Microsoft announces new collaboration with Red Button for attack simulation testing

As we highlighted in our latest attack trends report, Distributed Denial-of-Service (DDoS) attacks are one of the biggest security concerns today. Whether in the cloud or on-premises, DDoS attacks can be targeted at any endpoint that is publicly reachable through the internet. Planning and preparing for a DDoS attack is crucial to a well-vetted incident management response plan.

Today, Microsoft is excited to announce a new collaboration with Red Button, offering our customers an additional DDoS attack simulation testing provider to choose from. With Red Button’s DDoS Testing service suite, you will be able to work with a dedicated team of experts to simulate real-world DDoS attack scenarios in a controlled environment. Simulation testing allows you to assess your current state of readiness, identify gaps in your incident response procedures, and guide you in developing a proper DDoS response strategy.

Red Button DDoS Testing

Red Button’s DDoS Testing service suite includes three stages:

1. Planning session

Red Button experts meet with your team to understand your network architecture, assemble technical details, and define clear goals and testing schedules. This includes planning the DDoS test scope and targets, attack vectors, and attack rates. The joint planning effort is detailed in a test plan document.

2. Controlled DDoS attack

Based on the defined goals, the Red Button team launches a combination of multi-vector DDoS attacks. The test typically lasts between three to six hours. Attacks are securely executed using dedicated servers and are controlled and monitored using Red Button’s management console.

3. Summary and recommendations

The Red Button team provides you with a written DDoS Test Report outlining the effectiveness of DDoS mitigation. The report includes an executive summary of the test results, a complete log of the simulation, a list of vulnerabilities within your infrastructure, and recommendations on how to correct them.

Here is an example of a DDoS Test Report from Red Button:

In addition, Red Button offers two other service suites that can complement the DDoS Testing service suite:

◉ DDoS 360 is an “all included” annual service that includes the DDoS Testing, DDoS Hardening, DDoS team skills development, and DDoS Incident Response services. The program consists of multiple year-round activities carried out by Red Button’s top DDoS experts, which includes extensive pre-attack activities to strengthen your technological infrastructure and improve the skills of your teams as well as a dedicated incident response expert team in the event of an attack.

◉ DDoS Incident Response (IR) is a 30-day incident response service that consists of three phases: when under a DDoS attack or DDoS threat (for example, DDoS ransom threat), Red Button DDoS experts are immediately assigned and work closely with your security and IT teams to analyze the attack and apply the appropriate mitigations. Once the attack has been fully mitigated, Red Button audits your network architecture and DDoS protection system configuration, including running a DDoS test and provides detailed recommendations for hardening and optimization to prevent future attacks. Lastly, Red Button conducts DDoS training for your teams to increase your skills and readiness, and helps you build a DDoS Playbook that provides detailed procedures and activities to prepare for any future attack.

Azure DDoS simulation testing policy

Red Button’s simulation environment is built within Azure. You can only simulate attacks against Azure-hosted public IP addresses that belong to an Azure subscription of your own, which will be validated by Azure Active Directory (Azure AD) before testing. Additionally, these target public IP addresses must be protected under Azure DDoS Protection. Red Button services are sold separately from Azure DDoS Protection and can be purchased directly through Red Button.

You may only simulate attacks using our approved testing partners:

◉ Red Button.

◉ BreakingPoint Cloud.

Source: microsoft.com

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How Microsoft measures datacenter water and energy use to improve Azure Cloud sustainability

One of the biggest topics of discussion at COP26, the global climate conference held in November 2021, was how a lack of reliable and consistent measurement hampers progress on the path to Net Zero. I have been reflecting on this issue and, on this Earth Day, I would like to provide an update on how we are measuring energy and water use at our datacenters to improve sustainability across the Azure Cloud.

Today, we’re sharing an important update on how Microsoft, and our datacenters, are helping to solve our part of this measurement challenge.

