliability and stability of the deployed infrastructure. Learning Curve: The learning curve associated with IaC tools and languages can be steep for those new to the domain. As teams grow and diversify, onboarding members with varying levels of expertise becomes a hurdle. Time-Consuming Development Cycles: Crafting infrastructure code manually is a time-consuming process. Infrastructure professionals often find themselves reinventing the wheel, writing boilerplate code, and handling repetitive tasks that could be automated. Unleashing GitHub Copilot for Infrastructure as Code In response to these challenges, Leveraging GitHub Copilot to generate infra code specifically for infrastructure professionals is helping to revolutionize the way infrastructure is written, addressing the pain points experienced by professionals in the field. The Significance of GH Copilot for Infra Code Generation with accuracy: Copilot harnesses the power of machine learning to interpret the intent behind prompts and swiftly generate precise infrastructure code. It understands the context of infrastructure tasks, allowing professionals to express their requirements in natural language and receive corresponding code suggestions. Streamlining the IaC Development Process: By automating the generation of infrastructure code, Copilot significantly streamlines the IaC development process. Infrastructure professionals can now focus on higher-level design decisions and business logic rather than wrestling with syntax intricacies. Consistency Across Environments and Projects: GH Copilot ensures consistency across environments by generating standardized code snippets. Whether deploying resources in a development, testing, or production environment, GH Copilot helps maintain uniformity in configurations. Accelerating Onboarding and Learning: For new team members and those less familiar with IaC, GH Copilot serves as an invaluable learning service. It provides real-time examples and best practices, fostering a collaborative environment where knowledge is shared seamlessly. Efficiency and Time Savings: The efficiency gains brought about by GH Copilot are substantial. Infrastructure professionals can witness a dramatic reduction in development cycles, allowing for faster iteration and deployment of infrastructure changes. Copilot in Action Prerequisites 1.Install visual studio code latest version - https://code.visualstudio.com/download Have a GitHub Copilot license with a personal free trial or your company/enterprise GitHub account, install the Copilot extension, and sign in from Visual Studio Code. https://docs.github.com/en/copilot/quickstart Install the PowerShell extension for VS Code, as we are going to use PowerShell for our IaC sample. Below is the PowerShell code generated using VS Code & GitHub Copilot. It demonstrates how to create a simple Azure VM. We're employing a straightforward prompt with #, with the underlying code automatically generated within the VS Code editor. Another example to create azure vm with vm scale set with minimum and maximum number of instance count. Prompt used with # in below example. The PowerShell script generated above can be executed either from the local system or from the Azure Portal Cloud Shell. Similarly, we can create Terraform and devops code using this Infra Copilot. Conclusion In summary, GH Copilot is a big deal in the world of infrastructure as code. It helps professionals overcome challenges and brings about a more efficient and collaborative way of working. As we finish talking about GH Copilot's abilities, the examples we've looked at have shown how it works, what technologies it uses, and how it can be used in real life. This guide aims to give infrastructure professionals the info they need to improve how they do infrastructure as code.Announcing Cobalt 200: Azure’s next cloud-native CPU
By Selim Bilgin, Corporate Vice President, Silicon Engineering, and Pat Stemen, Vice President, Azure Cobalt Today, we’re thrilled to announce Azure Cobalt 200, our next-generation Arm-based CPU designed for cloud-native workloads. Cobalt 200 is a milestone in our continued approach to optimize every layer of the cloud stack from silicon to software. Our design goals were to deliver full compatibility for workloads using our existing Azure Cobalt CPUs, deliver up to 50% performance improvement over Cobalt 100, and integrate with the latest Microsoft security, networking and storage technologies. Like its predecessor, Cobalt 200 is optimized for common customer workloads and delivers unique capabilities for our own Microsoft cloud products. Our first production Cobalt 200 servers are now live in our datacenters, with wider rollout and customer availability coming in 2026. Azure Cobalt 200 SoC and platform Building on Cobalt 100: