Amazon EC2 AMD-Instances sind jetzt in der AWS-Region Asien-Pazifik (Mumbai) verfügbar

Amazon EC2 M5a-, M5ad-, R5a-, R5ad- und T3a-Instances sind ab heute in der AWS-Region Asien-Pazifik (Mumbai) verfügbar. Sowohl die M5a- als auch R5a-Instances wurden erstmals im November 2018 eingeführt. Die M5ad- und R5ad-Instances wurden im März 2019 eingeführt, gefolgt von T3a im April 2019. Alle AMD-Instances bieten 2,5 GHz-Prozessoren der Serie AMD EPYC 7000 und sind Varianten der Amazon EC2-Instance-Familien für Universalzwecke (M5), speicheroptimierten Instances (R5) und burst-baren Instances (T3).  
Quelle: aws.amazon.com

Improving observability of your Kubernetes deployments with Azure Monitor for containers

Over the past few years, we’ve seen significant changes in how an application is thought of and developed, especially with the adoption of containers and the move from traditional monolithic applications to microservices applications. This shift also affects how we think about modern application monitoring, now with greater adoption of open source technologies and the introduction of observability concepts.

In the past, vendors owned the application and infrastructure, and as a result, they knew what metrics to monitor. With open source products growing in number, vendors do not own all the metrics, and custom metrics are extremely necessary with current monitoring tools. Unlike the monolith application, which is a single deployment unit with a simple status of healthy or not, modern applications will consist of dozens of different microservices with fractional n-states. This is due to the sophisticated deployment strategies and rollbacks where customers may be running different versions of the same services in production, especially on Kubernetes. Thus, embracing these shifts is essential in monitoring.

Custom metrics and open source technologies help improve the observability of specific components of your application, but you also need to monitor the full stack. Azure Monitor for containers embraces both observability through live data and collecting custom metrics using Prometheus, providing the full stack end-to-end monitoring from nodes to Kubernetes infrastructure to workloads.

Collecting Prometheus metrics and viewing using Grafana dashboards

By instrumenting Prometheus SDK into your workloads, Azure Monitor for containers can scrape the metrics exposed from Prometheus end-points so you can quickly gather failure rates, response per secs, and latency. You can use Prometheus to collect some of the Kubernetes infrastructure metrics that are not provided out of the box by Azure Monitor by configuring the containerized agent.

From Log Analytics, you can easily run a Kusto Query Language (KQL) query and create your custom dashboard in the Azure portal dashboard. For many customers using Grafana to support their dashboard requirements, you can visualize the container and Prometheus metrics in a Grafana dashboard.

Below is an example of a dashboard that provides an end-to-end Azure Kubernetes Service (AKS) cluster overview, node performances, Kubernetes infrastructure, and workloads.
  

If you would like to monitor or troubleshoot other scenarios, such as list of all workload live sites, or noisy neighbor issues on a worker node, you can always switch to Azure Monitor for container to view the visualizations included from the Grafana dashboard by clicking on Azure Monitor – Container Insights in the top right-hand corner.

  
Azure Monitor for containers provides the live, real-time data of container logs and Kubernetes event logs to provide observability as seen above. You can see your deployments immediately and observe any anomalies using the live data.

If you are interested in trying Azure Monitor for containers, please check the documentation. Once you have enabled the monitoring, and if you would like to try the Grafana template, please go to the Grafana gallery. This template will light up using the out-of-the-box data collected from Azure Monitor for containers. If you want to add more charts to view other metrics collected, you can do so by checking our documentation.

Prometheus data collection and Grafana are also supported for AKS Engine as well.

For any feedback or suggestions, please reach out to us through Azure Community Support or Stack Overflow.
Quelle: Azure

Save more on Azure usage—Announcing reservations for six more services

With reserved capacity, you get significant discounts over your on-demand costs by committing to long-term usage of a service. We are pleased to share reserved capacity offerings for the following additional services. With the addition of these services, we now support reservations for 16 services, giving you more options to save and get better cost predictability across more workloads.

Blob Storage (GPv2) and Azure Data Lake Storage (Gen2).
Azure Database for MySQL.
Azure Database for PostgreSQL.
Azure Database for MariaDB.
Azure Data Explorer.
Premium SSD Managed Disks.

Blob Storage (GPv2) and Azure Data Lake Storage (Gen2)

Save up to 38 percent on your Azure data storage costs by pre-purchasing reserved capacity for one or three years. Reserved capacity can be pre-purchased in increments of 100 TB and 1 PB sizes, and is available for hot, cool, and archive storage tiers for all applicable storage redundancies. You can also use the upfront or monthly payment option, depending on your cash flow requirements.

