Kategorie: Microsoft Azure

Azure Digital Twins: Powering the next generation of IoT connected solutions

Last month at Microsoft Build 2020, we announced the new features for Azure Digital Twins, the IoT platform that enables the creation of next-generation IoT connected solutions that model the real world. Today, we are announcing that these updated capabilities are now available in preview. Using the power of IoT, businesses have gained unprecedented insights into their assets. But as connected solutions continue to evolve, companies are looking for ways to create richer models of entire business environments, which is quite challenging even for sophisticated businesses.

Our goal with Azure Digital Twins is to make the creation of sophisticated digital twin solutions easy. With today’s announcement, you can apply your domain expertise on top of Azure Digital Twins to design and build comprehensive digital models of entire environments.

Using Azure Digital Twins, you can gain insights that drive better products, optimization of operations, cost reduction, and breakthrough customer experiences. And you can now do so across environments of all types, including buildings, factories, farms, energy networks, railways, stadiums—even entire cities.

What’s new?

We received a lot of valuable feedback from the Azure Digital Twins preview and we are excited to share the expanded capabilities of Azure Digital Twins that will simplify and accelerate your creation of IoT connected solutions.

Open modeling language

The new preview of Azure Digital Twins lets you create custom models of any connected environment. The new preview of Azure Digital Twins lets you create custom models of any connected environment. Using the rich and flexible Digital Twins Definition Language (DTDL), based on the JSON-LD standard, you can configure your Azure Digital Twins service to tailor to the specific needs of your use case.

Real-world environments are created from connected twins. Each twin is modeled using properties, telemetry events, components, and relationships that define how twins can be connected into rich knowledge graphs. DTDL is also used for models throughout other Azure IoT services, including IoT Plug and Play and Azure Time Series Insights.

DTDL is the glue that helps you keep your Azure Digital Twins solution connected and compatible with other parts of the Azure ecosystem.

As part of our commitment to openness and interoperability, we will continue to promote best practices and shared digital twin models for a wide range of businesses and industry domains through the Digital Twins Consortium and other channels to accelerate your time building valuable IoT connected solutions that span many industry verticals and use cases.

Live execution environment

Azure Digital Twins lets you bring your digital twins to life using data from IoT and other data sources, creating an always-up-to-date digital representation of your environment that is scalable and secure.

Using a robust event system, you can build dynamic business logic and data processing as data flows through the execution environment, and now, you can harness the power of external compute resources, such as Azure Functions. This makes it easy to use pre-existing code with Azure Digital Twins, which provides freedom of choice in terms of programming languages and compute models.

To extract insights from the live execution environment, Azure Digital Twins provides a powerful query system that allows you to search for twins based on a wide range of conditions and relationships.

Input from IoT and business systems

You can easily connect assets such as IoT and IoT Edge devices, as well as existing business systems such as ERP and CRM, to Azure Digital Twins to drive the live execution environment.

You can now use a new or existing Azure IoT Hub to connect, monitor, and manage all of your assets at scale, taking advantage of the full device management capabilities that IoT Hub provides. The ability to use any existing Hub makes it easier to add Azure Digital Twins to existing IoT solutions incrementally.

Using the Azure Digital Twins REST APIs, you can also use data sources other than IoT, unlocking even more actionable insights with Azure Digital Twins.

Output to Azure Time Series Insight, storage, and analytics

You can integrate Azure Digital Twins with other Azure services to build complete end-to-end solutions. You can define event routes that send selected data to downstream services through endpoints that support Event Hubs, Event Grid, or Service Bus Event routes to send data to Azure Data Lake for long term storage; to data analytics services such as Azure Synapse Analytics to apply machine learning; to Logic Apps for workflow integration; or to Power BI to extract insights. Another important use case is time series data integration and historian analytics with Azure Time Series Insight.

Combined, these capabilities greatly simplify today’s difficult tasks of modeling and creating a digital representation of an environment, helping you focus on what differentiates your business rather than building and operating complex, distributed systems architecture securely and at scale.

Innovating with customers and partners

Azure Digital Twins is already being used by a broad set of customers and partners. Below are a few examples showcasing the applicability of Azure Digital Twins across a wide range of industries:

Ansys Twin Builder: physics-based digital twins

Physics-based simulation has long been an essential part of the product design process, helping engineers to optimize and validate design choices. With the broad deployment of IoT sensors in products and their environment, it is now possible to apply the same simulation technology after a product has been built, shipped and deployed in the field.  Simulation technology can be used to optimize performance and energy usage or predict failures in a highly accurate and immediate way, without the complexities associated with alternative techniques.

“Ansys Twin Builder lets engineers quickly deliver real-time simulation models for operational use. With Microsoft’s Azure Digital Twins platform, it is now possible to efficiently integrate the simulation-based twins into a broader IoT solution.” —Sameer Kher, Senior Director,Twin Builder Product Line for Ansys 

Bentley iTwin: infrastructure digital twins

In the world of infrastructure development, complex CAD data is the backbone of planning, execution and operation of major infrastructures, such as road and rail networks, public works and utilities, industrial plants, and commercial and institutional facilities. Bentley’s iTwin platform captures geometry and metadata of the project and its environment as the source of truth that drives daily decisions throughout the entire lifecycle of the project. As a developer, you can think of it as GitHub for CAD.

“Using Azure Digital Twins, we can bring this backbone to life using raw and processed information from IoT sensors distributed throughout the infrastructure. By bringing a wide range of information sources together into a comprehensive Digital Twin, including CAD data, real-world scans and photometry, IoT sensor data, weather feeds and many more, we can revolutionize the way infrastructure projects are planned, built and operated.” —Pavan Emani, Vice President, iTwin software development for Bentley 

To learn more about the customers and partners using Azure Digital Twins in exciting ways, we encourage you to visit the customer stories covering a spectrum of industry use cases.
  

Get started

We look forward to continuing to deliver on our commitment of simplifying and accelerating your time to value building next-generation IoT connected solutions. We are excited about the role Azure Digital Twins will play helping you gain valuable insights across your environments.

Watch this video to learn more:

 

Get started with Azure Digital Twins today.

Visit the Azure Digital Twins product page.

See the Azure Digital Twins documentation and quick start guides.

Watch the Deep Dive: Azure Digital Twins webinar for technical walkthrough. Join event at 9 AM PT June 29, 2020 for Live Q & A.