Leading Price-Performance Our Azure Cobalt journey began with Cobalt 100, our first custom-built processor for cloud-native workloads. Cobalt 100 VMs have been Generally Available (GA) since October of 2024 and availability has expanded rapidly to 32 Azure datacenter regions around the world. In just one year, we have been blown away with the pace that customers have adopted the new platform, and migrated their most critical workloads to Cobalt 100 for the performance, efficiency, and price-performance benefits. Cloud analytics leaders like Databricks and Snowflake are adopting Cobalt 100 to optimize their cloud footprint. The compute performance and energy-efficiency balance of Cobalt 100-based virtual machines and containers has proven ideal for large-scale data processing workloads. Microsoft’s own cloud services have also rapidly adopted Azure Cobalt for similar benefits. Microsoft Teams achieved up to 45% better performance using Cobalt 100 than their previous compute platform. This increased performance means less servers needed for the same task, for instance Microsoft Teams media processing uses 35% fewer compute cores with Cobalt 100. Designing Compute Infrastructure for Real Workloads With this solid foundation, we set out to design a worthy successor – Cobalt 200. We faced a key challenge: traditional compute benchmarks do not represent the diversity of our customer workloads. Our telemetry from the wide range of workloads running in Azure (small microservices to globally available SaaS products) did not match common hardware performance benchmarks. Existing benchmarks tend to skew toward CPU core-focused compute patterns, leaving gaps in how real-world cloud applications behave at scale when using network and storage resources. Optimizing Azure Cobalt for customer workloads requires us to expand beyond these CPU core benchmarks to truly understand and model the diversity of customer workloads in Azure. As a result, we created a portfolio of benchmarks drawn directly from the usage patterns we see in Azure, including databases, web servers, storage caches, network transactions, and data analytics. Each of our benchmark workloads includes multiple variants for performance evaluation based on the ways our customers may use the underlying database, storage, or web serving technology. In total, we built and refined over 140 individual benchmark variants as part of our internal evaluation suite. With the help of our software teams, we created a complete digital twin simulation from the silicon up: beginning with the CPU core microarchitecture, fabric, and memory IP blocks in Cobalt 200, all the way through the server design and rack topology. Then, we used AI, statistical modelling and the power of Azure to model the performance and power consumption of the 140 benchmarks against 2,800 combinations of SoC and system design parameters: core count, cache size, memory speed, server topology, SoC power, and rack configuration. This resulted in the evaluation of over 350,000 configuration candidates of the Cobalt 200 system as part of our design process. This extensive modelling and simulation helped us to quickly iterate to find the optimal design point for Cobalt 200, delivering over 50% increased performance compared to Cobalt 100, all while continuing to deliver our most power-efficient platform in Azure. Cobalt 200: Delivering Performance and Efficiency At the heart of every Cobalt 200 server is the most advanced compute silicon in Azure: the Cobalt 200 System-on-Chip (SoC). The Cobalt 200 SoC is built around the Arm Neoverse Compute Subsystems V3 (CSS V3), the latest performance-optimized core and fabric from Arm. Each Cobalt 200 SoC includes 132 active cores with 3MB of L2 cache per-core and 192MB of L3 system cache to deliver exceptional performance for customer workloads. Power efficiency is just as important as raw performance. Energy consumption represents a significant portion of the lifetime operating cost of a cloud server. One of the unique innovations in our Azure Cobalt CPUs is individual per-core Dynamic Voltage and Frequency Scaling (DVFS). In Cobalt 200 this allows each of the 132 cores to run at a different performance level, delivering optimal power consumption no matter the workload. We are also taking advantage of the latest TSMC 3nm process, further improving power efficiency. Security is top-of-mind for all of our customers and a key part of the unique innovation in Cobalt 200. We designed and built a custom memory controller for Cobalt 200, so that memory encryption is on by default with negligible performance impact. Cobalt 200 also implements Arm’s Confidential Compute Architecture (CCA), which supports hardware-based isolation of VM memory from the hypervisor and host OS. When designing Cobalt 200, our