The reservation discount will automatically apply to data stored on Azure Blob (GPv2) and Azure Data Lake Storage (Gen2). Discounts are applied hourly on the total data stored in that hour. Unused reserved capacity doesn’t carry over.

Storage reservations are flexible, which means you can exchange or cancel your reservation should your storage requirements change in the future (limits apply).

Purchase reserved capacity from Azure portal, or read the documentation.

Azure Database for MySQL, PostgreSQL, and MariaDB

Save up to 51 percent on your Azure Database costs for MySQL, PostgreSQL, and MariaDB by pre-purchasing reserved capacity. Reservation discount applies to the compute usage for these products and is available for both general-purpose and memory-optimized deployments. You can choose to pay monthly for the reservations.

As with all reservations, reservation discounts will automatically apply to the matching database deployments, so you don't need to do make any changes to your resources to get reservation discounts. The discount applies hourly on the compute usage. Unused reserved hours don't carry over.

You can exchange your reservations to move from general-purpose to memory-optimized, or vice-versa, any time after purchase. You can also cancel the reservation to receive a prorated amount back (limits apply).

Purchase reserved capacity from Azure portal, or read the documentation.

Azure Data Explorer Markup reserved capacity

Save up to 30 percent on your Azure Data Explorer Markup costs with reserved capacity. The reservation discount only applies on the markup meter, other charges, including compute and storage, are billed separately. You can also purchase reservations for virtual machines (VM) and storage to save even more on your total cost of ownership for Azure Data Explorer (Kusto) clusters. You can choose to pay monthly for the Azure Data Explorer markup reservations.

After purchase, the reservation discount will automatically apply to the matching cluster. The discount applies hourly on the markup usage. Unused reserved hours don't carry over. As usual, you can exchange or cancel the reservation should your needs change (limits apply).

Purchase reserved capacity from Azure portal, or read the documentation.

Premium SSD Managed Disks

Save up to 5 percent on your Premium SSD Managed Disk usage with reserved capacity. Discounts are applied hourly on the disks deployed in that hour regardless of whether the disks are attached to a VM. Unused reserved hours don't carry over. Reservation discount does not apply to Premium SSD Unmanaged Disks or Page Blobs consumption.

Disk reservations are flexible, which means you can exchange or cancel your reservation should your storage requirements change in the future (limits apply).

Purchase reserved capacity from Azure portal, or read the documentation.
Quelle: Azure

Docker’s Next Chapter: Advancing Developer Workflows for Modern Apps

Today we start the next chapter in the Docker story, one that’s focused on developers. That we have the opportunity to write this next chapter is thanks to you, our community, for without you we wouldn’t be here. And while our focus on developers builds on recent history, it’s a focus also grounded in Docker’s beginning.
In The Beginning
When Solomon Hykes, Docker’s founder, unveiled the Docker project in 2013, he succinctly stated the problem Docker aimed to solve as, “for a developer, shipping code to the server is hard.” To address, Docker abstracted out OS kernels’ complex container primitives, provided a developer-friendly, CLI-based workflow and defined an immutable, portable image format. The result transformed how developers work, making it much easier to build, ship and run their apps on any server. So while container primitives had existed for decades, Docker democratized them and made them as easy to use as
docker run hello-world
The rest is history. Over the last six years, Docker containerization catalyzed the growth of microservices-based applications, enabled development teams to ship apps many times faster and accelerated the migration of apps from the data center to the cloud. Far from a Docker-only effort, a vibrant community ecosystem of open source and commercial technologies arose which streamlined adoption. Not the least of these is Kubernetes, orchestration technology originating from Google, which enabled highly reliable container deployments at new levels of scale. Also during this time, Docker containers expanded from their Linux x86 beginnings to run on other OSes and architectures, including Microsoft Windows and Arm. To guide the community’s efforts, new governance organizations appeared including CNCF, OCI, and CNAB. As this market grew, our developer community rapidly embraced Docker Desktop and Docker Hub, growing to millions of active users who shared millions of containerized apps and pulled billions of app images.
All this and more in only six years – and the best is yet to come.
The Road Ahead
Going forward, Docker’s focus is to build on these foundations to advance developer workflows for modern apps. Along with real benefits, the last six years also resulted in additional complexities, an explosion of choices and new potential threats of lock-in. In light of these challenges, Docker and our community ecosystem have the opportunity to extend the open standards, functionality, automation tooling and cloud services of Docker Desktop and Docker Hub to better help developers build, share and run modern apps.
Build. Six years ago, most apps could be encapsulated using one or two containers; today, a cloud-native microservices-based app may be a composition of many containers as well as serverless functions and hosted cloud services. To address this growing complexity and help developers simplify defining, building and packaging these apps, Docker will continue to expand the functionality of our open source frameworks and developer productivity tools like Docker Compose, Docker Apps and Docker App Templates.
Share. In 2013, friction-free, language-independent shareable application content was extremely limited. Now, thanks to the modularization of functionality enabled by Docker, the meteoric rise of the Docker container as the de facto industry standard and community distribution venues like Docker Hub, developers can now augment the code they write themselves with shared open source and commercial containers. However, more publishers daily pushing more apps – over 5 million on Docker Hub alone – and the emergence of new packaging formats run the risk of overwhelming and slowing developers. Docker Desktop and Docker Hub can help them quickly find new technologies relevant to their applications.
Run. Early on, container infrastructure was not widely available to developers. In order to run Docker containerized apps on servers developers, had to ask their IT teams to install Docker Engines and, for scale, orchestrators like Kubernetes or Docker Swarm. And this container infrastructure had to be monitored, patched and managed.
Fast-forward to today, where cloud service providers offer Docker-compatible on-demand container infrastructure services for both individual containers, like AWS Fargate, as well as multi-container apps, like Microsoft AKS. These give developers speed and agility, but at the potential risk of lock-in. Thus, to make it even easier for developers to benefit from the speed of these services but without giving up app portability and infrastructure choice, Docker Hub will seamlessly integrate developers’ “build” and “share” workflows with the cloud “run” services of their choosing.
Stay Tuned
In Docker’s early days we shared a vision of making things easier for developers. The growth in the open source and commercial community around Docker and Kubernetes these last six years suggests that we’re onto something ;-). But we’re just getting started, and there’s plenty more to be done. With this future ahead of us, it’s a privilege to lead the outstanding Docker team for this next chapter of the Docker story. And it’s one we look forward to writing together with you, our developer community.
The post Docker’s Next Chapter: Advancing Developer Workflows for Modern Apps appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/

