Install on Windows – Deploy windows server 2016 standard in azure free download

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Those who manage virtual machines have learned that deploying VMs to the Azure public cloud from Microsoft is the best option. Review Windows Server release notes and system requirements. Register, then download and install. Windows Server evaluation editions expire in Next Windows Server や Windows 10 Enterprise LTSB でヘルプなどを選択した際 free webinar briefings on key Azure Active Directory deployment topics! hybrid solution, Azure Stack brings Azure services locally to the on-premise enterprise datacenter. It includes Windows Server How to install Docker Desktop for Windows. on how to set up and run Windows containers on Windows 10, Windows Server and Windows Server ❿
 
 

Deploy windows server 2016 standard in azure free download.Windows Server | Eval Center

 

Microsoft Imagine users may download and use it for experimentation, learning, and academic lab purposes too. Customers who download the full ISO will need to choose an edition and an installation option.

The Datacenter edition is a complete edition and includes the new datacenter-specific features Shielded Virtual Machines, Storage Spaces Direct, Storage Replica, and Software-Defined Networking in addition to unlimited server virtualization.

I had to put my experience to tell you, not only solved my problem and recommend it to friends say good, great job, but it only took me a little money, that makes me very happy. I installed to on my hp laptop, but after installation, it denied to accept last purchased windows 10 pro license from Microsoft, So I contacted them and followed their instructions but the same issue, I cannot buy a license again for my OS.

At this condition, one of your users recommend me to buy it from ODosta Which is an Indian site with best customer support?

We never recommend to purchase Windows license from outside Microsoft store. Try to fix the your Windows license with Microsoft support team instead of purchasing a new one.

Ninety percent Windows is pirated, the other ten are Microsoft partners or Enterprise users with unlimited wallets. Rest of the world uses Linux just saying err okay maybe one percent use a Mac but who cares about that. Thanks for the feedback. I want the link for windows server storage standard. Hi Admin, thank you so much admin for direct link otherwise if go through microsoft portal they plenty of question , you make my day.

It is asking for a license key when adding to VM. If we skip, its not getting installed. Any work around? Hi, I downloaded to use in VirtualBox but it shows on my desktop. How do I remove from my desktop? The Windows Server iso file is not password protected. You have to make sure you downloaded from a trusted source.

Save my name, email, and website in this browser for the next time I comment. This website uses cookies to improve your experience. We\’ll assume you\’re ok with this, but you can opt-out if you wish. Accept Read More. Servers Download. By Shams Last updated May 24, Free Download Windows Server ISO file for practicing Server Virtualization — Technig The Windows Server is the cloud-ready operating system that delivers new layers of security and Azure-inspired innovation for the applications and infrastructure.

Built-in Security Windows Server gives you the power to prevent attacks and detect suspicious activity with new features to control privileged access, protect virtual machines and harden the platform against emerging threats. Software-defined Infrastructure Windows Server delivers a more flexible and cost-efficient operating system for your data center, using software-defined compute, storage, and network virtualization features inspired by Azure.

Customers who download the full ISO will need to choose an edition and an installation option. The Datacenter edition is the most complete edition and includes the new datacenter-specific features Shielded Virtual Machines, Storage Spaces Direct, Storage Replica, and Software-Defined Networking in addition to unlimited server virtualization. When you complete your evaluation, you can convert your evaluation versions to retail.

Also, check out our technical documentation to learn how to upgrade or migrate your other existing servers to Windows Server Starting with the Fall release, Nano Server has been optimized for container use only and Server Core is available for host and guest VM deployments. Microsoft Docs Windows Server technical documentation. Upgrade Options Overview of Windows Server upgrades. How to Buy Pricing and licensing for Windows Server. Windows Server is the platform for building an infrastructure of connected applications, networks, and web services, from the workgroup to the data center.

It bridges on-premises environments with Azure, adding additional layers of security while helping you modernize your applications and infrastructure. Get started with Windows Server: R2. Server Core. Check Parameters for the System Configuration Checker. Configuration File. Add Features to an Instance. SQL Server Failover Cluster Installation. Repair a Failed SQL Server Installation. Rename a computer with SQL Server.

Update system metadata that is stored in sys. servers after the hostname of a computer hosting a stand-alone instance of SQL Server has been renamed. Install SQL Server Servicing Updates. Setup Log Files. Validate an Installation. Review the use of the SQL Discovery report to verify the version of SQL Server and the SQL Server features installed on the computer.

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Deploy windows server 2016 standard in azure free download – Change History

 

Customers who download the full ISO will need to choose an edition and an installation option. This ISO evaluation is for the Datacenter and Standard editions. The Datacenter edition is the most complete edition and includes the new datacenter-specific features Shielded Virtual Machines, Storage Spaces Direct, Storage Replica, and Software-Defined Networking in addition to unlimited server virtualization.

When you complete your evaluation, you can convert your evaluation versions to retail. Also, check out our technical documentation to learn how to upgrade or migrate your other existing servers to Windows Server The Nano Server deployment option in the Windows Server eval ISO is supported for host and guest VM deployments until the Spring release of Windows Server.

Starting with the Fall release, Nano Server has been optimized for container use only and Server Core is available for host and guest VM deployments. Microsoft Docs Windows Server technical documentation. Community Microsoft Tech Community:Windows Server. Getting Started Guide Get started with Windows Server. Upgrade Options Overview of Windows Server upgrades. How to Buy Pricing and licensing for Windows Server. Windows Server is the platform for building an infrastructure of connected applications, networks, and web services, from the workgroup to the data center.