While the environmental goals are similar, each industry has unique challenges in measuring its carbon emissions to build its sustainability strategy. It’s one of the key reasons we, together with ClimateWorks Foundation and 20 other leading organizations, launched the Carbon Call. It’s also why we developed Microsoft Cloud for Sustainability, an Azure-based platform that allows organizations to combine disparate data sources into one place and help provide insights into how to improve their sustainability approaches.

You’ve told us just how important measuring energy and water consumption from our datacenters is in taking sustainability into account for commercial decisions. Below you will see, for the first time, our datacenter PUE (Power Usage Effectiveness) and WUE (Water Usage Effectiveness) metrics. To address these capabilities, we set design goals—our theoretical estimates of the most efficient we can operate our datacenters—and ensure we have measurements of our actual efficiencies. These targets can vary between datacenter generations and usage; for instance, newer datacenter generations as well as datacenters operating at peak utilization are more efficient. We track these statistics at a global level and by our operating geographies—Americas, Asia Pacific, and EMEA (Europe, Middle East, Africa).

Understanding Power Usage Effectiveness (PUE)

PUE is an industry metric that measures how efficiently a datacenter consumes and uses the energy that powers the datacenter, including the operation of systems like powering, cooling, and operating the servers, data networks and lights. The closer the PUE number is to “1,” the more efficient the use of energy.

While local environment and infrastructure can affect how PUE is calculated, there are also slight variations across providers. Here’s the simplest way to think about PUE.

We design and build our datacenters toward the optimum PUE figure. We can also predict, with a high degree of accuracy, that optimum PUE figure. As we constantly innovate, we factor these changes into our datacenter designs to get as close to “1” as feasible. Our newest generation of datacenters have a design PUE of 1.12 and, with each new generation, we strive to become even more efficient. In the chart below, the blue bars show our estimated, or designed, PUE figures, while the grey bars indicate our actual PUE figures. As you can see, in Asia Pacific our actual PUE is higher; that’s due in part to higher ambient temperatures in the region which necessitates additional cooling.

In almost every region, our actual operating PUE is more efficient than our designs.

Understanding Water Usage Effectiveness (WUE)

Water Usage Effectiveness (WUE) is another key metric relating to the efficient and sustainable operations of our datacenters and is a crucial aspect as we work towards our commitment to be water positive by 2030.

WUE is calculated by dividing the number of liters of water used for humidification and cooling by the total annual amount of power (measured in kWh) needed to operate our datacenter IT equipment.

Like PUE, there are variables that can impact WUE—many of which relate to the location of the datacenter. Humid locations often have more atmospheric water, while arid locations have very little. Datacenters in colder parts of the world, like Sweden and Finland operate in naturally cooler environments so require less water for cooling. Our datacenter designs minimize water use. The chart below shows (in blue) our estimated or designed WUE figure, and in grey, our actual WUE figure. Again, Asia Pacific is higher due to higher ambient temperatures and as a result the need in some places for water-cooled chillers.

We continue to integrate our standards in water reduction technologies such as those in our Phoenix, Arizona datacenter where we use direct outside air most of the year to cool servers. We otherwise cool through direct evaporation that requires a fraction of the water compared to other, conventional water-based cooling systems such as water-cooled chillers.

Furthermore, by powering our datacenter with power from the Sun Streams 2 Solar Project owned by local partner, Longroad Energy, we’re displacing the water needed in the traditional electricity generation process and expect to save 356 million liters of water annually.

Scope 3 and supply chain

As we shared in March with our annual sustainability report, we made good progress on a number of our goals. Across the company’s operations, we saw an overall reduction in our Scope 1 and Scope 2 emissions of about 17 percent year over year, through our purchasing of renewable energy. At the same time, we also saw a rise in our Scope 3 emissions, which increased about 23 percent year over year.