GitHub Actions for Azure is now generally available

GitHub Actions make it possible to create simple yet powerful workflows to automate software compilation and delivery integrated with GitHub. These actions, defined in YAML files, allow you to trigger an automated workflow process on any GitHub event, such as code commits, creation of Pull Requests or new GitHub Releases, and more.

As GitHub just announced the public availability of their Actions feature today, we’re announcing that the GitHub Actions for Azure are now generally available.

You can find all the GitHub Actions for Azure and their repositories listed on GitHub with documentation and sample templates to help you easily create workflows to build, test, package, release and deploy to Azure, following a push or pull request.

You can also use Azure starter templates to easily create GitHub CI/CD workflows targeting Azure to deploy your apps created with popular languages and frameworks including .NET, Node.js, Java, PHP, Ruby, or Python, in containers or running on any operating system.

Connect to Azure

Authenticate your Azure subscription using the Azure login (azure/login) action and a service principal. You can then run Azure CLI scripts to create and manage any Azure resource using the Azure CLI (azure/cli) action, which sets up the GitHub Action runner environment with the latest (or any user-specified) version of the Azure CLI.

Deploy a Web app

Azure App Service is a managed platform for deploying and scaling web applications. You can easily deploy your web app to Azure App Service with the Azure WebApp (azure/webapps-deploy)and Azure Web App for Containers (azure/webapps-container-deploy) actions. You could also configure App settings and Connection Strings using the Azure App Service Settings (azure/appservice-settings) action.

Learn more about Azure App Service.

Deploy a serverless Function app

Streamline the deployment of your serverless applications to Azure Functions, an event-driven serverless compute platform, by bringing either your code using the Azure Functions action (azure/functions-action) or your custom container image using the Azure Functions for containers action (azure/functions-container-action) .

Learn more about Azure Functions.

Build and Deploy containerized Apps

For containerized apps (single- or multi-containers) use the Docker Login action (azure/docker-login) to create a complete workflow to build container images, push to a container registry (Docker Hub or Azure Container Registry), and then deploy the images to an Azure web app, Azure Function for Containers, or to Kubernetes.

Deploy to Kubernetes

We have released multiple actions and to help you connect to a Kubernetes cluster running on-premises or on any cloud (including Azure Kubernetes Service), bake and deploy manifests, substitute artifacts, check rollout status, and handle secrets within the cluster.