Watch the Deep Dive: Bentley and Azure Digital Twins webinar for architectural overview. Join event at 9 AM PT August 3, 2020 for Live Q & A.

Watch how Bentley uses Azure Digital Twins to build Bentley iTwin solution.

Watch the Azure Digital Twins Microsoft Build event session.

Read our customer stories from Ansys and Bentley.

Read Announcing Azure Digital Twins: Create digital replicas of spaces and infrastructure using cloud, AI and IoT to get familiar with the previous release.

Quelle: Azure

Advancing Azure service quality with artificial intelligence: AIOps

“In the era of big data, insights collected from cloud services running at the scale of Azure quickly exceed the attention span of humans. It’s critical to identify the right steps to maintain the highest possible quality of service based on the large volume of data collected. In applying this to Azure, we envision infusing AI into our cloud platform and DevOps process, becoming AIOps, to enable the Azure platform to become more self-adaptive, resilient, and efficient. AIOps will also support our engineers to take the right actions more effectively and in a timely manner to continue improving service quality and delighting our customers and partners. This post continues our Advancing Reliability series highlighting initiatives underway to keep improving the reliability of the Azure platform. The post that follows was written by Jian Zhang, our Program Manager overseeing these efforts, as she shares our vision for AIOps, and highlights areas of this AI infusion that are already a reality as part of our end-to-end cloud service management.”—Mark Russinovich, CTO, Azure

This post includes contributions from Principal Data Scientist Manager Yingnong Dang and Partner Group Software Engineering Manager Murali Chintalapati.

 

As Mark mentioned when he launched this Advancing Reliability blog series, building and operating a global cloud infrastructure at the scale of Azure is a complex task with hundreds of ever-evolving service components, spanning more than 160 datacenters and across more than 60 regions. To rise to this challenge, we have created an AIOps team to collaborate broadly across Azure engineering teams and partnered with Microsoft Research to develop AI solutions to make cloud service management more efficient and more reliable than ever before. We are going to share our vision on the importance of infusing AI into our cloud platform and DevOps process. Gartner referred to something similar as AIOps (pronounced “AI Ops”) and this has become the common term that we use internally, albeit with a larger scope. Today’s post is just the start, as we intend to provide regular updates to share our adoption stories of using AI technologies to support how we build and operate Azure at scale.

Why AIOps?

There are two unique characteristics of cloud services:

The ever-increasing scale and complexity of the cloud platform and systems
The ever-changing needs of customers, partners, and their workloads

To build and operate reliable cloud services during this constant state of flux, and to do so as efficiently and effectively as possible, our cloud engineers (including thousands of Azure developers, operations engineers, customer support engineers, and program managers) heavily rely on data to make decisions and take actions. Furthermore, many of these decisions and actions need to be executed automatically as an integral part of our cloud services or our DevOps processes. Streamlining the path from data to decisions to actions involves identifying patterns in the data, reasoning, and making predictions based on historical data, then recommending or even taking actions based on the insights derived from all that underlying data.

 
Figure 1. Infusing AI into cloud platform and DevOps.

The AIOps vision

AIOps has started to transform the cloud business by improving service quality and customer experience at scale while boosting engineers’ productivity with intelligent tools, driving continuous cost optimization, and ultimately improving the reliability, performance, and efficiency of the platform itself. When we invest in advancing AIOps and related technologies, we see this ultimately provides value in several ways:

Higher service quality and efficiency: Cloud services will have built-in capabilities of self-monitoring, self-adapting, and self-healing, all with minimal human intervention. Platform-level automation powered by such intelligence will improve service quality (including reliability, and availability, and performance), and service efficiency to deliver the best possible customer experience.
Higher DevOps productivity: With the automation power of AI and ML, engineers are released from the toil of investigating repeated issues, manually operating and supporting their services, and can instead focus on solving new problems, building new functionality, and work that more directly impacts the customer and partner experience. In practice, AIOps empowers developers and engineers with insights to avoid looking at raw data, thereby improving engineer productivity.
Higher customer satisfaction: AIOps solutions play a critical role in enabling customers to use, maintain, and troubleshoot their workloads on top of our cloud services as easily as possible. We endeavor to use AIOps to understand customer needs better, in some cases to identify potential pain points and proactively reach out as needed. Data-driven insights into customer workload behavior could flag when Microsoft or the customer needs to take action to prevent issues or apply workarounds. Ultimately, the goal is to improve satisfaction by quickly identifying, mitigating, and fixing issues.

My colleagues Marcus Fontoura, Murali Chintalapati, and Yingnong Dang shared Microsoft’s vision, investments, and sample achievements in this space during the keynote AI for Cloud–Toward Intelligent Cloud Platforms and AIOps at the AAAI-20 Workshop on Cloud Intelligence in conjunction with the 34th AAAI Conference on Artificial Intelligence. The vision was created by a Microsoft AIOps committee across cloud service product groups including Azure, Microsoft 365, Bing, and LinkedIn, as well as Microsoft Research (MSR). In the keynote, we shared a few key areas in which AIOps can be transformative for building and operating cloud systems, as shown in the chart below.
 

Figure 2. AI for Cloud: AIOps and AI-Serving Platform.

AIOps

Moving beyond our vision, we wanted to start by briefly summarizing our general methodology for building AIOps solutions. A solution in this space always starts with data—measurements of systems, customers, and processes—as the key of any AIOps solution is distilling insights about system behavior, customer behaviors, and DevOps artifacts and processes. The insights could include identifying a problem that is happening now (detect), why it’s happening (diagnose), what will happen in the future (predict), and how to improve (optimize, adjust, and mitigate). Such insights should always be associated with business metrics—customer satisfaction, system quality, and DevOps productivity—and drive actions in line with prioritization determined by the business impact. The actions will also be fed back into the system and process. This feedback could be fully automated (infused into the system) or with humans in the loop (infused into the DevOps process). This overall methodology guided us to build AIOps solutions in three pillars.

Figure 3. AIOps methodologies: Data, insights, and actions.

AI for systems

Today, we're introducing several AIOps solutions that are already in use and supporting Azure behind the scenes. The goal is to automate system management to reduce human intervention. As a result, this helps to reduce operational costs, improve system efficiency, and increase customer satisfaction. These solutions have already contributed significantly to the Azure platform availability improvements, especially for Azure IaaS virtual machines (VMs). AIOps solutions contributed in several ways including protecting customers’ workload from host failures through hardware failure prediction and proactive actions like live migration and Project Tardigrade and pre-provisioning VMs to shorten VM creation time.

