Microsoft Azure News, Entwicklungen, Updates, HowTos - Seite 215 von 259

This post was authored by the Azure Stack Team.

Today, we are excited to announce preview releases of Azure PaaS services for Azure Stack and a refresh to Azure Stack TP3. Last month, we released Azure Stack TP3 and provided additional information about hybrid use cases, the pay-as-you-use business model for Azure Stack, and roadmap updates. If you haven’t already, read Jeffery Snover’s Azure Stack TP3 blog post for more context. Additionally, we put together a whitepaper with an even more detailed roadmap of Azure services, integrated systems details, and initial geo-availability.

This update continues delivering Azure Services on premises so customers can create innovative applications for the hybrid cloud. Specifically, this release includes:

Azure App Service (Web apps, API apps, and Mobile apps)
Azure Functions
Updated versions of SQL/MySQL database services

New to App Service this release:

Azure Functions preview for AAD based deployments
Deployment in disconnected environments
Deployment on ADFS authenticated Azure Stack
Installation and deployment improvements
Azure Resource Manager (ARM) API version 2016-03-01 support for App Services
Synchronization of SKUs with Azure – i.e. Free (F1), Shared (D1), and Standard (S1, S2, S3)
Service reliability improvements

Azure Stack TP3 refresh: Based on feedback and several ongoing improvements/bug fixes, we’ve also updated the Azure Stack TP3 software for a better deployment and operational experience. A list of the latest features and improvements to Azure Stack TP3 is now available.

Important Note: If you have already deployed Azure Stack TP3, you will need to redeploy using the updated software before deploying the Azure PaaS services.

Visit our Azure Stack technical documentation page to guide your deployment efforts and look at the documentation for App Service and Functions, SQL, and MySQL.

Visit the Azure Stack forum for troubleshooting help and User Voice if you’d like to provide feedback. Learn more and see the current list of known issues.

We’d love to hear from you!
Quelle: Azure

7. April 20177. April 2017

da Agency

What’s brewing in Visual Studio Team Services: April 2017 Digest

This post series provides the latest updates and news for Visual Studio Team Services and is a great way for Azure users to keep up-to-date with new features being released every three weeks. Visual Studio Team Services offers the best DevOps tooling to create an efficient continuous integration and release pipeline to Azure. With the rapidly expanding list of features in Team Services, teams can start to leverage it more efficiently for all areas of their Azure workflow, for apps written in any language and deployed to any OS.

Git tags

We’ve now added tag support into the web experience. Instead of creating tags from the command line and pushing the tags to the repository, you can now simply go to a commit and add a tag. The tag creation dialog will also let you tag any other ref in the repo.

Your commits will now show the tags that you have created.

The commit list view also supports a context menu. No need to go to the commit details page to create tags and create new branches.

Soon we will add a page for tag management.

Git branch policy improvements

Branch policies provide a great way to help maintain quality in your repos by allowing you to require a passing build, require code reviewers, and more. As part of review pull requests, users often leave comments. You can now ensure that all comments in pull requests are being addressed with the new Comments policy. Once enabled, active comments will block completion of the PR. Reviewers that leave comments for the PR author but optimistically approve the pull request can be sure that comments won’t be missed.

Sometimes you need to override policies, such as in the middle of the night when addressing an issue in production. Users bypassing pull request policies must now specify a reason. In the Complete pull request dialog, users will see a new Reason field, if they choose to bypass.

After entering the reason and completing the pull request, the message will be displayed in the pull request’s Overview.

Import Team Foundation Version Control into a Git repo

If you’re using Team Foundation Version Control (TFVC) and are looking for an easy way to migrate to Git, try out the new TFVC import feature. Select Import Repository from the repository selector drop-down.

Select TFVC for the source type. Individual folders or branches can be imported to a new Git repository, or the entire TFVC repository can be imported (minus the branches). You can import up to 180 days of history.