benchmark workloads and design simulations revealed an interesting trend: several universal compute patterns emerged – compression, decompression, and encryption. Over 30% of cloud workloads had significant use of one of these common operations. Optimizing for these common operations required a different approach than just cache sizing and CPU core selection. We designed custom compression and cryptography accelerators – dedicated blocks of silicon on each Cobalt 200 SoC – solely for the purpose of accelerating these operations without sacrificing CPU cycles. These accelerators help reduce workload CPU consumption and overall costs. For example, by offloading compression and encryption tasks to the Cobalt 200 accelerator, Azure SQL is able to reduce use of critical compute resources, prioritizing them for customer workloads. Leading Infrastructure Innovation with Cobalt 200 Azure Cobalt is more than just an SoC, and we are constantly optimizing and accelerating every layer in the infrastructure. The latest Azure Boost capabilities are built into the new Cobalt 200 system, which significantly improves networking and remote storage performance. Azure Boost delivers increased network bandwidth and offloads remote storage and networking tasks to custom hardware, improving overall workload performance and reducing latency. Cobalt 200 systems also embed the Azure Integrated HSM (Hardware Security Module), providing customers with top-tier cryptographic key protection within Azure’s infrastructure, ensuring sensitive data stays secure. The Azure Integrated HSM works with Azure Key Vault for simplified management of encryption keys, offering high availability and scalability as well as meeting FIPS 140-3 Level 3 compliance. An Azure Cobalt 200 server in a validation lab Looking Forward to 2026 We are excited about the innovation and advanced technology in Cobalt 200 and look forward to seeing how our customers create breakthrough products and services. We’re busy racking and stacking Cobalt 200 servers around the world and look forward to sharing more as we get closer to wider availability next year. Check out Microsoft Ignite opening keynote Read more on what's new in Azure at Ignite Learn more about Microsoft's global infrastructureMt Diablo - Disaggregated Power Fueling the Next Wave of AI Platforms
AI platforms have quickly shifted the industry from rack powers near 20 kilowatts to a hundred kilowatts and beyond in just the span of a few years. To enable the largest accelerator pod size within a physical rack domain, and enable scalability between platforms, we are moving to a disaggregated power rack architecture. Our disaggregated power rack is known as Mt Diablo and comes in both 48 Volt and 400 Volt flavors. This shift enables us to leverage more of the server rack for AI accelerators and at the same time gives us the flexibility to scale the power to meet the needs of today’s platforms and the platforms of the future. This forward thinking strategy enables us to move faster and foster collaboration to power the world’s most complex AI systems.OCP-SAFE, a systematic hardware security appraisal framework
In the ever-evolving landscape of data center technology, security is paramount. Today, data centers are an intricate web of diverse processing devices and peripherals, all dependent on firmware. But how can we ensure the security and reliability of this critical code? Microsoft and Google have joined forces with the Open Compute Foundation to introduce OCP - SAFE (Security Appraisal Framework Enablement). This framework introduces systematic firmware security reviews that focus on firmware provenance, development practices, and vulnerability check. In this article, we explore how OCP - SAFE standardizes security requirements, streamlines compliance, and empowers hardware device manufacturers to meet security assurance standards across various market segments, reducing time-to-market, expanding market reach, and enhancing product quality.Liquid Cooling in Air Cooled Data Centers on Microsoft Azure
With the advent of artificial intelligence and machine learning (AI/ML), hyperscale datacenters are increasingly accommodating AI accelerators at scale, demanding higher power at higher density than is customary in traditionally air-cooled facilities. As Microsoft continues to expand our growing datacenter fleet to enable the world’s AI transformation, we are faced with a need to develop methods for utilizing air-cooled datacenters to provide liquid cooling capabilities for new AI . Additionally, increasing per-rack-density for AI accelerators necessitates the use of standalone liquid-to-air heat-exchangers to support legacy datacenters that are typically not equipped with the infrastructure to support direct-to-chip (DTC) liquid cooling.