Of course, engineering improvements and ongoing system innovation also play important roles in the continuous improvement of platform reliability.

Hardware Failure Prediction is to protect cloud customers from interruptions caused by hardware failures. We shared our story of Improving Azure Virtual Machine resiliency with predictive ML and live migration back in 2018. Microsoft Research and Azure have built a disk failure prediction solution for Azure Compute, triggering the live migration of customer VMs from predicted-to-fail nodes to healthy nodes. We also expanded the prediction to other types of hardware issues including memory and networking router failures. This enables us to perform predictive maintenance for better availability.
Pre-Provisioning Service in Azure brings VM deployment reliability and latency benefits by creating pre-provisioned VMs. Pre-provisioned VMs are pre-created and partially configured VMs ahead of customer requests for VMs. As we described in the IJCAI 2020 publication, As we described in the AAAI-20 keynote mentioned above,  the Pre-Provisioning Service leverages a prediction engine to predict VM configurations and the number of VMs per configuration to pre-create. This prediction engine applies dynamic models that are trained based on historical and current deployment behaviors and predicts future deployments. Pre-Provisioning Service uses this prediction to create and manage VM pools per VM configuration. Pre-Provisioning Service resizes the pool of VMs by destroying or adding VMs as prescribed by the latest predictions. Once a VM matching the customer's request is identified, the VM is assigned from the pre-created pool to the customer’s subscription.

AI for DevOps

AI can boost engineering productivity and help in shipping high-quality services with speed. Below are a few examples of AI for DevOps solutions.

Incident management is an important aspect of cloud service management—identifying and mitigating rare but inevitable platform outages. A typical incident management procedure consists of multiple stages including detection, engagement, and mitigation stages. Time spent in each stage is used as a Key Performance Indicator (KPI) to measure and drive rapid issue resolution. KPIs include time to detect (TTD), time to engage (TTE), and time to mitigate (TTM).

 
Figure 4. Incident management procedures.

As shared in AIOps Innovations in Incident Management for Cloud Services at the AAAI-20 conference, we have developed AI-based solutions that enable engineers not only to detect issues early but also to identify the right team(s) to engage and therefore mitigate as quickly as possible. Tight integration into the platform enables end-to-end touchless mitigation for some scenarios, which considerably reduces customer impact and therefore improves the overall customer experience.

Anomaly Detection provides an end-to-end monitoring and anomaly detection solution for Azure IaaS. The detection solution targets a broad spectrum of anomaly patterns that includes not only generic patterns defined by thresholds, but also patterns which are typically more difficult to detect such as leaking patterns (for example, memory leaks) and emerging patterns (not a spike, but increasing with fluctuations over a longer term). Insights generated by the anomaly detection solutions are injected into the existing Azure DevOps platform and processes, for example, alerting through the telemetry platform, incident management platform, and, in some cases, triggering automated communications to impacted customers. This helps us detect issues as early as possible.

For an example that has already made its way into a customer-facing feature, Dynamic Threshold is an ML-based anomaly detection model. It is a feature of Azure Monitor used through the Azure portal or through the ARM API. Dynamic Threshold allows users to tune their detection sensitivity, including specifying how many violation points will trigger a monitoring alert.

Safe Deployment serves as an intelligent global “watchdog” for the safe rollout of Azure infrastructure components. We built a system, code name Gandalf, that analyzes temporal and spatial correlation to capture latent issues that happened hours or even days after the rollout. This helps to identify suspicious rollouts (during a sea of ongoing rollouts), which is common for Azure scenarios, and helps prevent the issue propagating and therefore prevents impact to additional customers. We provided details on our safe deployment practices in this earlier blog post and went into more detail about how Gandalf works in our USENIX NSDI 2020 paper and slide deck.

AI for customers

To improve the Azure customer experience, we have been developing AI solutions to power the full lifecycle of customer management. For example, a decision support system has been developed to guide customers towards the best selection of support resources by leveraging the customer’s service selection and verbatim summary of the problem experienced. This helps shorten the time it takes to get customers and partners the right guidance and support that they need.

AI-serving platform

To achieve greater efficiencies in managing a global-scale cloud, we have been investing in building systems that support using AI to optimize cloud resource usage and therefore the customer experience. One example is Resource Central (RC), an AI-serving platform for Azure that we described in Communications of the ACM. It collects telemetry from Azure containers and servers, learns from their prior behaviors, and, when requested, produces predictions of their future behaviors. We are already using RC to predict many characteristics of Azure Compute workloads accurately, including resource procurement and allocation, all of which helps to improve system performance and efficiency.

Looking towards the future

We have shared our vision of AI infusion into the Azure platform and our DevOps processes and highlighted several solutions that are already in use to improve service quality across a range of areas. Look to us to share more details of our internal AI and ML solutions for even more intelligent cloud management in the future. We’re confident that these are the right investment solutions to improve our effectiveness and efficiency as a cloud provider, including improving the reliability and performance of the Azure platform itself.
Quelle: Azure

Five reasons to view this Azure Synapse Analytics virtual event

The virtual event Azure Synapse Analytics: How It Works is now available on demand. In demos and technical discussions, Microsoft customers explain how they’re using the newest Azure Synapse Analytics capabilities to deliver insights faster, bring together an entire analytics ecosystem in a central location, reduce costs, and transform decision-making.

This post outlines five key reasons to view the one-hour event.

Learn how to deliver powerful insights with speed and ease

Today, it’s critical to have a data-driven culture in your organization. Analytics play a pivotal role in helping many organizations make insights-driven decisions—decisions to transform supply chains, develop new ways to interact with customers, and evaluate new offerings.

At Azure Synapse Analytics: How It Works, customers showed how they combine data ingestion, data warehousing, and big data analytics in a single cloud-native service using Azure Synapse. If you’re a data engineer trying to wrangle multiple data types from multiple sources to create pipelines or a database administrator with responsibilities over your data lake and data warehouse, you’ll see how all this can be simplified in a code-free environment.

Customers also demonstrated how they give their employees access to unprecedented, real-time insights from enterprise data using Azure Synapse with built-in Power BI authoring.