Team Foundation Version Control support for Android Studio, IntelliJ, and Rider

We’ve now officially released support for TFVC in Android Studio and the variety of JetBrains IDE’s such as IntelliJ IDEA and Rider EAP. Users can seamlessly develop without needing to switch back and forth from the IDE to the command line to perform their Team Services actions. It also includes additional features that you otherwise wouldn’t get from the command line client, such as seeing an updated status of your repository’s related builds along with the capability to browse work items assigned to you or from your personal queries.

Currently we support:

Checkout a TFVC repository from Team Services or Team Foundation Server 2015+
Execute all basic version control actions such as add, delete, rename, move, etc.
View local changes and history for your files
Create, view, and edit your workspace
Checkin and update local files
Merge conflicts from updates
Lock and unlock files and directories
Add labels to files and directories
Configure a TFS proxy

Check out our brief demo of getting up and running inside of Android Studio. For a more comprehensive look at the plugin, checkout our presentation and tutorial inside of IntelliJ.

To start using the TFVC features, download the latest version of the plugin and follow the setup steps.

Continuous delivery in the Azure portal using any Git repo

You can now configure a continuous delivery (CD) workflow for an Azure App Service for any public or private Git repository that is accessible from the Internet. With a few clicks in the Azure portal, you can set up a build and release definition in Team Services that will periodically check your Git repository for any changes, sync those changes, run an automated build and test, followed by a deployment to Azure App Service.

Start using this feature today by navigating to your app’s menu blade in the Azure portal and clicking Continuous Delivery (Preview) under the App Deployment section.

Conditional build tasks

If you’re looking for more control over your build tasks, such as a task to clean things up or send a message when something goes wrong, we now support four built-in choices for you to control when a task is run:

If you are looking for more flexibility, such as a task to run only for certain branches, with certain triggers, under certain conditions, you can express your own custom conditions:

and(failed(), eq(variables['Build.Reason&039;], &039;PullRequest&039;))

Take a look at the conditions for running a task.

Customizable backlog levels

You can now add backlog levels to manage the hierarchy of their work items and name them in a way that makes sense for your work item types. You can also rename and recolor existing backlog levels, such as Stories or Features. See Customize your backlogs or boards for a process for details on how to get started.

Mobile work item discussion

Our mobile discussion experience has been optimized to provide a mobile-friendly, streamlined experience for submitting a comment. Discussion is the most common action that takes place in a mobile device. We look forward to hearing what you think about our new experience!

Extension of the month

If you are like us, you use open source software in your development projects. Reusing components enables great productivity gains. However, you can also reuse security vulnerabilities or violate licenses without realizing it.

The WhiteSource Bolt extension for build makes it easy to find out whether you are using vulnerable components. After installing it in your account, add it to your build definition and queue a new build. You’ll get a report like the following. In the table under the summary, you will see a list of components with issues and the recommended way to address those issues.

If you have Visual Studio Enterprise, you get 6 months of WhiteSource Bolt for one team project included with your subscription (redeem the code from your benefits page or see this page for VS subscribers for more detailed instructions).

Have a look at the full list of new features by checking out the release notes for March 8th and March 29th.

Happy coding!
Quelle: Azure

6. April 20176. April 2017

da Agency

Enhance protection of VMs with Azure Advisor backup recommendations

We have seen a few customer cases where customers accidentally deleted VMs or data inside a VM running in Azure. While Azure provides protection against infrastructure related failures, it can’t guard against user initiated actions such as accidental deletion or a wrong patch on the guest OS triggered by customer. Azure Backup provides a capability to guard against accidental deletions and guest OS level corruption scenarios using its cloud-first approach to backup and seamlessly enables to restore a full VM or instantly recover files inside a VM. Customers can configure backup either from Recovery Services vault or directly from VM management blade. However, we have seen customers missing on configuring backup and risking their critical data. Today we are making a step towards making sure that we advise you to protect your VMs using backup with Advisor recommendations, made generally available last week.

Azure Advisors, is a personalized cloud consultant that helps to optimize use of Cloud, as customers start on their digital transformation using Azure. It analyzes your Azure usage and provides timely recommendations to help optimize and secure your deployments. It provides recommendations in four categories: High Availability, Security, Performance and Cost. With this announcement, it can provide recommendations about virtual machines which are not backed up and with few clicks it will let you enable backup on those virtual machines.