It bridges on-premises environments with Azure, adding additional layers of security while helping you modernize your applications and infrastructure.

Get started with Windows Server: R2. Windows Server Essentials edition is a cloud-connected first server designed for small businesses with up to 25 users and 50 devices. If you are considering installing any version of Windows Server Essentials, we would encourage you to consider Microsoft Get started with Windows Server Essentials: R2 Learn more about Microsoft for business. Hyper-V Server provides a simple and reliable virtualization solution to help organizations improve their server utilization and reduce costs.

The latest release of Hyper-V Server provides new and enhanced features that can help you deliver the scale and performance needs of your mission-critical workloads. Get started with Hyper-V Server: R2 This is a limited time offer running throug h out until 30th June , and I encourage you to book your Trial in a Box now.

These include: your school accepts these as gifts from Microsoft; the school allows the entire Trial in a Box demonstration to be completed in full; your school agrees to share information on the current numbers and brands of devices owned or leased by the school. 執筆者 : Leon Welicki Principal Group PM Manager, Azure. このポストは、 年 9 月 24 日に投稿された Announcing Azure user experience improvements at Ignite の翻訳です。. Azure ユーザーの皆さん、こんにちは!

本日は、 Azure ユーザー エクスペリエンスに行った改善点をご紹介します。このブログ記事では、今回のリリースに含まれる改善点の一部を取り上げます。これらの改善を実現できたのは、ユーザーの皆さんからのフィードバックのおかげです。引き続きフィードバックをお待ちしています。. マイクロソフトは、 モダン デザインへの更新 を実施し、生産性と操作性の向上、画面面積の有効利用を目的として、 Azure Portal のデザインと操作性を刷新しました。. デザインは更新されましたが、 既存のインタラクション モデルに変更はない ため、製品の使用方法を学習し直す必要はありません。これまでと同じパターンやインタラクションを引き続き使用しながら、ここでご紹介したメリットも活用できるようになります。ぜひ感想をお聞かせください! Azure をご利用の皆様の多くは、個人用と仕事用の複数のアカウントを管理していることと思います。今回のリリースでは、同じブラウザー インスタンスで 複数のアカウントを切り替えられる機能 をプレビューとして提供します。以下の画像のようなイメージです。.

Azure Portal で複数のアカウントを管理. このエクスペリエンスは プレビュー環境 でのみ提供されます。ぜひお試しいただき、ご意見をお聞かせください。. このエクスペリエンスを提供するにあたって、サブスクリプション フィルターを上部のナビゲーション バーに移動しました 以下の画像を参照 。. サブスクリプション フィルターとディレクトリの選択パネル 機能の説明. マイクロソフトは Build 以来、 AKS と IoT Hub による リソース作成の簡素化 に取り組んでいます。作成ウィザードを垂直パネルから水平タブに移動し、わかりやすい名前と説明を付けて、画面面積を有効利用できるようにしました。今回の新しい作成エクスペリエンスは、次の原則に沿って設計されました。. 今回のリリースでは、 Virtual Machines および Storage のアカウント作成エクスペリエンスにこれらの原則を適用しました。.

それでは、 仮想マシンの作成 について見ていきましょう。仮想マシンの作成には、上記の原則を適用しただけでなく、フローを設計し直し、ポータル内で仮想マシンを作成するときに選択できるオプション 複数のデータ ディスク、 OS オプション、初期化、タグ付けなど も改善しました。これらの新しい設定はすべてオプションなので、それらを活用してより細かい設定でリソースを作成できますが、必要に応じて シンプルで簡単な方法 で作成することも可能です 簡易作成 。以下のアニメーション GIF は、 仮想マシンのデプロイをわずか数秒で始める 方法を示しています。. リソースが作成されると、 デプロイ 手続きに入ります。このとき、作成中のリソースの ステータス や作成操作に関する有用な情報を確認したり、デプロイする実際のテンプレートにアクセスしたりすることができます。.

デプロイ中 作成後に自動的に表示される画面. デプロイが完了すると、 [Go to resource] ボタンが表示されます。クリックすると、 VM の管理を開始できます。. デプロイに成功 リソースに直接移動するボタンが表示される. デプロイに失敗した場合、 一貫したエラー エクスペリエンス が得られます。ここからエラーの原因を特定し、対処することができます。エラー メッセージが 印字 される場合と、バックエンドから JSON が返され、そこから障害の詳細情報に入手できる場合があります。. デプロイに失敗 上部の赤いバナーをクリックするとパネルが開き、エラーの詳細が表示される. Storage の作成エクスペリエンスも同様に更新されています。以下は Storage のアカウント画面です。. Storage アカウント作成エクスペリエンス. 通知 パネルが更新され、 情報密度 、入手可能なデータ、 インタラクション の点でデータの表示方法が改善されました。これらの変更に加え、 Notification と Activity Log を連携することにも着手しています。これは、両方のエクスペリエンスの多くのエントリがデータ ソースを共有しているためです。まず、通知リストの上部にアクティビティ ログへのリンクを表示します。通知リストが空のときも同じリンクを表示します。.

Azure Activity Log の UI も改善されています。馴染みのない方のためにご説明すると、 Activity Log は、 Azure 内で発生したサブスクリプションレベルの イベント に関するインサイトを提供するログです。これには、 Azure Resource Manager のオペレーション データ から サービスの正常性イベント の更新情報まで、さまざまなデータが含まれています。新しいエクスペリエンスは、 生産性 、画面 面積 の利用、 パフォーマンス を向上させる目的で設計されています。.