Kubectl tool installer (azure/setup-kubectl): Installs a specific version of kubectl on the runner.
Kubernetes set context (azure/k8s-set-context): Used for setting the target Kubernetes cluster context which will be used by other actions or run any kubectl commands.
AKS set context (azure/aks-set-context): Used for setting the target Azure Kubernetes Service cluster context.
Kubernetes create secret (azure/k8s-create-secret): Create a generic secret or docker-registry secret in the Kubernetes cluster.
Kubernetes deploy (azure/k8s-deploy): Use this to deploy manifests to Kubernetes clusters.
Setup Helm (azure/setup-helm): Install a specific version of Helm binary on the runner.
Kubernetes bake (azure/k8s-bake): Use this action to bake manifest file to be used for deployments using Helm 2, kustomize, or Kompose.

To deploy to a cluster on Azure Kubernetes Service (AKS), you could use azure/aks-set-context to communicate with the AKS cluster, and then use azure/k8s-create-secret to create a pull image secret and finally use the azure/k8s-deploy to deploy the manifest files.

Deploy to Azure SQL or MySQL databases

We now have an action for Azure SQL Databases (azure/sql-action) that uses a connection string for authentication and DACPAC/SQL scripts to deploy to your Azure SQL Database.

If you would like to deploy to an Azure Database for MySQL database using MySQL scripts, use the MySQL action (azure/mysql-action) instead.

Trigger a run in Azure Pipelines

GitHub Actions make it easy to build, test, and deploy your code right from GitHub, but you can also use it to trigger external CI/CD tools and services, including Azure Pipelines. If your workflow requires an Azure Pipelines run for deployment to a specific Azure Pipelines environment, as an example, the Azure Pipelines (azure/pipelines) action will enable you to trigger this run as part of your Actions workflow.

Utility Actions

Finally, we also released an action for variable substitution Microsoft/variable-substitution, which enables you to parameterize the values in JSON, XML, or YAML files (including configuration files, manifests, and more) within a GitHub Action workflow.

More coming soon

We will continue improving upon our available set of GitHub Actions, and will release new ones to cover more Azure services.

Please try out the GitHub Actions for Azure and share your feedback via Twitter on @Azure. If you encounter a problem, please open an issue on the GitHub repository for the specific action.
Quelle: Azure