Achieve unprecedented ROI

Companies featured at the event have demonstrated significant cost reductions with cloud analytics solutions. Compared to on-premises solutions, these solutions:

Require lower implementation and maintenance costs.
Reduce analytics project development time.
Provide access to more frequent innovation.
Deliver higher levels of security and business continuity.
Help ensure a better competitive advantage and higher customer satisfaction.

With cloud analytics, organizations pay for data and analytics tools only when needed, pausing consumption when not in use. They can reallocate budget previously spent on hardware and infrastructure management to optimizing processes and launching new projects. In fact, customers average a 271 percent ROI with Azure Synapse—savings that come from lower operating costs, increased productivity, reallocating staff to higher-value activities, and increasing operating income due to improved analytics. Analytics in Azure is up to 14 times faster and costs 94 percent less than other cloud providers.

Deliver a unified analytics experience to everyone in your organization

BI specialists, data engineers, and other IT and data professionals are using Azure Synapse to build, manage, and optimize analytics pipelines, using a variety of skillsets.

Data engineers can use a code-free visual environment for managing data pipelines.
Database administrators can automate query optimization and easily explore data lakes.
Data scientists can build proofs of concept in minutes.
Business analysts can securely access datasets and use Power BI to build dashboards in minutes—all while using the same analytics service.

Analyze data at limitless scale

By viewing the event, you’ll learn how to access and analyze all your data, from your enterprise data lake to multiple data warehouses and big data analytics systems, with blazing speed. Join us to see how data professionals can query both relational and non-relational data using the familiar SQL language, using either serverless or provisioned resources—with Azure Synapse.

Attain unmatched security

Of course, trust is critical for any cloud solution. Customers will share how they take advantage of advanced Azure Synapse security and privacy features such as automated threat detection and always-on data encryption to help ensure that data stays safe and private by using column-level security and native row-level security. You’ll also learn about dynamic data masking, which automatically protects sensitive data in real time.

In summary, by viewing the Azure Synapse Analytics: How It Works virtual event, you’ll learn how to deliver:

Powerful insights.
Unprecedented ROI.
Unified experience.
Limitless scale.
Unmatched security.

Quelle: Azure

Introducing Azure Load Balancer insights using Azure Monitor for Networks

We are excited to announce that Azure Load Balancer customers now have instant access to a packaged solution for health monitoring and configuration analysis. Built as part of Azure Monitor for Networks, customers now have topological maps for all their Load Balancer configurations and health dashboards for their Standard Load Balancers preconfigured with relevant metrics.

Through this, you have a window into the health and configuration of your networks, enabling rapid fault localization and informed design decisions. You can access this through the Insights blade of each Load Balancer resource and Azure Monitor for Networks, a central hub that provides access to health and connectivity monitoring for all your network resources.

Visualize functional dependencies

The functional dependency view will enable you to picture even the most complex load balancer setups. With visual feedback on Load Balancing rules, Inbound NAT rules, and backend pool resources, you can make updates while keeping a complete picture of your configuration in mind.

For Standard Load Balancers, your backend pool resources are color-coded with Health Probe status empowering you to visualize the current availability of your network to serve traffic. Alongside the above topology you are presented with a time-wise graph of health status, giving a snapshot view of the health of your application.

Monitor a rich metric dashboard with no setup needed

After reviewing your topology, you may want to dig even further into the data to understand how your Load Balancer is performing through the detailed metrics page. The detailed metrics page is a dashboard preconfigured with separate tabs providing useful insights into Availability, Data Throughput, Flow Distribution, and Connection Latency.

The Overview tab provides a high-level view and from here you can visit the Frontend & Backend Availability or Data Throughput tabs for more in-depth information.

Through the Frontend and Backend availability tabs, you are provided with a breakdown of your Load Balancer and backend pool health status over time. You can consult the Data Throughput tab to learn how much data is parsed through your services by frontend IP, frontend port, and direction.

The Flow Distribution tab provides visualization of load distribution amongst backend resources. This enables you to see the number of flows being created by each backend instance and to keep track of whether you are approaching the limit.

The Connection Monitors tab plots round-trip latencies from Connection Monitors across the globe on a map. With this, you can evaluate the performance impact distances from regions around the world have on your service.

The new monitoring experience is seamless and straightforward to use, with integrated guides and instructions provided as part of each tab.

One place for all your network monitoring needs

Azure Monitor for Networks fully supports the new monitoring and insights experience for Azure Load Balancer. With all your network resource metrics in a single place, you can quickly filter by type, subscription, and keyword to view the health, connectivity, and alert status of all your Azure network resources such as Azure Firewalls, ExpressRoute, and Application Gateways.

As we rapidly transition to the cloud and applications become more complex, customers need tools to easily maintain, monitor, and update their network configurations. With the integration of the Azure Load Balancer with Azure Monitor for Networks, we deliver a piece of this and look forward to continuing to provide our valued customers with the best in class experience they deserve.

Next steps

Learn more about the Azure Load Balancer, Azure Monitor for Networks, and Network Watcher.
Deploy your first Load Balancer, customize your metrics, and create a Connection Monitor.
Give us feedback on this and new features you want to see.

Quelle: Azure

Azure Support API: Create and manage Azure support tickets programmatically

Large enterprise customers running business-critical workloads on Azure manage thousands of subscriptions and use automation for deployment and management of their Azure resources. Expert support for these customers is critical in achieving success and operational health of their business. Today, customers can keep running their Azure solutions smoothly with self-help resources, such as diagnosing and solving problems in the Azure portal, and by creating support tickets to work directly with technical support engineers.

We have heard feedback from our customers and partners that automating support procedures is key to help them move faster in the cloud and focus on their core business. Integrating internal monitoring applications and websites with Azure support tickets has been one of their top asks. Customers expect to create, view, and manage support tickets without having to sign-in to the Azure portal. This gives them the flexibility to associate the issues they are tracking with the support tickets they raise with Microsoft. The ability to programmatically raise and manage support tickets when an issue occurs is a critical step for them in Azure usability.

We’re happy to share that the Azure Support API is now generally available. With this API, customers can integrate the creation and management of support tickets directly into their IT service management (ITSM) system, and automate common procedures.

Using the Azure Support API, you can:

Create a support ticket for technical, billing, subscription management, and subscription and service limits (quota) issues.
Get a list of support tickets with detailed information, and filter by status or created date.
Update severity, status, and contact information.
Manage all communications for a support ticket.