ログを確認できるだけでなく、 CSV ファイルとしてダウンロードし、 クイック インサイト を確認することもできます。クイック インサイトは、過去 24 時間に発生したエラーやデプロイといった一般的なクエリのセットです。.

Storage Explorer が参照可能なリソースとして提供されるようになり、 [All Services] リストまたはグローバル検索から簡単にアクセスできるようになりました。複数のサブスクリプションの複数のストレージ アカウントを単一のビューから簡単に管理できます。. Azure Advisor の通知が仮想マシンの概要ページに統合され、ステータス バーとして表示されるようになりました。このステータス バーをクリックすると、 Azure Advisor が開きます。ここで推奨事項を確認し、対処することができます。. 仮想マシンのブレード 上部の青いバナー から Azure Advisor にアクセス. Quickstart Center は、 Azure を初めて使用するお客様を支援する目的で、 Build のプレビューとしてリリースされたエクスペリエンスです。今回のリリースでは、お客様のフィードバックに基づいてエクスペリエンスを簡素化しました。.

QuickStart Center で目標を選択. また、 プレイブック を追加して QuickStart Center の機能を拡張しました。プレイブックとは、マイクロソフトがお勧めするベスト プラクティスとガバナンス ガイドラインに従って Azure 環境をセットアップできるようにしたインストラクションのセットです。 RBAC によるアクセス管理、リソースの整理と タグ付け 、リソースの 保護 、 コンプライアンス の実施、 Azure 環境の正常性の 監視 などのトピックをご用意しています。.

Azure Resource Graph ARG では、大量の Azure リソースを使用して高速かつリッチなクエリを実行できます。今回のリリースでは、最も一般的なエントリ ポイントである [ All Resources ] ビューで ARG を使用するエクスペリエンスのプレビューを提供します。. この新しいエクスペリエンスは、高速になっただけでなく、大量のリソースに対応しています。また、さまざまな切り口で結果をナビゲートできます。これに加え、 グローバル検索 にも ARG が統合されたため、ポータル上部の検索バーを使用する際、さらに高速なエクスペリエンスをお楽しみいただけます。. 新しくなった [All Resources] ビューのエクスペリエンス プレビュー.

このエクスペリエンスの UI は今も進化 を続けています。 Azure で優れたリソース表示エクスペリエンスを実現するために、皆さんからのフィードバックをお待ちしています。新しいエクスペリエンスをお試しになったら、ぜひご意見をお寄せください。. 最も多く寄せられたご要望 の 1 つは、 Azure モバイル アプリから ロール ベースのアクセス制御を管理 したいというものです。外出中にロール ベースのアクセス制御を管理できれば、モバイルの機能として強力です。コンピューターから離れているとユーザー自身ですべてのミッション クリティカルなシナリオに対処することはできませんが、 Azure モバイル アプリがあれば、同僚に必要な許可を与え、問題への対処を依頼することができます。.

モバイル アプリのロール ベースのアクセス制御 iOS と Android. この記事では多くの新機能を紹介してきましたが、これらは今回のリリースで追加されるうちのほんの一部です。 Azure チームは、エクスペリエンスの改善に向けて常に全力で取り組んでいます。今後もより良いエクスペリエンスをお届けできるように、ぜひフィードバックをお寄せください。 Azure のユーザー エクスペリエンスに関するフィードバックやご意見は、 lwelicki microsoft. com まで直接お寄せください。. So, it makes sense that people would rely on two digital assistants to stay on top of their home and work lives — but also want the two of them to work together at times. customers who are interested in early access to the collaboration will be able to summon Cortana on Echo devices and enable Alexa on their Windows 10 PCs and Harman Kardon Invoke speakers.

As part of a public preview releasing this week, they also can offer feedback on how to improve the experience. 執筆者 : Corey Sanders Azure 、コーポレート バイス プレジデント. このポストは、 年 9 月 24 日に投稿された A crazy amount of new Azure infrastructure announcements の翻訳です。. 皆様、こんにちは。今回の記事では、フロリダ州オーランドで開催された Microsoft Ignite での発表内容を簡単にまとめてご紹介します。 Microsoft Ignite は、親しい知人やお客様をはじめ、 2 万人もの方々とお会いできるすばらしいイベントです。.

会場では、「 Azure のインフラストラクチャはビジネス変革にどう役立つのか? このようなサービスは、企業の皆様との長年の密接なパートナーシップによって生まれたものです。たとえば、次の 2 社のお客様がいます。 Walmart 英語 はマイクロソフトとの連携のもと、 Azure コンピューティング機能を活用した小売業界におけるデジタル イノベーションを推進しています。また、輸送業界大手の J. Hunt 英語 はマイクロソフト パートナーと協力し、 Azure を活用した J. マイクロソフトは、コア インフラストラクチャだけでなく、サーバーレスの Kubernetes のサポートや、高速で使いやすい Azure Functions 、 App Service のシンプルな Web アプリケーション、 IoT エッジ コンピューティング、変換可能な AI 、データ分析など、最先端の機能のイノベーションを急ピッチで進めています。. 本日は、「あらゆるワークロード向けのインフラストラクチャ」、「ハイブリッド サービス」、「セキュリティと管理」という 3 つの分野の最新イノベーションをご紹介します。.