Value Proposition:

Periodic Recommendations – Advisors provide hourly recommendations for virtual machines that are not backed up so that you never miss to backup important VMs. You can also control recommendations by snoozing them.

Seamless experience to backup – You can seamlessly enable backup on virtual machines by clicking on a recommendation and by specifying vault (where backups will be stored) and backup policy (schedule of backups and retention of backup copies).

Freedom from infrastructure – With Azure Backup integration into recoomendations, you need not provision any additional infrastructure to configure backup.

Application consistent backup – Azure Backup provides application consistent backup for Windows and Linux and by configuring backup using recommendations, you will get a consistent backup without the need to shut down the virtual machine.

Real-time machine learning on globally-distributed data with Apache Spark and DocumentDB

At the Strata + Hadoop World 2017 Conference in San Jose, we have announced the Spark to DocumentDB Connector. It enables real-time data science, machine learning, and exploration over globally distributed data in Azure DocumentDB. Connecting Apache Spark to Azure DocumentDB accelerates our customer’s ability to solve fast-moving data science problems, where data can be quickly persisted and queried using DocumentDB. The Spark to DocumentDB connector efficiently exploits the native DocumentDB managed indexes and enables updateable columns when performing analytics, push-down predicate filtering against fast-changing globally-distributed data, ranging from IoT, data science, and analytics scenarios. The Spark to DocumentDB connector uses the Azure DocumentDB Java SDK. You can get started today and download the Spark connector from GitHub!

What is DocumentDB?

Azure DocumentDB is our globally distributed database service designed to enable developers to build planet scale applications. DocumentDB allows you to elastically scale both, throughput and storage across any number of geographical regions. The service offers guaranteed low latency at P99, 99.99% high availability, predictable throughput, and multiple well-defined consistency models, all backed by comprehensive SLAs. By virtue of its schema-agnostic and write optimized database engine, by default DocumentDB is capable of automatically indexing all the data it ingests and serve SQL, MongoDB, and JavaScript language-integrated queries in a scale-independent manner. As a cloud service, DocumentDB is carefully engineered with multi-tenancy and global distribution from the ground up.
These unique benefits make DocumentDB a great fit for both operational as well as analytical workloads for applications including web, mobile, personalization, gaming, IoT, and many other that need seamless scale and global replication.

What are the benefits of using DocumentDB for machine learning and data science?

DocumentDB is truly schema-free. By virtue of its commitment to the JSON data model directly within the database engine, it provides automatic indexing of JSON documents without requiring explicit schema or creation of secondary indexes. DocumentDB supports querying JSON documents using well-familiar SQL language. DocumentDB query is rooted in JavaScript's type system, expression evaluation, and function invocation. This, in turn, provides a natural programming model for relational projections, hierarchical navigation across JSON documents, self joins, spatial queries, and invocation of user defined functions (UDFs) written entirely in JavaScript, among other features. We have now expanded the SQL grammar to include aggregations, thus enabling globally-distributed aggs in addition to these capabilities.

Figure 1: With Spark Connector for DocumentDB, data is parallelized between the Spark worker nodes and DocumentDB data partitions

Distributed aggregations and advanced analytics

While Azure DocumentDB has aggregations (SUM, MIN, MAX, COUNT, SUM and working on GROUP BY, DISTINCT, etc.) as noted in Planet scale aggregates with Azure DocumentDB, connecting Apache Spark to DocumentDB allows you to easily and quickly perform an even larger variety of distributed aggregations by leveraging Apache Spark. For example, below is a screenshot of calculating a distributed MEDIAN calculation using Apache Spark&039;s PERCENTILE_APPROX function via Spark SQL.

select destination, percentile_approx(delay, 0.5) as median_delay
from df
where delay < 0
group by destination
order by percentile_approx(delay, 0.5)

Figure 2: Area visualization for the above distributed median calculation via Jupyter notebook service on Spark on Azure HDInsight.