Benefits of Azure Support API

Reduce the time between finding an issue and getting support from Microsoft

A typical troubleshooting process when the customer encounters an Azure issue looks something like this:

On step five, if the issue is unresolved and identified to be on the Azure side, customers navigate to the Azure portal, to contact support. With programmatic case management access, customers can automate their support process with their internal tooling to create and manage their support tickets, thus reducing the time between finding an issue and contacting support.

Customers now have one end-end process that goes smoothly from internal to external without the person filing the issue having to deal with the complexity and challenges between separate case management systems.

Create support tickets via ARM templates

Deploying an ARM template that creates resources can sometimes result in a ResourceQuotaExceeded deployment error, indicating that you have exceeded your Azure subscription and service limits (quotas). This happens because quotas are applied in the resource group, subscription, account, and other scopes. For example, your subscription may be configured to limit the number of cores for a region. If you attempt to deploy a virtual machine with more cores than the permitted amount, you receive an error stating the quota has been exceeded. The way to resolve it is to request a quota increase by filing a support ticket. With Support APIs in place, you can avoid signing in to the Azure portal to create a ticket, instead request quota increases directly via ARM templates.

Getting started

The Azure Support API is available with a Professional Direct, Premier, or Unified technical support plan.

For detailed examples using .NET and C#, refer to our code samples.

View the list of all languages and interfaces we support for ticket creation and management. As always, you can also directly use the Support REST API.

Use the API and tell us about it

We are looking forward to hearing your feedback about the Azure Support API. In the Azure support feedback forum, you can post ideas and suggestions for the API and other aspects of the support experience.

To report an API issue, go to the issues section of the GitHub repository for the language or interface you're using. For example, go to the repository for issues with the PowerShell cmdlets. Select New issue and tag it with the labels Support and Service Attention.
Quelle: Azure

Deploy to Azure Container Instances with Docker Desktop

This blog was co-authored by MacKenzie Olson, Program Manager, Azure Container Instances. 

Today we’re excited about the first release of the new Docker Desktop integration with Microsoft Azure. Last month Microsoft and Docker announced this collaboration, and today you can experience it for yourself.

The new edge release of Docker Desktop provides an integration between Docker and Microsoft Azure that enables you to use native Docker commands to run your applications as serverless containers with Azure Container Instances.

You can use the Docker CLI to quickly and easily sign into Azure, create a Container Instances context using an Azure subscription and resource group, then run your single-container applications on Container Instances using docker run. You can also deploy multi-container applications to Container Instances that are defined in a Docker Compose file using docker compose up.

Code-to-Cloud with a serverless containers

Azure Container Instances is a great solution for running a single Docker container or an application comprised of multiple containers defined with a Docker Compose file. With Container Instances, you can run your containers in the cloud without needing to set up any infrastructure and take advantage of features such as mounting Azure Storage and GitHub repositories as volumes. Because there is no infrastructure or platform management overhead, Container Instances caters to those who need to quickly run containers in the cloud.

Container Instances is also a good target to run the same workloads in production. In production cases, we recommend leveraging Docker commands inside of an automated CI/CD flow. This saves time having to rewrite configuration files because the same Dockerfile and Docker Compose files can be deployed to production with tools such as GitHub Actions. Container Instances also has a pay-as-you-go pricing model, which means you will only be billed for CPU and memory consumption per second, only when the container is running.

Let’s look at the new Docker Azure integration using an example. We have a worker container that continually pulls orders off a queue and performs necessary order processing. Here are the steps to run this in Container Instances with native Docker commands:

Run a single container

As you can see from the above animation, the new Docker CLI integration with Azure makes it easy to get a container running in Azure Container Instances. Using only the Docker CLI you can log in to Azure with multi-factor authentication and create a Docker context using Container Instances as the backend. Detailed information on Container Instances contexts can be found in the documentation.

Once the new Container Instances context is created it can be used to target Container Instances with many of the standard Docker commands you likely already use; like docker run, docker ps, and docker rm. Running a simple docker run <image> command will start a container in Container Instances using the image that is stored in a registry like Docker Hub or Azure Container Registry. You can run other common Docker commands to inspect, attach-to, and view logs from the running container.

Use Docker Compose to deploy a multi-container app

We see many containerized applications that consist of a few related containers. Sidecar containers often perform logging or signing services for the main container. With the new Docker Azure integration, you can use Docker Compose to describe these multi-container applications.

You can use a Container Instances context and a Docker Compose file as part of your edit-build-debug inner loop, as well as your CI/CD flows. This enables you to use docker compose up and down commands to spin up or shut down multiple containers at once in Container Instances.

Visual Studio Code for an even better experience

The Visual Studio Code Docker extension provides you with an integrated experience to start, stop, and manage your containers, images, contexts, and more. Use the extension to scaffold Dockerfiles and Docker Compose files for any language. For Node.js, Python, and .NET, you get integrated, one-click debugging of your app inside the container. And then of course there is the Explorer, which has multiple panels that make the management of your Docker objects easy from right inside Visual Studio Code.

Use the Containers panel to list, start, stop, inspect, view logs, and more.

 

From the Images panel you can list, pull, tag, and push your images.

 
Connect to Azure Container Registry and Docker Hub in the Registries panel to view and manage your images in the cloud. You can even deploy straight to Azure.

 

The Contexts panel lets you list all your contexts and quickly switch between them. When you switch context, the other panels will refresh to show the Docker objects from the selected context. Container Instances contexts will be fully supported in the next release of the docker extension.

Try it out

To start using the Docker Azure integration install the Docker Desktop edge release. You can leverage the current Visual Studio Code Docker extension today, Container Instances context support will be added very soon.

To learn more about the Docker Desktop release, you can read this blog post from Docker. You can find more information in the documentation for using Docker Container Instances contexts.
Quelle: Azure

The next frontier in machine learning: driving responsible practices

Organizations around the world are gearing up for a future powered by artificial intelligence (AI). From supply chain systems to genomics, and from predictive maintenance to autonomous systems, every aspect of the transformation is making use of AI. This raises a very important question: How are we making sure that the AI systems and models show the right ethical behavior and deliver results that can be explained and backed with data?