GPU 対応の仮想マシン. NVIDIA GPU 機能を備えた 2 つの新しい N-series の仮想マシンをリリースしました。コンピューティングやグラフィック負荷の高いワークロードに最適な GPU は、ハイエンドのリモート ビジュアライゼーション、人口機能、予測分析などのシナリオにおけるイノベーションで活用できます。. NVv2 VM プレビュー – 新しい Nv-series は、パワフルなリモート ビジュアライゼーション ワークロードやグラフィック負荷の高いアプリケーションをサポートします。 NVIDIA GRID テクノロジと NVIDIA Tesla M60 GPU を採用し、最大 GiB の RAM で Premium SSD をサポートします。現在、米国西部、南中部、ヨーロッパ西部でプレビュー版をご利用いただけます。.

NDv2 VM 年内にプレビュー – ND シリーズに新たに追加された NDv2 VM は、 DL トレーニング、推論、機械学習に特化しています。 NVIDIA NVLink GPU で相互接続された 8 基の NVIDIA Tesla V Tensor コア GPU と、 40 基の Intel Skylake コアを搭載し、さらなる品質と速度を実現しています。 NDv2 VM のプレビューは 年内に提供を開始する予定です。. 詳しくは エンジニアリング ブログ 英語 、またはマイクロソフトの サイト をご覧ください。. 高性能コンピューティング HPC に最適な 2 つの新しい H シリーズ VM を発表しました。この VM は、流体力学、構造力学、エネルギー探査、気象予報、リスク分析などの HPC ワークロードに向けてパフォーマンスとコストが最適化されています。.

HB VM 年内にプレビュー – HB VM は、 60 基の AMD EPYC コアと GiB の RAM を搭載しており、パブリック クラウドにおいて最も高い 20GBps のメモリ帯域幅を誇ります。これは、流体力学や気象予報の計算を行ううえで非常に重要です。. HC VM 年内にプレビュー – HC VM は計算量の非常に多いワークロード向けに最適化されています。最大 GiB の RAM 、 44 基の Intel Skylake コアを搭載し、クロック速度は最大 3.

H シリーズ VM は 年末にプレビューを開始する予定です。詳しくは こちら 英語 をご覧ください。. お客様のネットワークのニーズに対応するために、 Azure Firewall と Virtual WAN の一般提供を開始しました。さらに、 Azure Front Door Service 、 ExpressRoute Global Reach 、 ExpressRoute Direct のプレビューもご利用いただけます。.

Azure Firewall 一般提供 – Azure Firewall は、 Azure Virtual Network のリソースを保護するクラウド ベースのマネージド ネットワーク セキュリティ サービスです。完全なステートフル ファイアウォールで、高可用性とクラウドの拡張性を実現します。 Azure Firewall のドキュメントは こちら 、価格は こちら でご覧いただけます。.

Virtual WAN 一般提供 – Virtual WAN は、グローバル接続が可能な、シンプルな統合セキュリティ プラットフォームで、大規模なブランチ接続に対応しています。お好みの SDWAN およびセキュリティ テクノロジ ベンダーを利用することができ、 OpenVPN を利用したクライアントサイドの接続もサポートしています。. ExpressRoute Global Reach プレビュー – ExpressRoute Global Reach では、 ExpressRoute 回路を接続することで、オンプレミスからのトラフィック送信にマイクロソフトのグローバル ネットワークを活用できます。たとえば、カリフォルニアとテキサスに ExpressRoute に接続されたデータセンターがある場合、マイクロソフトのグローバル ネットワーク バックボーンを使用して 2 つのデータセンター間のトラフィックをやり取りできます。世界最大のグローバル ネットワークを備えた Azure は、このサービスを実現できる唯一のクラウドです。.

ExpressRoute Direct プレビュー – ExpressRoute Direct は、パブリック クラウドとの世界最速のプライベート エッジ接続を可能にします。グローバルなマイクロソフトのバックボーンに最高 Gbps の速度で直接接続できます。 Azure のリソースやリージョンへのアクセスにグローバル バックボーンを使用することで、ストレージへの大量データ投入、物理的な分離、専用キャパシティ、高帯域幅バースト キャパシティなどのシナリオが実現します。.

Front Door Service プレビュー – Front Door は、拡張性に非常に優れた安全なグローバル Web アプリケーション向けエントリ ポイントを提供します。エンドユーザーに近接した POP で構成される、グローバルなエニーキャスト ベースのネットワークを利用することで、 HTTP 負荷分散とパスベースのルーティング ルールにより、 Web アプリケーションを簡単に拡張できるようになります。.

Standard SSD 一般提供 – Standard SSD は、常に一定のレイテンシを必要とする IOPS の低いワークロードに最適化されたコスト効率の良いディスク製品です。 HDD ディスクと比べて、可用性、信頼性、レイテンシに優れているいという特長があり、 Web サーバー、 IOPS の低いアプリケーション サーバー、あまり頻繁に使用しないエンタープライズ アプリケーション、開発およびテスト ワークロードに最適です。詳しくは エンジニアリング ブログ 英語 をご覧ください。.

マネージド ディスク サイズの拡大 プレビュー – Premium SSD のシングル ディスクのストレージ容量が 32TiB に拡張され、最大ディスク IOPS は 20, 、帯域幅は MBps になります。ストレージ容量を大幅に拡張しつつ、管理が簡単になるという利点があります。詳しくは エンジニアリング ブログ 英語 をご覧ください。. 現在、多くの企業がクラウドを導入しています。しかし、グローバル企業では、さまざまな理由でオンプレミスのデータセンターを活用する必要があります。たとえば、データ主権の問題や各種規制要件に直面しているケース、ローカルのデータセンターに残しておく必要があるミッションクリティカルなシステムを運用しているケース、データをオンプレミスまたは国内に保存するというコンプライアンス要件があるケースなどです。マイクロソフトはこの事実に即して、一貫性のある包括的なハイブリッド クラウドを構築しました。今回、データ管理、一貫性の強化、ハイブリッド環境の保護に役立つ新しいハイブリッド機能をリリースします。.