Push-down predicate filtering

As noted in the following animated gif, the queries from Apache Spark will push down predicated to Azure DocumentDB and take advantage that DocumentDB indexes every attribute by default. Furthermore, by pushing computation close to the where the data lives, we can do processing in-situ, and reduce the amount of data that needs to be moved. At global scale, this results in tremendous performance speedups for analytical queries.

For example, if you only want to ask for the flights departing from Seattle (SEA), the Spark to DocumentDB connector will:

Send the query to Azure DocumentDB.
As all attributes within Azure DocumentDB are automatically indexed, only the flights pertaining to Seattle will be returned to the Spark worker nodes quickly.

This way as you perform your analytics, data science, or ML work, you will only transfer the data you need.

Blazing fast IoT scenarios

Azure DocumentDB is designed for high-throughput, low-latency IoT environments. The animated GIF below refers to a flights scenario.

Together, you can:

Handle high throughput of concurrent alerts (e.g., weather, flight information, global safety alerts, etc.)
Send this information downstream for device notifications, RESTful services, etc. (e.g., alert on your phone of an impending flight delay) including the use of change feed
At the same time, as you are building up ML models against your data, you can also make sense of the latest information

Updateable columns

Related to the previously noted blazing fast IoT scenarios, let&039;s dive into updateable columns:

As the new piece of information comes in (e.g. the flight delay has changed from 5 min to 30 min), you want to be able to quickly re-run your machine learning (ML) models to reflect this newest information. For example, you can predict the impact of the 30min for all the downstream flights. This event can be quickly initiated via the Azure DocumentDB Change Feed to refresh your ML models.

Next steps

In this blog post, we’ve looked at the new Spark to DocumentDB Connector. The Spark with DocumentDB enables both ad-hoc, interactive queries on big data, as well as advanced analytics, data science, machine learning, and artificial intelligence. DocumentDB can be used for capturing data that is collected incrementally from various sources across the globe. This includes social analytics, time series, game or application telemetry, retail catalogs, up-to-date trends and counters, and audit log systems. Spark can then be used for running advanced analytics and AI algorithms at scale on top of the data coming from DocumentDB.

Companies and developers can employ this scenario in online shopping recommendations, spam classifiers for real time communication applications, predictive analytics for personalization, and fraud detection models for mobile applications that need to make instant decisions to accept or reject a payment. Finally, internet of things scenarios fit in here as well, with the obvious difference that the data represents the actions of machines instead of people.

To get started running queries, create a new DocumentDB account from the Azure Portal and work with the project in our Azure-DocumentDB-Spark GitHub repo. Complete instructions are available in the Connecting Apache Spark to Azure DocumentDB article.

Stay up-to-date on the latest DocumentDB news and features by following us on Twitter @DocumentDB or reach out to us on the developer forums on Stack Overflow.
Quelle: Azure