This week at Spark + AI Summit, we talked about Microsoft’s commitment to the advancement of AI and machine learning driven by principles that put people first.

Understand, protect, and control your machine learning solution

Over the past several years, machine learning has moved out of research labs and into the mainstream and has grown from a niche discipline for data scientists with PhDs to one where all developers are empowered to participate. With power comes responsibility. As the audience for machine learning expands, practitioners are increasingly asked to build AI systems that are easy to explain and that comply with privacy regulations.

To navigate these hurdles, we at Microsoft, in collaboration with the Aether Committee and its working groups, have made available our responsible machine learning (responsible ML) innovations that help developers understand, protect and control their models throughout the machine learning lifecycle. These capabilities can be accessed in any Python-based environment and have been open sourced on GitHub to invite community contributions.

 
Understanding the model behavior includes being able to explain and remove any unfairness within the models. The interpretability and fairness assessment capabilities powered by the InterpretML and Fairlearn toolkits, respectively, enable this understanding. These toolkits help determine model behavior, mitigate any unfairness, and improve transparency within the models.

Protecting the data used to create models by ensuring data privacy and confidentiality, is another important aspect of responsible ML. We’ve released a differential privacy toolkit, developed in collaboration with researchers at the Harvard Institute for Quantitative Social Science and School of Engineering. The toolkit applies statistical noise to the data while maintaining an information budget. This ensures an individual’s privacy while enabling the machine learning process to run unharmed.

Controlling models and its metadata with features, like audit trails and datasheets, brings the responsible ML capabilities full circle. In Azure Machine Learning, auditing capabilities track all actions throughout the lifecycle of a machine learning model. For compliance reasons, organizations can leverage this audit trail to trace how and why a model’s predictions showed certain behavior.

Many customers, such as EY and Scandinavian Airlines, use these capabilities today to build ethical, compliant, transparent, and trustworthy solutions while improving their customer experiences.

Our continued commitment to open source

In addition to open sourcing our responsible ML toolkits, there are two more projects we are sharing with the community. The first is Hyperspace, a new extensible indexing subsystem for Apache Spark. This is designed to work as a simple add-on, and comes with Scala, Python, and .Net support. Hyperspace is the same technology that powers the indexing engine inside Azure Synapse Analytics. In benchmarking against common workloads like TPC-H and TPC-DS, Hyperspace has provided gains of 2x and 1.8x, respectively. Hyperspace is now on GitHub. We look forward to seeing new ideas and contributions on Hyperspace to make Apache Spark’s performance even better.

The second is a preview of ONNX Runtime's support for accelerated training. The latest release of training acceleration incorporates innovations from the AI at Scale initiative, such as ZeRO optimization and Project Parasail, which improves memory utilization and parallelism on GPUs.

We deeply value our partnership with the open source community and look forward to collaborating to establish responsible ML practices in the industry.

Additional resources

Learn more about responsible ML.
Walk through an interactive demo for responsible ML.
Read the IDC white paper on responsible AI.
Use the Azure architecture center for proven architectures on analytics and AI.

 

Quelle: Azure

Azure.com operates on Azure part 1: Design principles and best practices

Azure puts powerful cloud computing tools into the hands of creative people around the world. So, when your website is the face of that brand, you better use what you build, and it better be good. As in, 99.99-percent composite SLA good.

That’s our job at Azure.com, the platform where Microsoft hopes to inspire people to invent the next great thing. Azure.com serves up content to millions of people every day. It reaches people in nearly every country and is localized in 27 languages. It does all this while running on the very tools it promotes.

In developing Azure.com, we practice what we preach. We follow the guiding principles that we advise our customers to adopt and the principles of sustainable software engineering (SSE). Even this blog post is hosted on the very infrastructure that it describes.

In part one of our two-part series, we will peek behind the Azure.com web page to show you how we think about running a major brand website on a global scale. We will share our design approach and best practices for security, resiliency, scalability, availability, environmental sustainability, and cost-effective operations—on a global scale.

Products, features, and demos supported on Azure.com

As a content platform, Azure.com serves an audience of business and technical people—from S&P 500 enterprises to independent software vendors, and from government agencies to small businesses. To make sure our content reaches everyone, we follow Web Content Accessibility Guidelines (WCAG). We also adopted sustainable software engineering principles to help us responsibly achieve global scale and reduce our carbon footprint.

Azure.com supports static content, such as product and feature descriptions. But the fun is in the interactive components that let readers customize the details, like the products available by region page where we show service availability across 61 regions (and growing), the Azure updates page that keeps people informed about Azure changes, and the search box.

The Azure pricing page provides up-to-date pricing information for more than 200 services across multiple markets, and it factors in any discounts for which a signed-in user is eligible. We also built a comprehensive pricing calculator for all services. Prospective customers can calculate and share complex cost estimates in 24 currencies.

As a marketing channel, Azure.com also hosts demos. For example, we created in-browser interactive demos to display the benefits of Azure Cognitive Services, and we support streaming media for storytelling. We also provided a total cost of ownership (TCO) calculator for estimating cloud migration savings in 27 languages and 12 regions.

And did we mention the 99.99-percent composite SLA that Azure.com meets?

Pricing calculator: Interactive cost estimation tool for all Azure products and services.

History of Azure.com

As the number of Azure services has grown, so has our website, and it has always run on Azure. Azure.com is always a work in progress, but here are a few milestones in our development history:

2013: Azure.com begins life on the popular open-source Umbraco CMS. It markets seven Azure services divided into four categories: compute, data services, app services, and network.
2015: Azure.com moves to a custom ASP.NET Model View Controller (MVC) application hosted on Azure. It now supports 16 Azure services across four categories.
2020: Azure.com continues to expand its support of more categories of content. Today, the website describes more than 200 Azure offerings, including Azure services, capabilities, and features.

 

Azure.com timeline: Every year we support more great Azure products and services.

Design principles behind Azure.com

To create a solid architectural foundation for Azure.com, we follow the core pillars of great Azure architecture. These pillars are the design principles behind the security, performance, availability, and efficiency that make Azure.com run smoothly and meet our business goals.

Design principles: Azure.com follows the tenets of Azure architectural best practices.

You can take a class on how to Build great solutions with the Microsoft Azure Well-Architected Framework.

A pillar of security and resiliency

Like any cloud application, Azure.com requires security at all layers. That means everything covered by the Open Systems Interconnection (OSI) model, from the network to the application, web page, and backend dependencies. This is our defense-in-depth approach to security.