Azure Data Box Edge プレビュー – Azure Data Box Edge はマイクロソフトが提供する物理アプライアンスです。エッジでコンピューティングを実行しながら、 Azure にデータを移行することができます。 AI 対応のエッジ コンピューティング機能を備えているため、オンプレミスのデータをクラウドにアップロードする前に分析し、事前処理を加え、変換することができます。詳しくは Data Box Edge ブログ 英語 をご覧ください。.

Windows Server 近日中に一般提供 – Windows Server の最新バージョンをリリースします。 Windows Server は、ハイブリッド管理、 Linux コンテナーなどのすばらしい機能を備えたクラウド向けの OS です。詳しくは Windows Server ブログ 英語 をご覧ください。. Azure Stack – Azure と Azure Stack を組み合わせることで、アプリケーションのデプロイをさらに柔軟なものにし、選択肢が広がります。このたび、ハイブリッド環境の一貫性のオプションとして、 Azure の Event Hubs 、 Blockchain Template 、 Kubernetes のプレビューを開始します。詳しくは ブログ 英語 をご覧ください。.

Azure は、世界中に配置されているデータセンターの保護、 DoS 攻撃からのインフラストラクチャ保護、 just-in-time アクセスのようなプラットフォーム保護などを可能にするセキュリティ基盤を提供します。今回、 Azure のセキュリティ コントロールとサービスを拡張して、ネットワーク、アプリケーション、データ、 ID の保護に役立つ新しいサービスをリリースします。これらのサービスは、 Office や Xbox などのファースト パーティ サービスで収集された膨大な数のシグナルから生まれるユニークなインテリジェンスによって強化されます。. Confidential Computing DC VM シリーズ 近日プレビュー – 数週間後に、 Confidential Computing と開発用オープン SDK に対応した新しい VM サイズのプレビューをリリースします。 DC サイズは、 Intel SGX テクノロジを使用して保護されたエンクレーブを有効にすることにより、 CPU 処理中のデータまでを保護することができます。悪意のある社内または社外の当事者から使用中のデータを保護し、機密性と整合性を確保できるようにしたのは、クラウド プロバイダーの中でもマイクロソフトが初めてです。このリリースでは、この新しいクラウド セキュリティを活用するためのオープン SDK も提供します。.

Secure Score 、脅威保護の強化、ネットワーク マップ プレビュー – Microsoft Secure Score は、現在の環境のセキュリティ スコアや潜在的なリスクの把握を容易にします。 Azure Security Center では、 Secure Score とリスクを緩和しセキュリティを強化する推奨策が表示されるようになりました。脅威保護機能を拡張し、 Azure Storage 、 Azure Postgres SQL 、 Linux VM で稼働するコンテナーを追加しました。また、新しいネットワーク マップを追加したことで、ネットワーク関連の脆弱性をすばやく可視化し、把握できるようになりました。詳しくは ブログ 英語 をご覧ください。. Azure DevOps の Azure Blueprint と Azure Policy プレビュー – 本日、 Azure Blueprints のプレビューを開始しました。これは、ポリシー、ロールベースのアクセス制御、リソース テンプレートといった構成可能なアーティファクトを使用して、 Azure 環境を簡単に反復可能な形でデプロイおよび更新できるようにする機能です。これを使用すると、異なる環境を複数構成してすばやく準拠させることができます。開発者は、サポートいらずで新しい環境を作成できるようになります。さらに、 Azure DevOps のリリース管理パイプラインに Azure Policy 定義を追加できるため、リリース後ではなく出荷前にポリシーのコンプライアンスを確立することができます。.

Azure ポータルのコスト管理機能 プレビュー – 私たちは 1 年前に、 Azure が、クラウドにおけるコスト削減を支援する、無料のコスト管理機能を備えた初のクラウド プラットフォームであると発表しました。今回、エクスペリエンス改善の一環で、このコスト管理機能がネイティブ機能として Azure ポータルに統合されます。さらに、 PowerBI やカスタム アプリケーションから直接コスト管理機能にアクセスできる API も提供します。 Azure ポータルのコスト管理機能のプレビューは、現在 EA のお客様にご利用いただけます。その他のお客様には、年末にかけて順次提供を開始する予定です。. クラウドへの移行は複雑であり、簡単なものではありません。マイクロソフトは、クラウドへの移行をできる限りスムーズにするためのイノベーションを提供したいと考えています。先日、 Azure Migrate で Hyper-V 評価がサポートされるようになりました。また、 Azure SQL Database マネージド インスタンス の一般提供を開始しました。これを使用すると、 SQL サーバーを完全に管理された Azure サービスへ移行することができます。 Azure Database Migration Service 英語 の一環として、複数の新しい移行シナリオもサポートしました。さらに、 Azure Data Box Heavy と Azure Data Box の新しいサイズをリリースします。 Azure Data Box Heavy の 1 PB はプレビュー版で、 Azure Data Box の TB は一般提供でご利用いただけます。詳しくは Azure Migration Center をご覧ください。.