We know that Scope 3 emissions (representing the total emissions across a company’s entire value chain) are the most difficult to control and reduce, because we can often only influence change. We know this is a long-term effort and this year we have increased our focus on operational discipline that is rooted in reliable data. We’ve also been working with partners across the industry, including Infrastructure Masons on carbon transparency within the datacenter supply chain, and will have exciting news to share at the Datacloud Global Congress on April 25 to 27.

Source: microsoft.com

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Azure Purview is now Microsoft Purview

In September of 2021, we announced the highly anticipated general availability of Azure Purview—a cloud-native data governance solution to enable organizations of all sizes to manage and govern their on-premises, multicloud, and software as a service (SaaS) data. Since Azure Purview was brought onto the market, thousands of organizations including London Heathrow Airport, Grundfos, and illimity have collectively discovered tens of billions of data assets as well as served up millions of searches every month to empower knowledge workers to find valuable enterprise data quickly and easily. 

Organizations that use Azure Purview have a more holistic understanding of their hybrid data estate, which is always kept up to date with automated data discovery and sensitive data classification. In addition to empowering knowledge workers, this understanding, along with insights from sensitivity, business context, and relationships between data assets is also being used by teams working under the Chief Data Officers (CDO), the Chief Information and Security Officers (CIO and CISO) and the Chief Risk and Compliance Officers (CRO and CCO) to govern, protect, and manage data more effectively.

Traditional data management solutions rely on multiple unconnected, duplicative business processes, and a patchwork of software products augmented with custom code and point-wise integrations. Dozens of products are sometimes used together to address fragments of the data governance and compliance landscape, forcing Chief Data, Security, Compliance, and Legal Officers to stitch together solutions that don’t work together, expose infrastructure gaps, and are costly and complex to manage. A survey of US-based decision-makers showed that to meet their compliance and data-protection needs, almost 80 percent had purchased multiple products, and a majority had purchased three or more. The result is increased operations costs, ineffective data governance, poor security outcomes, failed compliance audits, and damage to brand reputation. Additionally, as the threat landscape continues to evolve, the types of risks organizations face inevitably expand and extend well beyond the traditional cybersecurity risks. This means that risk roles within the organization are blurring, requiring a collaborative and cohesive approach across data, compliance, and risk officers, as each drives an integral part of an effective data strategy. We believe the new way to optimize your data strategy is to deliver a unified view of data in the organization across hybrid, multicloud environments by bringing together the business users of data with the protectors of data.

In the past, we have shared how Azure Purview and Microsoft 365 Compliance are used together to ensure consistent, automated application of sensitivity labels to data assets across the data estate to simplify how organizations understand their sensitive data.

Today, we are excited to introduce Microsoft Purview—a comprehensive set of solutions from Microsoft to help you govern, protect, and manage your entire data estate. By bringing together the former Azure Purview and the former Microsoft 365 Compliance portfolio under one brand and over time, a more unified platform, Microsoft Purview can help you understand and govern the data across your estate, safeguard that data wherever it lives, and improve your risk and compliance posture in a much simpler way than traditional solutions on the market today.

Microsoft Purview

◉ Helps you gain visibility into assets across your entire data estate.

◉ Leverages that visibility to manage end-to-end data risks and regulatory compliance.

◉ Governs, protects, and manages data in a new, more comprehensive, and simpler way. 

Customers of the Azure Purview portal can now use the Microsoft Purview governance portal. For customers of Microsoft 365 E5 or Microsoft E5 Compliance, check out the Microsoft Purview compliance portal to see what’s new!

Source: microsoft.com

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Enhance your data visualizations with Azure Managed Grafana—now in preview

Organizations are transforming their digital environments to increase agility and to operate more efficiently. We see this transformation in how customers migrate to the cloud and adopt cloud-native technologies and practices in their own environments. As their digital estates become increasingly more complex and critical to their business operations, it becomes even more important to effectively manage and monitor their applications and infrastructure.   