5. April 20175. April 2017

da Agency

Announcing general availability of Azure HDInsight 3.6

This week at DataWorks Summit, we are pleased to announce general availability of Azure HDInsight 3.6 backed by our enterprise grade SLA. HDInsight 3.6 brings updates to various open source components in Apache Hadoop & Spark eco-system to the cloud, allowing customers to deploy them easily and run them reliably on an enterprise grade platform. What’s new in Azure HDInsight 3.6 Azure HDInsight 3.6 is a major update to the core Apache Hadoop & Spark platform as well as with various open source components. HDInsight 3.6 has the latest Hortonworks Data Platform (HDP) 2.6 platform, a collaborative effort between Microsoft and Hortonworks to bring HDP to market cloud-first. You can read more about this effort here. HDInsight 3.6 GA also builds upon the public preview of 3.6 which included Apache Spark 2.1. We would like to thank you for trying the preview and providing us feedback, which has helped us improve the product. Apache Spark 2.1 is now generally available, backed by our existing SLA. We are introducing capabilities to support real-time streaming solutions with Spark integration to Azure Event Hubs and leveraging the structured streaming connector in Kafka for HDInsight. This will allow customers to use Spark to analyze millions of real-time events ingested into these Azure services, thus enabling IoT and other real-time scenarios. HDInsight 3.6 will only have the latest version of Apache Spark such as 2.1 and above. There is no support for older versions such as 2.0.2 or below. Learn more on how to get started with Spark on HDInsight. Apache Hive 2.1 enables ~2X faster ETL with robust SQL standard ACID merge support and many more improvements. This release also includes an updated preview of Interactive Hive using LLAP (Long Lived and Process) which enables 25x faster queries. With the support of the new version of Hive, customers can expect sub-second performance, thus enabling enterprise data warehouse scenarios without the need for data movement. Learn more on how to get started with Interactive Hive on HDInsight. This release also includes new Hive views (Hive view 2.0) which provides an easy to use graphical user interface for developers to get started with Hadoop. Developers can use this to easily upload data to HDInsight, define tables, write queries and get insights from data faster using Hive views 2.0. Following screenshot shows new Hive views 2.0 interface. We are expanding our interactive data analysis by including Apache Zeppelin notebook apart from Jupyter. Zeppelin notebook is pre-installed when you use HDInsight 3.6, and you can easily launch it from the portal. Following screenshot shows Zeppelin notebook interface. Getting started with Azure HDInsight 3.6 It is very simple to get started with Apache HDInsight 3.6 – simply go to the Microsoft Azure portal and create an Azure HDInsight service. Once you’ve selected HDInsight, you can pick the specific version and workload based on your desired scenario. Azure HDInsight supports a wide range of scenarios and workloads such as Hive, Spark, Interactive Hive (Preview), HBase, Kafka (Preview), Storm, and R Server as options you can select from. Learn more on creating clusters in HDInsight. Once you’ve complete the wizard, the appropriate cluster will be created. Apart from the Azure portal, you can also automate creation of the HDInsight service using the Command Line Interface (CLI). Learn more on how to create cluster using CLI. We hope that you like the enhancements included within this release. Following are some resources to learn more about this HDI 3.6 release: Learn more and get help Azure HDInsight Overview Getting started with Azure HDInsight Use Hive on HDInsight Use Spark on HDInsight Use Interactive Hive on HDInsight Use HBase on HDInsight Use Kafka on HDInsight Use Storm on HDInsight Use R Server on HDInsight Open Source component guide on HDInsight Extend your cluster to install open source components HDInsight release notes HDInsight versioning and support guidelines How to upgrade HDInsight cluster to a new version Ask HDInsight questions on stackoverflow Ask HDInsight questions on Msdn forums Summary This week at DataWorks Summit, we are pleased to announce general availability of Azure HDInsight 3.6 backed by our enterprise grade SLA. HDInsight 3.6 brings updates to various open source components in Apache Hadoop & Spark eco-system to the cloud, allowing customers to deploy them easily and run them reliably on an enterprise grade platform.
Quelle: Azure

4. April 20174. April 2017

da Agency

Microsoft’s open approach to networking

At Microsoft, we’re focused on enabling our customers by supporting all the technologies they depend on, and collaborating across organizational and industrial boundaries to bring the best possible experience to the cloud. Microsoft embraces open source and partner ecosystems to scale our own development efforts and accelerate innovation. Products that include Visual Studio Code, .NET, and ASP.NET are being publicly developed on GitHub with contributions from both Microsoft and non-Microsoft developers. These products are targeting Windows, Mac, and Linux. Microsoft is a contributing member of open source communities, including the Apache Software Foundation, Linux Foundation, R Consortium, and Node.js Foundation.

For the Azure cloud platform, we serve customers on a vast worldwide scale, and they bring a wide range of technology needs with them. We must provide solutions with the unique flexibility to operate seamlessly across on-premises, hybrid, and cloud infrastructure, in an operating system–agnostic environment. Today, Linux virtual machines (VMs) comprise over 33 percent of all VMs running in Azure. Many partners in the Azure Marketplace run their workloads in Linux. Our HDInsight MapReduce service is built on Apache Hadoop and supports Spark, Hive, Apache Kafka, and Apache Storm. Meanwhile, the Azure Container Service (ACS) adopts open source container technologies like Docker, Apache Mesos, and Kubernetes to run both Linux and Windows containers. By doing this, ACS provides container orchestration that’s completely portable, while also being optimized for Azure.