この多くの新機能によって、今後 Azure で実現できることが大幅に増えます。 Azure の目標は、お客様がビジネスでより多くの成果を上げられるようにすることです。そのためにマイクロソフトは、お客様のニーズに最適なクラウドを構築し、あらゆるワークロード、他社にはないハイブリッド機能、高コスト効率の機能、組み込みのセキュリティ機能や管理機能など、すべてをサポートするインフラストラクチャを提供します。. Ignite にご参加のお客様は、ぜひブースにお立ち寄りいただき、製品エキスパートに積極的に声をかけてみてください。インフラストラクチャ移行やアプリケーション最新化プロジェクトに関するアドバイスやお手伝いをさせていただきます。今後さらに多くの皆様にクラウドをご利用いただけることを楽しみにしています。. This post is authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at Microsoft.

What if we could infuse AI into the everyday tools we use, to delight everyday users? With just a little bit of creativity — and the power of the Microsoft AI platform behind us — it\’s now become easier than ever to create AI-enabled apps that can wow users. Introducing Snip Insights! An open source cross-platform AI tool for intelligent screen capture, Snip Insights is a step change in terms of how users can generate insights from their screen captures.

The initial prototype of Snip Insights, built for Windows OS and released at Microsoft Build in May, was created by Microsoft Garage interns based out of Vancouver, Canada. Our team at Microsoft AI Lab , in collaboration with the Microsoft AI CTO team, took Snip Insights to the next level by giving the tool an intuitive new user interface, adding cross-platform support for MacOS, Linux, and Windows , and offering free download and usage under the MSA license.

Snip Insights taps into Microsoft Azure Cognitive Services APIs and helps increase user productivity by automatically providing them with intelligent insights on their screen captures.

Snip Insights taps into cloud AI services and — depending on the image that was screen-captured — can convert it into translated text, automatically detect and tag images, and provide smart image suggestions that improve the user workflow.

This simple act of combining a familiar everyday desktop tool with Azure Cognitive Services has helped us create a one-stop shop for image insights. For instance, imagine that you\’ve scanned a textbook or work report.

Rather than having to manually type out the information in it, snipping it will now provide you with editable text, thanks to the power of OCR. Or perhaps you\’re scrolling through your social media feed and come across someone wearing a cool pair of shoes — you can now snip that to find out where to purchase them. Snip Insights can even help you identify famous people and popular landmarks. In the past, you would have to take the screen shot, save the picture, upload it to an image search engine, and then draw your conclusions and insights from there.

This is so much smarter, isn\’t it? Supported Platforms. Snip Insights is available on these three platforms:. Forms enables you to build native UIs for iOS, Android, macOS, Linux, and Windows from a single, shared codebase. You can dive into app development with Xamarin.

Forms by following our free self-guided learning from Xamarin University. Forms has preview support for GTK apps. Learn more here: Xamarin. Forms GTK. To add the keys to Snip Insights, a Microsoft Garage Project, start the application. Scroll down until you find the \”Cognitive Services, Enable AI assistance\” toggle, and toggle it to the On position.

You should now see the Insight Service Keys section. Use the following steps to create your LUIS App and retrieve an App ID:. You can now paste each key in the settings panel of the application. Remember to Click the Save button after entering all the keys. NOTE: For each key entered there is a corresponding Service Endpoint. There are some default endpoints included you can use these as an example but when you copy each key, also check and replace the Service Endpoint for each service you are using.

You will find the service endpoint for each Cognitive Service on the Overview Page. Remember to Click the Save button after updating all the Service Endpoints. In Summary. If you made it this far, and followed the above steps, you will have a fully working application to get started. We hope you have fun testing the project and thanks in advance for your contribution! You can find the code, solution development process and other details on GitHub. We hope this post inspires you get started with AI today, and motivates you to become an AI developer.

Announcing new open source contributions to the Apache Spark community for creating deep, distributed, object detectors — without a single human-generated label. This post is authored by members of the Microsoft ML for Apache Spark Team — Mark Hamilton, Minsoo Thigpen, Abhiram Eswaran, Ari Green, Courtney Cochrane, Janhavi Suresh Mahajan, Karthik Rajendran, Sudarshan Raghunathan, and Anand Raman.

In today\’s day and age, if data is the new oil, labelled data is the new gold. Here at Microsoft, we often spend a lot of our time thinking about \”Big Data\” issues, because these are the easiest to solve with deep learning. However, we often overlook the much more ubiquitous and difficult problems that have little to no data to train with. In this work we will show how, even without any data , one can create an object detector for almost anything found on the web.

This effectively bypasses the costly and resource intensive processes of curating datasets and hiring human labelers, allowing you to jump directly to intelligent models for classification and object detection completely in sillico.

We apply this technique to help monitor and protect the endangered population of snow leopards. We illustrate how to use these capabilities using the Snow Leopard Conservation use case, where machine learning is a key ingredient towards building powerful image classification models for identifying snow leopards from images.

Use Case — The Challenges of Snow Leopard Conservation. Snow leopards are facing a crisis. Their numbers are dwindling as a result of poaching and mining, yet little is known about how to best protect them.

Part of the challenge is that there are only about four thousand to seven thousand individual animals within a potential 1. In addition, Snow Leopard territory is in some of the most remote, rugged mountain ranges of central Asia, making it near impossible to get there without backpacking equipment. Figure 1: Our team\’s second flat tire on the way to snow leopard territory. To truly understand the snow leopard and what influences its survival rates, we need more data.