Announcing Network Intelligence Center—towards proactive network operations

We are very excited to introduce Network Intelligence Center, Google Cloud’s comprehensive network monitoring, verification, and optimization platform across the cloud and on-prem data centers, along with an initial set of modules. Organizations are increasingly adopting multi-cloud implementations and hybrid deployments as a part of their cloud strategy, to deliver the best experiences for their customers around the globe. Networks are at the foundation of this digital transformation, and becoming increasingly complex due to the growing heterogeneity in the customer environment. This puts pressure on network operations teams, who live under the presumption that “the network is guilty until proven innocent.” Unfortunately, today’s fragmented networking tools don’t make their lives any easier, leading to a lack of visibility, long troubleshooting times to resolve connectivity and performance issues, configuration errors being discovered too late in production, and no centralized way to holistically understand and remedy network health.*1, *2 & **3The vision for intelligent and predictive network operationsAdoption of hybrid and multi-cloud is absolutely critical for organizations to remain agile. However, this underscores the need for intelligent and continuous network operations—the promise that the network is doing what it needs to do, in line with business intent. For example, if you have global operations, you may want to ensure that users in a specific geographical region are being served out of the nearest datacenter at all times. To realize this vision, you need proactive network operations that can predict and heal network failures, driven by AI/ML recommendations and remediation. These systems also need to maintain an equilibrium between automation and control, to support customers who are at different points in their journey from reactive towards proactive network operations. In the first phase of fulfilling this vision for intelligent network operations, Network Intelligence Center offers four modules: Connectivity Tests and Network Topology, both in beta; Performance Dashboard and Firewall Metrics & Insights in alpha; with several other modules to follow. Network Topology: Visualize complex networks with insights Google Cloud Platform (GCP) is the first cloud provider to provide comprehensive visibility into your global GCP deployment (not just your single VPC) and its interaction with the public internet, including an organization-wide view of the topology, and associated network performance metrics with Network Topology.Visibility is especially important in cloud to gain trust and confidence since customers do not own the infrastructure. Visualizing cloud topologies is also complicated by the fact that a cloud network is really a mesh, making it hard to understand and navigate the differences between on-premises and cloud environments. Network Topology lets you visualize and navigate your network with structure and insights, simplifying network monitoring and troubleshooting, and helping  you fulfill your compliance requirements.Further, Network Topology provides useful performance metrics for various nodes and edges, enabling metrics-driven troubleshooting, policy checks, and architecture and capacity optimization. For instance, you can easily visualize how your users are being served worldwide and if they are being served optimally out of their nearest geographical regions. You can quickly search through complex networks and drill down into exactly what matters to you, shrinking troubleshooting time. In the event of a network issue, you can go back in time up to six weeks to track the evolution of the network’s topology, to diagnose issues quickly. “Network Intelligence Center has been transformational for us to optimize our network operations. Using Network Intelligence Center we discovered that data transferred to a particular GCE region was significantly higher than expected. By looking at Network Topology we were able to diagnose and fix this issue, and save significant costs,” said Rob Lyon, Enterprise Architect at Kochava, a mobile app analytics company. “Network Intelligence Center helps us quickly identify and troubleshoot network problems. We’ve gained full situational awareness of our network topology, saving us time on monitoring network health,” said Jason Cradit, Senior Director of Technology at Pivvot, a real-time location data and analytics company. Visualize your network with insights: See metrics for nodes and edges. Egress traffic breakdown from one region (europe-west1 here) to other regions can be used for optimizationConnectivity Tests: Diagnose connectivity issues and predict the impact of configuration changes GCP is the first cloud provider to provide a solution based on formal verification techniques that empowers you to diagnose connectivity issues, and proactively verify the impact of configuration changes to prevent outages.75% of network outages and performance issues result from misconfiguration. More often than not, these misconfigurations are discovered in production. Not knowing the impact of making a configuration change in firewall rules or routing rules makes network monitoring reactive rather than proactive, introducing risk and extending mean time to resolution. Network Intelligence Center helps to quickly diagnose connectivity issues and prevent outages by allowing teams to perform on-demand tests with the Connectivity Test module. Connectivity Test enables you to self-diagnose connectivity issues within GCP or GCP to an external IP address which could be on-prem or another cloud, helping to isolate whether the issue is in GCP or not. You can create, save and run tests to help verify the impact of configuration changes and ensure that network intent captured by these tests is not violated, proactively preventing network outages. These tests also help assure network security and compliance. Connectivity Test has been used internally by Google Cloud’s support team to resolve customer issues. “We’re excited that with the help of Network Intelligence Center, we’ll be able to verify that our network connectivity matches intent and quickly troubleshoot network configuration issues,” said David Breise, Cloud & Network Engineer at HIPAAVault, a secure HIPAA-compliant file-sharing provider.Create & save connectivity tests that reflect your intent. Any time you make configuration change, run them to verify  impact.Speed up cloud migrationsIn addition to monitoring, verification, and optimization of the network between on-premises and cloud, Network Intelligence Center can also help accelerate your migration to cloud. With a holistic view into network architecture, health and performance before and after migration, it allows you to plan your cloud migrations efficiently and optimize your architecture. It also helps to identify and debug network issues between on-prem and cloud, and can help you easily track topology changes during migration, as well as view actual traffic flows and performance metrics before and  during the migration. Additional modules: Performance Dashboard and Firewall Metrics & InsightsNetwork Intelligence Center includes two additional modules in alpha: Performance Dashboard and Firewall Metrics & Insights. Performance Dashboard provides real-time network performance visibility into packet loss and latency at a per-project level, a compelling capability for network health monitoring at a project level that reflects a user’s network experience, not offered by any other cloud provider today. These metrics provide insights into your network performance, showing VM-to-VM packet loss and latency data aggregated across zones. In the case of an application performance issue, Performance Dashboard helps the network team quickly determine whether the issue is with the network or with the application. Fill out this form to sign up as an alpha customer for Performance Dashboard. Firewall Metrics & Insights provides visibility into firewall rules usage, helping you optimize firewall rules by revealing insights into shadowed firewall rules. You can access this capability today from the firewall rules page in alpha, and it will be integrated into Network Intelligence Center when it reaches beta. Fill out this form to sign up as an alpha customer for Firewall Metrics & Insights. Moving from reactive to proactive network operationsAs you build more and more applications on a Google Cloud foundation, you need a network operations solution that lets you easily visualize complex, cloud-based topologies, helps you proactively identify and troubleshoot problems, and assists you in making good decisions when running at global scale. We are excited to see you use Network Intelligence Center to adopt a more proactive approach to monitoring network health and to predict, diagnose and verify your network quickly. Stay tuned for new Networking Intelligence Center modules as well as blogs that dive deep into Connectivity Test and Network Topology. Meanwhile, give Connectivity Test and Network Topology a try in the Google Cloud Console. Also, visit our website to learn more about networking and take a tour of Cloud City. And as always, reach out to the Google Cloud Networking team with your questions and feedback.*1 & 2. https://www.channelpartnersonline.com/2019/02/27/multicloud-hybrid-cloud-adoption-growing/**3. https://blog.ipswitch.com/best-practices-in-network-configuration-and-change-management
Quelle: Google Cloud Platform