Resiliency is the ability to defend against malicious attacks, bad actors, or bots saturating your compute resources and possibly causing unnecessary scale-out and cost overruns. Resiliency isn’t about avoiding failure, but rather responding to failure in a way that avoids downtime and data loss.

One metric for resiliency is the recovery time objective (RTO), which says how long an application can be offline after suffering an outage. For us, it’s less than 30 minutes. Failure mode analysis (FMA) is another assessment of resiliency and includes planning for failures and running live fire drills. We use both these methods to assess the resiliency of Azure.com.

Super scalable and highly available

Any cloud application needs enough scalability to handle peak loads. For Azure.com, peaks occur during major events and marketing campaigns. Regardless of the load, Azure.com requires high availability to support around-the-clock operations. We trust the platform to support business continuity and guard against unexpected outages, overloaded resources, or failures caused by upstream dependencies.

As a case in point, we rely on Azure scalability to handle the big spikes in demand during Microsoft Build and Microsoft Ignite, the largest annual events handled by Azure.com. The number of requests per second (RPS) jumps 20 to 30 percent as tens of thousands of event attendees flock to Azure.com to learn about newly announced Azure products and services.

Whatever the scale, the Azure platform provides reliable, sustainable operations that enable Microsoft and other companies to deliver premium content to our customers.

Cost-effective high performance is a core design principle

Our customers often tell us that they want to move to a cloud-based system to save money. It’s no different at Azure.com, where cost-efficient provisioning is a core design principle. Azure.com has a handy cost calculator to compare the cost of running on-premises to running on Azure.

Efficiency means having a way to track and optimize underutilized resources and use dynamic scaling to support seasonal traffic demands. This principle applies to all layers of the software development life cycle (SDLC), starting with managing all the work items, using a source code repository, and implementing continuous integration (CI) and continuous deployment (CD). Cost-efficiency extends to the way we provision and host resources in multiple environments, and maintain an inventory of our digital estate.

But being cost-conscious doesn’t mean giving up on speed. Top-notch performance takes minimal network latency, fast server response times, and consistent page load and render times. Azure.com performance always focuses on the user experience, so we make sure to optimize network routing and minimize round-trip time (RTT).

Operating with zero downtime

Uptime is important for any large web application. We aim for zero downtime. That means no service downtime—ever. It’s a lofty goal, but it’s possible when you use CI/CD practices that spare users from the effects of the build and deployment cycles.

For example, if we push a code update, we aim for no site downtime, no failed requests, and no adverse impact on Azure.com users. Our CI/CD pipeline is based on Azure DevOps and pumps out hundreds of builds and multiple deployments to the live production servers every day without a hitch.

Another service level indicator (SLI) that we use is mean time to repair (MTTR). With this metric, lower is better. To minimize MTTR SLI, you need DevOps tools for identifying and repairing bottlenecks or crashing processes.

Next steps

From our experience working on Azure.com, we can say that following these design principles and best practices improves application resiliency, lowers costs, boosts security, and ensures scalability.

To review the workings of your Azure architecture, consider taking the architecture assessment.

For more information about the Azure services that make up Azure.com, see the next article in this blog series, How Azure.com operates on Azure part 2: Technology and architecture.
Quelle: Azure

How Azure.com operates on Azure part 2: Technology and architecture

When you’re the company that builds the cloud platforms used by millions of people, your own cloud content needs be served up fast. Azure.com—a complex, cloud-based application that serves millions of people every day—is built entirely from Azure components and runs on Azure.

Microsoft culture has always been about using our own tools to run our business. Azure.com serves as an example of the convenient platform-as-a-service (PaaS) option that Azure provides for agile web development. We trust Azure to run Azure.com with 99.99-percent availability across a global network capable of a round-trip time (RTT) of less than 100 milliseconds per request.

In part two of our two-part series we share our blueprint, so you can learn from our experience building a website on planetary scale and move forward with your own website transformation.

This post will help you get a technical perspective on the infrastructure and resources that make up Azure.com. For details about our design principles, read Azure.com operates on Azure part 1: Design principles and best practices.

The architecture of a global footprint

With Azure.com, our goal is to run a world-class website in a cost-effective manner at planetary scale. To do this, we currently run more than 25 Azure services. (See Services in Azure.com below.)

This blog examines the role of the main services, such as Azure Front Door, which routes HTTP requests to the web front end, and Azure App Service, a fully managed platform for creating and deploying cloud applications.

The following diagram shows you a high-level view of the global Azure.com architecture.

On the left, networking services provide the secure endpoints and connectivity that give users instant access, no matter where they are in the world.
On the right, developers use Azure DevOps services to run a continuous integration (CI) and continuous deployment (CD) pipeline that delivers updates and features with zero downtime.
In between, a variety of PaaS options that provide compute,  storage, security, monitoring, and more.

Azure.com global architecture: A high-level look at the Azure services and dataflow.

Host globally, deliver regionally

The Azure.com architecture is hosted globally but runs locally in multiple regions for high availability. Azure App Service hosts Azure.com from the nearest global datacenter infrastructure, and its automatic scaling features ensure that Azure.com meets changing demands.

The diagram below shows a close-up of the regional architecture hosted in App Service. We use deployment slots to deploy to development, staging, and production environments. Deployment slots are live apps with their own host names. We can swap content and configurations between the slots while maintaining application availability.

Azure.com regional architecture: App Service hosts regional instances in slots.

A look at the key PaaS components behind Azure.com

Azure.com is a complex, multi-tier web application. We use PaaS options as much as possible because managed services save us time. Less time spent on infrastructure and operations means more time to create a world-class customer experience. The platform performs OS patching, capacity provisioning, and load balancing, so we’re free to focus elsewhere.

Azure DNS

Azure DNS enables self-service quick edits to DNS records, global nameservers with 100-percent availability, and blazing fast DNS response times via Anycast addressing. We use Azure DNS aliases for both CNAME and ANAME record types.

Azure Front Door Service

Azure Front Door Service enables low-latency TCP-splitting, HTTP/2 multiplexing and concurrency, and performance based global routing. We saw a reduction in RTT to less than 100 milliseconds per request, as clients only need to connect to edge nodes, not directly to the origin.