To this end, we have teamed up with the Snow Leopard Trust to help them gather and understand snow leopard data. The Trust can use this information to establish new protected areas and improve their community-based conservation efforts. However, the problem with camera-trap data is that the biologists must sort through all the images to distinguish photos of snow leopards and their prey from photos which have neither.

In addition, data collection practices have changed over the years. We have worked to help automate the Trust\’s snow leopard detection pipeline with Microsoft Machine Learning for Apache Spark MMLSpark. This includes both classifying snow leopard images, as well as extracting detected leopards to identify and match to a large database of known leopard individuals. Step 1: Gathering Data. Gathering data is often the hardest part of the machine learning workflow.

Without a large, high-quality dataset, a project is likely never to get off the ground. However, for many tasks, creating a dataset is incredibly difficult, time consuming, or downright impossible. We were fortunate to work with the Snow Leopard Trust who have already gathered 10 years of camera trap data and have meticulously labelled thousands of images.

However, the trust cannot release this data to the public, due to risks from poachers who use photo metadata to pinpoint leopards in the wild. As a result, if you are looking to create your own Snow Leopard analysis, you need to start from scratch. Figure 2: Examples of camera trap images from the Snow Leopard Trust\’s dataset. Announcing: Bing on Spark.

Confronted with the challenge of creating a snow leopard dataset from scratch, it\’s hard to know where to start. Amazingly, we don\’t need to go to Kyrgyzstan and set up a network of motion sensitive cameras. We already have access to one of the richest sources of human knowledge on the planet — the internet.

The tools that we have created over the past two decades that index the internet\’s content not only help humans learn about the world but can also help the algorithms we create do the same. Today we are releasing an integration between the Azure Cognitive Services and Apache Spark that enables querying Bing and many other intelligent services at massive scales.

This integration is part of the Microsoft ML for Apache Spark MMLSpark open source project. The Cognitive Services on Spark make it easy to integrate intelligence into your existing Spark and SQL workflows on any cluster using Python, Scala, Java, or R.

Under the hood, each Cognitive Service on Spark leverages Spark\’s massive parallelism to send streams of requests up to the cloud. In addition, the integration between SparkML and the Cognitive Services makes it easy to compose services with other models from the SparkML, CNTK, TensorFlow, and LightGBM ecosystems.

Figure 3: Results for Bing snow leopard image search. We can use Bing on Spark to quickly create our own machine learning datasets featuring anything we can find online. To create a custom snow leopard dataset takes only two distributed queries. The first query creates the \”positive class\” by pulling the first 80 pages of the \”snow leopard\” image results.

The second query creates the \”negative class\” to compare our leopards against. We can perform this search in two different ways, and we plan to explore them both in upcoming posts. Our first option is to search for images that would look like the kinds of images we will be getting out in the wild, such as empty mountainsides, mountain goats, foxes, grass, etc.

Our second option draws inspiration from Noise Contrastive Estimation , a mathematical technique used frequently in the Word Embedding literature. The basic idea behind noise contrastive estimation is to classify our snow leopards against a large and diverse dataset of random images. Our algorithm should not only be able to tell a snow leopard from an empty photo, but from a wide variety of other objects in the visual world.

Unfortunately, Bing Images does not have a random image API we could use to make this dataset. Instead, we can use random queries as a surrogate for random sampling from Bing.

Generating thousands of random queries is surprisingly easy with one of the multitude of online random word generators. Once we generate our words, we just need to load them into a distributed Spark DataFrame and pass them to Bing Image Search on Spark to grab the first 10 images for each random query. With these two datasets in hand, we can add labels, stitch them together, dedupe, and download the image bytes to the cluster.

SparkSQL parallelizes this process and can speed up the download by orders of magnitude. It only takes a few seconds on a large Azure Databricks cluster to pull thousands of images from around the world. Additionally, once the images are downloaded, we can easily preprocess and manipulate them with tools like OpenCV on Spark.

Figure 4: Diagram showing how to create a labelled dataset for snow leopard classification using Bing on Spark. Step 2: Creating a Deep Learning Classifier. Now that we have a labelled dataset, we can begin thinking about our model.

Convolutional neural networks CNNs are today\’s state-of-the-art statistical models for image analysis. They appear in everything from driverless cars, facial recognition systems, and image search engines.

To build our deep convolution network, we used MMLSpark , which provides easy-to-use distributed deep learning with the Microsoft Cognitive Toolkit on Spark. MMLSpark makes it especially easy to perform distributed transfer learning, a deep learning technique that mirrors how humans learn new tasks. When we learn something new, like classifying snow leopards, we don\’t start by re-wiring our entire brain. Instead, we rely on a wealth of prior knowledge gained over our lifetimes.

We only need a few examples, and we quickly become high accuracy snow leopard detectors. Amazingly transfer learning creates networks with similar behavior. We begin by using a Deep Residual Network that has been trained on millions of generic images.

Next, we cut off a few layers of this network and replace them with a SparkML model, like Logistic Regression, to learn a final mapping from deep features to snow leopard probabilities. As a result, our model leverages its previous knowledge in the form of intelligent features and can adapt itself to the task at hand with the final SparkML Model. Figure 5 shows a schematic of this architecture.

With MMLSpark, it\’s also easy to add improvements to this basic architecture like dataset augmentation, class balancing, quantile regression with LightGBM on Spark , and ensembling.

To learn more, explore our journal paper on this work, or try the example on our website. It\’s important to remember that our algorithm can learn from data sourced entirely from Bing. It did not need hand labeled data, and this method is applicable to almost any domain where an image search engine can find your images of interest. Figure 5: A diagram of transfer learning with ResNet50 on Spark. Step 3: Creating an Object Detection Dataset with Distributed Model Interpretability.