In this blog, I will talk about how Azure network services is extending this commitment to open technologies in containers, switching, and partner ecosystems.

Open source software in Azure Network Services

Azure network services actively look for opportunities to contribute to existing open source projects, as well as open source Azure Networking services. Considering the importance of networking to fully realize the potential of containers, we just announced Microsoft Azure VNet for Containers.

Azure VNet for Containers

Azure VNet for Containers provides the best networking experience for containers that are running in Azure. It‘s an open source project in GitHub that links together open source container orchestrator engines and the Azure network services platform. The code, written in the Go programming language, works for both Linux and Windows. We’re eager to collaborate with developers across the world to improve and advance its capabilities.

Azure VNet for Containers connects the container to your Azure Virtual Network (VNet), thereby making available the rich Azure SDN stack to containers enabling direct connectivity between containers, VMs and other resources in the VNet. Azure networking features such as Network Security Groups, route tables, load balancing, on-premises connectivity etc. are now available to containers. The solution can be plugged into the Azure Container Service for a single click use or deployed manually in individual virtual machines.

The Azure VNet for Containers is composed of a network plug-in that provides the network interface for the containers and an IPAM (IP address management) plug-in that manages the IP addresses from the VNet. There are currently two popular plug-in models for containers: the Container Network Interface (CNI) model, adopted by Kubernetes, Apache Mesos, and others, and the Container Network Model (CNM) model, used by Docker and others. The Azure container network plug-in is implemented for both models. This is also designed to be integrated directly into the open source acs-engine.

Figure 1. Azure network services support for containers

With the availability of this plug-in, the power and features of Azure network services are natively available to all the major container platforms in an open and portable fashion.
SONiC

Software for Open Networking in the Cloud (SONiC) and Switch Abstraction Interface (SAI) are two contributions that we made to the Open Compute Project (OCP) that focuses on open source datacenter technologies. Like Azure VNet for Containers, SONiC also uses containerization for fast evolution.

SONiC source code, test cases, test bed setup, and builds are fully available on GitHub. SONiC consists of core services developed by Microsoft and the community. It builds on existing open source technologies such as Docker for containers, Redis for key-value database, protocols like Quagga BGP and LLDPD, and Ansible for deployment. We used the best work in the industry to build SONiC. It evolves quickly because we’re building it with existing open source projects. We contributed SONiC back to the community to propel the advance of open networking software in a wonderful, virtuous cycle.

Figure 2. SONiC is open sourced and is built on open source technologies

SAI provides a simple, consistent, and salable interface across different ASIC chips. With the support from major silicon vendors, the SAI community grew to 77 contributors from 9 companies. Community members actively engage in weekly discussions and workshops. In two years, we had seven releases. Six switch networking stacks(network operating systems), including SONiC, OS10, OPX, FlexSwitch and others, are built on top of SAI, which is starting to become the ASIC API standard.

Learn more by viewing our OCP Summit 2017 talks about SONiC and SAI. You also can learn more about our SAI and SONiC innovations in an earlier blog in this series, SONiC: The networking switch software that powers the Microsoft Global Cloud.

Rich partner ecosystem

Network virtual appliances (NVAs) in Azure support network functionality and services in the form of VMs. NVAs include web application firewall (WAF), firewalls, gateways/routers, application delivery controllers, IDS/IPS, WAN optimizers, SD-WAN solutions, and other network functions. Customers can deploy these NVAs through the Azure Marketplace into their VNets and deployments. Examples of open sourced NVAs include NGINX and pfSense. Over 90 percent of NVAs are based on Linux or FreeBSD.

We also use open source technologies in our own NVAs. We just announced the general availability of Azure Application Gateway WAF to protect applications from the most common web vulnerabilities, as identified by Open Web Application Security Project (OWASP) Top 10 vulnerabilities. Application Gateway WAF uses the OWASP ModSecurity Core Rule Set. These rules, managed and maintained by the open source community, conform to rigorous standards.