Grafana is a popular open-source analytics visualization tool that allows users to bring together logs, traces, metrics, and other disparate data from across an organization, regardless of where they are stored. Last year, we announced our strategic partnership with Grafana Labs to develop a Microsoft Azure managed service that lets customers run Grafana natively within the Azure cloud platform. Today, we are announcing that Azure Managed Grafana is available in preview. With Azure Managed Grafana, the Grafana dashboards our customers are familiar with are now integrated seamlessly with the services and security of Azure.

Seamless connection across Azure data sources and beyond

The Grafana application lets users easily visualize all their telemetry data in a single user interface. With Grafana's extensible architecture, users can visualize and correlate multiple data sources across on-premises, Azure, and multi-cloud environments. Azure Managed Grafana particularly optimizes this experience for Azure-native data stores such as Azure Monitor and Data Explorer thus making it easy for customers to connect to any resource in their subscription and view all resulting telemetry in a familiar Grafana dashboard.

Customers can preserve existing charts in the Azure portal that are used for monitoring. Through service-to-service integration, our customers can bring any chart in the Azure portal over to their Azure Managed Grafana instance with a one-click “pin to” operation thus automating the entire migration process. 

Azure Managed Grafana also provides a rich set of built-in dashboards for various Azure Monitor features to help customers easily build new visualizations. For example, some features with built-in dashboards include Azure Monitor application insights, Azure Monitor container insights, Azure Monitor virtual machines insights, and Azure Monitor alerts.

Secured access and sharing of Grafana dashboards with Azure Active Directory

In Azure Managed Grafana, customers can customize user permissions with specific roles and assignments stored in Azure Active Directory. These definitions are mapped transparently to Grafana’s internal roles, which enforces the actual access control. This integration enables both simplicity and consistency by allowing customers to manage users in their teams and authorize their use of a Grafana instance centrally through Azure Active Directory.

On the backend, Azure Managed Grafana can be configured to access Azure Monitor through a managed identity that was set up as part of the Grafana instance creation. Using this option, customers do not need to deal with another credential separately—though that is still possible if preferred.

Source: microsoft.com

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Accelerate silicon design innovation on Azure with Synopsys Cloud

Semiconductor and silicon technology are the basis of digital transformation happening everywhere, across industries and our daily lives, impacting the way we work, learn, and play. The continuous improvement in the performance and power of silicon has been key to enabling this innovation. Here at Microsoft, we’ve empowered our long-standing partners in the semiconductor industry to embrace Azure’s cloud infrastructure and scale out electronic design automation (EDA). With a new EDA-optimized cloud environment running on Azure, the launch of Synopsys Cloud marks a significant milestone for the industry by offering silicon design teams the ability to scale and accelerate their development cycles—transforming chip design the way that the cloud transformed computing.

Increasing flexibility and efficiency in silicon development on Azure

The collective rise in time-to-market pressure caused by the global chip shortage and increasing computational demands have caused chipmakers to seek more flexibility and efficiency in the silicon design process. Migrating chip design to Azure’s optimized infrastructure helps address part of this equation by enabling critical design and verification workloads on the cloud—resulting in faster time-to-results and better quality at a lower cost. With Synopsys Cloud built on Azure, chip designers will now also have access to a new pay-per-use model offering automated provisioning of infrastructure and EDA tools to address the growing demands of silicon design.

This “pay-as-you-go” model is a software as a service (SaaS)-based approach that will reduce barriers for companies of all sizes while enabling greater innovation and value for customers and EDA vendors alike. Using the power of Azure’s workload scaling and virtual machine (VM) selection capabilities, Synopsys Cloud customers will be able to optimize critical EDA workloads—from reducing processing time on verification tasks to saving runtime and enabling faster design convergence on library characterization.

Expanding access to chip design on the cloud

Microsoft has long been committed to helping companies of all sizes unlock more potential on the cloud. With its powerful chip design and verification tools running on Azure’s trusted and comprehensive cloud platform, Synopsys is Microsoft’s preferred partner for EDA on the cloud. Using Synopsys’ solution, customers ranging from startups to large design enterprises benefit from simplified access to custom infrastructure for all their chip design needs—helping them build silicon and tackle designs they previously could not.