For business continuity, Azure Front Door Service supports backend health probes, a resiliency pattern, that in effect removes unhealthy regions when they are misbehaving. In addition, to enable a backup site, Azure.com uses priority-based traffic routing. In the event our primary service backend goes offline, this method enables Azure Front Door Service to support ringed failovers.

Azure Front Door Service also acts as a reverse proxy, enabling pattern-based URL rewriting or request forwarding to handle dynamic traffic changes.

Web Application Firewall

Web Application Firewall (WAF) helps improve the platform’s security posture by providing load shedding bad bots and protection against OWASP top 10 attacks at the application layer. WAF forces developers to pay more attention to their data payloads, such as cookies, request URLs, form post parameters, and request headers.

We use WAF custom rules to block traffic to certain geographies, IPs, URLs, and other request properties. Rules offload traffic at the network edge from reaching your origin.

Content Delivery Network

To reduce load times, Azure.com uses Content Delivery Network (CDN) for load shedding to origin. CDN helps us lower the consumed bandwidth and keep costs down. CDN also improves performance by caching static assets at the Point of Presence (POP) edge nodes and reducing RTT latency. Without CDN, our origin nodes would have to handle every request for static assets.

CDN also supports DDoS protection, improving app security. We enable CDN compression and HTTP/2 to optimize delivery for static payloads. Using CDN is also a sustainable approach to optimizing network traffic because it reduces the data movement across a network.

Azure App Service

We use App Service horizontal autoscaling to handle burst traffic. The Autoscale feature is simple to use and is based on Azure Monitor metrics for requests per second (RPS) per node. We also reduced our Azure expenses by 50 percent by using elastic compute—a benefit that directly reduces our carbon consumption.

Azure.com uses several other handy App Service features:

Always On means there’s no idle timeout.
Application initialization provides custom warmup and validation.
VIP swap blue-green deployment pattern supports zero-downtime deployments.
To reduce network latency to the edge, we run our app in 12 geographically separate datacenters. This practice supports geo-redundancy should one or more datacenters go dark.
To improve app performance, we use the App Service DaaS – .NET profiler. This feature identifies node bottlenecks and hotspots for weak performing code blocks or slow dependencies.
For disaster recovery and improved mean time to recovery (MTTR), we use slot swap. In the event that an app deployment exception is not caught by our PPE testing, we can quickly roll back to last stable version.

App Service is also a PaaS service, which means we don't have to worry about the virtual machine (VM) infrastructure, OS updates, app frameworks, and the downtime associated with managing these. We follow the paired region concept when choosing our datacenters to mitigate against any rolling infrastructure updates and ensure improved isolation and resiliency.

As a final note, it’s important to choose the right App Service plan tier so that you can right-size your vertical scaling. The plan you choose also affects sustainable energy proportionality, which means running instances at a higher utilization rate to maximize carbon efficiency.

DaaS – .NET Profiler: identifying code bottlenecks and measuring improvements. In this case we found our HTML whitespace “minifier” was saturating our compute nodes. After disabling it, we verified response times, and CPU usage improved significantly.

Azure Monitor

Azure Monitor enables passive health monitoring over Application Insights, Log Analytics, and Azure Data Explorer data sources. We rely on these query monitor alerts to build configuration-based health models based on our telemetry logs so we know when our app is misbehaving before our customers tell us.

For example, we monitor CPU consumption by datacenter as the following screenshot shows. If we see sustained, high CPU usage for our app metrics, Monitor can trigger a notification to our response team, who can quickly respond, triage the problem, and help improve MTTR. We also receive proactive notifications if a client-browser is misbehaving or throwing console errors, such as when Safari changes a specific push and replace state pattern.

Performance counters: We are alerted if CPU spikes are sustained for more than five minutes.

Application Insights

Application Insights, a feature of Monitor, is used for client- and server-side Application Performance Management (APM) telemetry logging. It monitors page performance, exceptions, slow dependencies, and offers cross-platform profiling. Customers typically use Application Insights in break-fix scenarios to improve MTTR and to quickly triage failed requests and application exceptions.

We recommend enabling telemetry sampling so you don’t exhaust your data volume storage quota. We set up daily storage quota alerts to capture any telemetry saturation before it shuts off our logging pipeline.

Application Insights also provides OpenTelemetry support for distributed tracing across app domain boundaries and dependencies. This feature enables traceability from the client side all the way to the backend data or service tier.

Data volume capacity alert: Example showing that the data storage threshold is exceeded, which is useful for tracking runaway telemetry logs.

Developing with Azure DevOps

A big team works on Azure.com, and we use Azure DevOps Services to coordinate our efforts. We create internal technical docs with Azure Wikis, track work items using Azure Boards, build CI/CD workflows using Azure Pipelines, and manage application packages using Azure Artifacts. For software configuration management and quality gates, we use GitHub, which works well with Azure Boards.

We submit hundreds of daily pull requests as part of our build process, and the CI/CD pipeline deploys multiple updates every day to the production site. Having a single tool to manage the entire software development life cycle (SDLC) simplifies the learning curve for the engineering team and our internal customers.

To stay on top of what’s coming, we do a lot of planning in Delivery Plans. It’s a great tool for viewing incremental tasks and creating forecasts for the major events that affect Azure.com traffic, such as Microsoft Build, Microsoft Ignite, and Microsoft Ready.

What’s next

As the Azure platform evolves, so does Azure.com. But some things stay the same—the need for a reliable, scalable, sustainable, and cost-effective platform. That’s why we trust Azure.

Microsoft offers many resources and best practices for cloud developers, please see our additional resources below. To get started, create your Azure free account today.

Services in Azure.com

For more information about the services that make up Azure.com, check out the following resources.

Compute

Azure App Service
Azure Functions
Azure Cognitive Services

Networking

Azure Front Door
Azure DNS
Web Application Firewall
Azure Traffic Manager
Azure Content Delivery Network

Storage

Azure Cognitive Search
Azure Cache for Redis
Azure Blob storage and Azure queues
Application Insights
Azure Cosmos DB
Azure Data Explorer
Azure Media Services

Access provisioning

Azure Active Directory
Microsoft Graph
Azure Key Vault

Application life cycle

Azure DevOps
Azure Log Analytics
Azure Monitor
Azure Security Center
Azure Resource Manager
Azure Cost Management
Azure Service Health
Azure Advisor

Quelle: Azure