Optics

Typically, you don’t think of optical technologies in the context of openness. However, we’ve also innovated at the optical network layer. Microsoft has incorporated new optical technologies into the Azure network. Findings from ACG Research show that the Microsoft metro network solution will result in over 65 percent reduction in total cost of ownership and power savings of over 70 percent over five years. We’ve worked with several of our partners to make available to everyone the building blocks of the Microsoft implementation of open optical systems. Microsoft is working with our partners to bring even more integration, miniaturization, and power savings into future 400 Gbps interconnects that will power our network and benefit the entire industry.

Academic publications

Many of the underlying technical innovations in Azure Networking have their roots in Microsoft Research. We published in top peer reviewed academic forums the internal designs and algorithms of the Azure Networking SDN stack (SIGCOMM 2015), programmable virtual switching (NSDI 2017) , software load balancing (SIGCOMM 2013), network virtualization (SIGCOMM 2009), and innovative diagnostics and monitoring mechanisms. Our Azure networking services team has a deep passion for tackling the hardest networking scale problems in the world. We will continue to share our innovations in academic papers to receive critical feedback about our ideas, as well as to help the network community further advance, which in turn pushes us to be better.

Summary

Over the past few years, Microsoft has embraced, and is fully committed to, open source. Our motivation is simple. We want the best technologies in the world to be available and performant in Azure. We cherish opportunities to contribute to the open source community and to incorporate the communities’ advancements into our services. Considering the scale of the issues that we face daily running one of the world’s largest networks, we are very passionate about advancing state-of-the-art networking. By sharing code via open source projects and ideas via academic forums, we accelerate innovation. We’re a different Microsoft from years past. The cloud and open source are changing the world. This is an exciting time for all of us in networking as we all strive to help customers adapt and take full advantage of the cloud.

To read more posts from this series please visit:

Networking innovations that drive the cloud disruption
SONiC: The networking switch software that powers the Microsoft Global Cloud
How Microsoft builds its fast and reliable global network
Lighting up network innovation
Azure Network Security

Quelle: Azure

4. April 20174. April 2017

da Agency

Price reductions on L Series and announcing next generation Hyper-threaded virtual machines

For Microsoft Azure, we have a long standing promise of making our prices comparable with AWS on commodity services such as compute, storage, and bandwidth. In keeping with this commitment, we are happy to announce price reductions of up to 69% on our storage-optimized virtual machines, L Series. We are also excited to share more about our next generation of Hyper-Threaded virtual machines for general purpose and memory optimized workloads that are up to 28% lower in prices than the current generation.

Price reductions on L Series

We are reducing prices by 60% to 69% on our newly-launched L Series virtual machines, effective April 1st to match recent price changes from AWS. These VMs are storage optimized sizes, best suited for low latency workloads such as NoSQL databases including Cassandra and MongoDB. L Series offers virtual machines from 4 to 32 vCPUs, based on Intel® Xeon® processor E5 v3 family with 32 to 256 GiB memory, and from 678 GB to 5.6TB of SSD disk.

New Hyper-Threaded VMs and Dv2 limited time promotion

In the next few months, Microsoft will be introducing a new generation of Hyper-Threading Technology virtual machines for general purpose workloads, Dv3, and a new family for memory optimized workloads, Ev3. This shift from physical cores to virtual cores is a key architectural change in our VMs that enables us to unlock the full potential of the latest processors. This new generation will introduce sizes with 64 vCPUs on Intel® Broadwell E5-2673 v4 2.3 processor and with 432 GiB of memory on the largest Ev3 sizes. By unlocking more power from the underlying hardware, we are able to harness better performance and efficiency, resulting in cost savings that we are passing on to our customers.

As our new Hyper-Threaded VMs become generally available in the coming months, we would like to give our customers the opportunity to take advantage of these savings early. These new Hyper-Threaded VMs will be priced up to 28% lower than Dv2 Series VMs, matching the comparable AWS instance prices. Starting today, you can provision a Dv2 Promo VM on our current generation hardware at the lower Dv3 and Ev3 VM prices, allowing you to take advantage of these cost savings now.

This promotion will be available until the launch of the Dv3 and Ev3 VMs later this year. We encourage you to deploy the Dv2 Promo VMs using Azure Resource Manager to simplify migration to the new VMs in the future.
Quelle: Azure