Innovation for wide-ranging impact

From intelligent scaling of EDA resources to using AI and machine learning models to transform design and resource management, silicon manufacturing has already seen vast improvements with the introduction of the cloud. The shift towards cloud-centric silicon design has enabled newfound access to compute, storage, and tooling resources. Ultimately, improved time-to-results, quality-of-results, and cost-of-results are just the beginning of what cloud-enabled EDA enhancements can offer. As design on the cloud becomes increasingly widespread, I look forward to seeing the silicon industry continuing to innovate towards new levels of ingenuity—powered by the Microsoft Cloud.

Source: azure.microsoft.com

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Feathr: LinkedIn’s feature store is now available on Azure

Feature store motivation

With the advance of AI and machine learning, companies start to use complex machine learning pipelines in various applications, such as recommendation systems, fraud detection, and more. These complex systems usually require hundreds to thousands of features to support time-sensitive business applications, and the feature pipelines are maintained by different team members across various business groups.

In these machine learning systems, we see many problems that consume lots of energy of machine learning engineers and data scientists, in particular duplicated feature engineering, online-offline skew, and feature serving with low latency.

Figure 1: Illustration on problems that feature store solves.

Duplicated feature engineering

◉ In an organization, thousands of features are buried in different scripts and in different formats; they are not captured, organized, or preserved, and thus cannot be reused and leveraged by teams other than those who generated them.

◉ Because feature engineering is so important for machine learning models and features cannot be shared, data scientists must duplicate their feature engineering efforts across teams.

Online-offline skew

◉ For features, offline training and online inference usually require different data serving pipelines—ensuring consistent features across different environments is expensive.

◉ Teams are deterred from using real-time data for inference due to the difficulty of serving the right data.

◉ Providing a convenient way to ensure data point-in-time correctness is key to avoid label leakage.

Serving features with low latency

◉ For real-time applications, getting feature lookups from database for real-time inference without compromising response latency and with high throughput can be challenging.

◉ Easily accessing features with very low latency is key in many machine learning scenarios, and optimizations needs to be done to combine different REST API calls to features.

To solve those problems, a concept called feature store was developed, so that:

◉ Features are centralized in an organization and can be reused

◉ Features can be served in a synchronous way between offline and online environment

◉ Features can be served in real-time with low latency

Introducing Feathr, a battle-tested feature store

Developing a feature store from scratch takes time, and it takes much more time to make it stable, scalable, and user-friendly. Feathr is the feature store that has been used in production and battle-tested in LinkedIn for over 6 years, serving all the LinkedIn machine learning feature platform with thousands of features in production.

At Microsoft, the LinkedIn team and the Azure team have worked very closely to open source Feathr, make it extensible, and build native integration with Azure. It’s available in this GitHub repository and you can read more about Feathr on the LinkedIn Engineering Blog.

Some of the highlights for Feathr include:

◉ Scalable with built-in optimizations. For example, based on some internal use case, Feathr can process billions of rows and PB scale data with built-in optimizations such as bloom filters and salted joins.

◉ Rich support for point-in-time joins and aggregations: Feathr has high performant built-in operators designed for Feature Store, including time-based aggregation, sliding window joins, look-up features, all with point-in-time correctness.

◉ Highly customizable user-defined functions (UDFs) with native PySpark and Spark SQL support to lower the learning curve for data scientists.

◉ Pythonic APIs to access everything with low learning curve; Integrated with model building so data scientists can be productive from day one.

◉ Rich type system including support for embeddings for advanced machine learning/deep learning scenarios. One of the common use cases is to build embeddings for customer profiles, and those embeddings can be reused across an organization in all the machine learning applications.

◉ Native cloud integration with simplified and scalable architecture, which is illustrated in the next section.

◉ Feature sharing and reuse made easy: Feathr has built-in feature registry so that features can be easily shared across different teams and boost team productivity.

Feathr on Azure architecture

The high-level architecture diagram below articulates how would a user interacts with Feathr on Azure:

Figure 2: Feathr on Azure architecture.

1. A data or machine learning engineer creates features using their preferred tools (like pandas, Azure Machine Learning, Azure Databricks, and more). These features are ingested into offline stores, which can be either:

◉ Azure SQL Database (including serverless), Azure Synapse Dedicated SQL Pool (formerly SQL DW).

◉ Object storage, such as Azure BLOB storage, Azure Data Lake Store, and more. The format can be Parquet, Avro, or Delta Lake.

2. The data or machine learning engineer can persist the feature definitions into a central registry, which is built with Azure Purview.

3. The data or machine learning engineer can join on all the feature dataset in a point-in-time correct way, with Feathr Python SDK and with Spark engines such as Azure Synapse or Databricks.

4. The data or machine learning engineer can materialize features into an online store such as Azure Cache for Redis with Active-Active, enabling multi-primary, multi-write architecture that ensures eventual consistency between clusters.

5. Data scientists or machine learning engineers consume offline features with their favorite machine learning libraries, for example scikit-learn, PyTorch, or TensorFlow to train a model in their favorite machine learning platform such as Azure Machine Learning, then deploy the models in their favorite environment with services such as Azure Machine Learning endpoint.

6. The backend system makes a request to the deployed model, which makes a request to the Azure Cache for Redis to get the online features with Feathr Python SDK.

A sample notebook containing all the above flow is located in the Feathr repository for more reference.

Feathr has native integration with Azure and other cloud services. The table below shows these integrations:

Feathr component	Cloud Integrations
Offline store – Object Store	Azure Blob Storage Azure ADLS Gen2 AWS S3
Offline store – SQL	Azure SQL DB Azure Synapse Dedicated SQL Pools (formerly SQL DW) Azure SQL in VM Snowflake
Online store	Azure Cache for Redis
Feature Registry	Azure Purview
Compute Engine	Azure Synapse Spark Pools Databricks
Machine Learning Platform	Azure Machine Learning Jupyter Notebook
File Format	Parquet ORC Avro Delta Lake

Table 1: Feathr on Azure Integration with Azure Services.

Installation and getting started

Feathr has a pythonic interface to access all Feathr components, including feature definition and cloud interactions, and is open sourced here. The Feathr python client can be easily installed with pip:

pip install -U feathr

Source: microsoft.com

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Diversifying the telecommunications supply chain with Open RAN

Over the past few years, there has been an increasingly steady drumbeat for the need to diversify and open the telecommunications supply chain. This has been driven both by security concerns and by the need to improve the negotiating power of operators by introducing new entrants into the market. A key part of this supply chain that can be diversified is radio access network (RAN), where operators have typically spent most of their investments in network infrastructure.

To address the need for diversification, groups such as the O-RAN alliance have formed to open up RAN capabilities. In addition, select operator communities from all over Europe, the Middle East, Asia, and Africa have begun experimenting in this space. Governments have also been weighing in, designating telecommunications networks as a national priority and a critical part of infrastructure that needs to be secured, and nurtured to drive innovation. An example of this was the UK Government’s 5G diversification strategy—a plan to grow the telecommunications supply chain while simultaneously making it more resilient to future trends and threats.

Microsoft has successfully transformed into an edge and cloud company; so, we understand the magnitude of such an evolution. At Microsoft, our guiding principle is to support, develop, and foster a partner-rich ecosystem. We believe that the role that we play best as a cloud provider is to provide a secure, scalable, well-managed carrier-grade platform serving as the enabler for third parties to build upon.

Future Radio Access Network Challenge (FRANC)

As it turns out, the UK government’s Department of Digital, Culture, Media, and Sports (DCMS) was thinking along the same lines. The Future Radio Access Network Challenge (FRANC) was designed as a follow-on to their diversification strategy. It identified the need to accelerate Open RAN innovation to meet its target of 35 percent of all network traffic over Open RAN by 2030, as well as spark UK-based innovation in this space.

This initiative aligns well with our ambitions—to grow and diversify the supply chain as well as support a healthy and vibrant Open RAN ecosystem. We reached out to Intel and Capgemini, industry leaders in Open RAN, and the University of Edinburgh, a leading academic institution, to join us in demonstrating how beautifully our ideas could fit together to achieve our mutual objectives.

DCMS has endorsed this approach, with the Microsoft-led consortium being one of the award recipients of their challenge. At the Mobile World Congress (MWC) 2022, we pulled back the curtain a bit more to explain what we will be doing jointly as a group, and how our combined efforts will help accelerate the Open RAN ecosystem.

Technology showcased at the Mobile World Congress 2022

At MWC 2022, in close collaboration with our partners, we showed how disaggregated software and hardware are the future of telecommunications networks. This new software-driven programmable network architecture leads to faster rollouts with lower total cost of ownership. Cloud technologies—AI and machine learning analytics, edge computing, large-scale management, self-diagnostics, network programmability, network verification, and global connectivity—can be leveraged to improve highly secure operational efficiency of the virtualized RAN. This infrastructure also supports the creation of new revenue streams through the enablement of a developer ecosystem. 

Additionally, we announced the next wave of Azure for Operators solutions and services, which includes Azure Operator Distributed Services (AODS). AODS combines the enhanced version of AT&T’s Network Cloud software we acquired with the best of Azure, including our industry-leading security, monitoring, analytics, AI, machine learning, and so much more. Capable of handling network-intensive workloads and mission-critical applications, AODS is a carrier-grade platform that provides flexibility and scalability to support deployments at the edge of the cloud, the edge of the network, or the enterprise edge. We’ve been focused on ensuring that this edge infrastructure (both near and far edge) is capable of supporting RAN workloads.

We demonstrated a system that used our AODS solution, which provides hybrid cloud platform for telecommunications network functions. The architectural components included commercial off-the-shelf hardware equipment with Intel’s silicon and Capgemini’s Open RAN network functions. Specifically, Intel® Xeon® Scalable processors, PTP-enabled network interface cards (NICs), and the Intel vRAN Accelerator ACC100 Adapter, all leveraged by the FlexRAN™ layer 1 software. Capgemini provided the vCU and vDU Open RAN network functions, and Microsoft provided the cloud-managed platform through AODS.

Figure 1: Hardware setup of the live demonstration of carrier-grade cloud-managed Open RAN platform at MWC 2022. 

The setup included four commercial off-the-shelf servers connected to a top-of-rack (ToR) and management switch. A radio unit (RU), capable of 4x4 multiple input, multiple output (MIMO) over a 100 MHz channel, was connected to the ToR using 7.2 times front-haul interface. A grandmaster clock was also connected to the ToR switch providing PTP synchronization to the RU and the servers. A 5G smartphone device was used to connect to the network. We showed how with AODS we can connect all these together and deploy a virtualized RAN from a cloud with a few mouse clicks and manage it remotely. Such a seamless deployment process reduces the integration efforts that have to be done by vendors, allowing them to focus on innovation instead.

Looking ahead

Along with our partners, Microsoft is bringing to life carrier-grade edge-cloud solutions that empower operators globally to deploy Open RAN network functions easily and securely. Our tools and services can manage RAN deployments at scale. With Azure machine learning and AI, a core component of our technologies, operators can perform analytics that optimize performance, improve management, and proactively detect and solve problems.

Security principles designed for the cloud are being adopted to make the platform resilient, to prevent, detect, and respond to threats in the network and across the firmware and telecommunications supply chains. Edge and network monitoring and programmability via open API’s will enable a new generation of 5G applications while simultaneously improving operational efficiency. Operators can increase revenues and reduce infrastructure costs while building future-proof solutions.

Source: microsoft.com

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