At this point, we have shown how to create a deep image classification system that leverages Bing to eliminate the need for labelled data. Classification systems are incredibly useful for counting the number of sightings.

However, classifiers tell us nothing about where the leopard is in the image, they only return a probability that a leopard is in an image. What might seem like a subtle distinction, can really make a difference in an end to end application. For example, knowing where the leopard is can help humans quickly determine whether the label is correct. It can also be helpful for situations where there might be more than one leopard in the frame.

Most importantly for this work, to understand how many individual leopards remain in the wild, we need to cross match individual leopards across several cameras and locations. The first step in this process is cropping the leopard photos so that we can use wildlife matching algorithms like HotSpotter. Ordinarily, we would need labels to train an object detector, aka painstakingly drawn bounding boxes around each leopard image.

We could then train an object detection network learn to reproduce these labels. Unfortunately, the images we pull from Bing have no such bounding boxes attached to them, making this task seem impossible. At this point we are so close, yet so far.

We can create a system to determine whether a leopard is in the photo, but not where the leopard is. Thankfully, our bag of machine learning tricks is not yet empty. It would be preposterous if our deep network could not locate the leopard. How could anything reliably know that there is a snow leopard in the photo without seeing it directly?

Sure, the algorithm could focus on aggregate image statistics like the background or the lighting to make an educated guess, but a good leopard detector should know a leopard when it sees it. If our model understands and uses this information, the question is \”How do we peer into our model\’s mind to extract this information? Thankfully, Marco Tulio Ribeiro and a team of researchers at the University of Washington have created an method called LIME Local Interpretable Model Agnostic Explanations , for explaining the classifications of any image classifier.

This method allows us to ask our classifier a series of questions, that when studied in aggregate, will tell us where the classifier is looking.

What\’s most exciting about this method, is that it makes no assumptions about the kind of model under investigation. You can explain your own deep network, a proprietary model like those found in the Microsoft cognitive services, or even a very patient human classifier.

This makes it widely applicable not just across models, but also across domains. Figure 6: Diagram showing the process for interpreting an image classifier. Figure 6 shows a visual representation of the LIME process.

First, we will take our original image, and break it into \”interpretable components\” called \”superpixels\”. More specifically, superpixels are clusters of pixels that groups pixels that have a similar color and location together. We then take our original image and randomly perturb it by \”turning off\” random superpixels. This results in thousands of new images which have parts of the leopard obscured. We can then feed these perturbed images through our deep network to see how our perturbations affect our classification probabilities.

These fluctuations in model probabilities help point us to the superpixels of the image that are most important for the classification.

The learned linear model weights then show us which superpixels are important to our classifier. To extract an explanation, we just need to look at the most important superpixels. LIME gives us a way to peer into our model and determine the exact pixels it is leveraging to make its predictions. For our leopard classifier, these pixels often directly highlight the leopard in the frame.

This not only gives us confidence in our model, but also providing us with a way to generate richer labels. LIME allows us to refine our classifications into bounding boxes for object detection by drawing rectangles around the important superpixels.

From our experiments, the results were strikingly close to what a human would draw around the leopard. Figure 7: LIME pixels tracking a leopard as it moves through the mountains.

Announcing: LIME on Spark. LIME has amazing potential to help users understand their models and even automatically create object detection datasets. However, LIME\’s major drawback is its steep computational cost. To create an interpretation for just one image, we need to sample thousands of perturbed images, pass them all through our network, and then train a linear model on the results. If it takes 1 hour to evaluate your model on a dataset, then it could take at least 50 days of computation to convert these predictions to interpretations.

To help make this process feasible for large datasets, we are releasing a distributed implementation of LIME as part of MMLSpark. This will enable users to quickly interpret any SparkML image classifier, including those backed by deep network frameworks like CNTK or TensorFlow. This helps make complex workloads like the one described, possible in only a few lines of MMLSpark code. If you would like to try the code, please see our example notebook for LIME on Spark.

Figure 8: Left: Outline of most important LIME superpixels. Right: example of human-labeled bounding box blue versus the LIME output bounding box yellow. Step 4: Transferring LIME\’s Knowledge into a Deep Object Detector. By combining our deep classifier with LIME, we have created a dataset of leopard bounding boxes.

Furthermore, we accomplished this without having to manually classify or labelling any images with bounding boxes. Bing Images, Transfer Learning, and LIME have done all the hard work for us. We can now use this labelled dataset to learn a dedicated deep object detector capable of approximating LIME\’s outputs at a x speedup. Finally, we can deploy this fast object detector as a web service, phone app, or real-time streaming application for the Snow Leopard Trust to use.

To build our Object Detector, we used the TensorFlow Object Detection API. We again used deep transfer learning to fine-tune a pre-trained Faster-RCNN object detector. This detector was pre-trained on the Microsoft Common Objects in Context COCO object detection dataset.

Just like transfer learning for deep image classifiers, working with an already intelligent object detector dramatically improves performance compared to learning from scratch.

In our analysis we optimized for accuracy, so we decided to use a Faster R-CNN network with an Inception Resnet v2. Source: Google Research. We found that Faster R-CNN was able to reliably reproduce LIME\’s outputs in a fraction of the time. Figure 10 shows several standard images from the Snow Leopard Trust\’s dataset. On these images, Faster R-CNN\’s outputs directly capture the leopard in the frame and match near perfectly with human curated labels.

Figure A comparison of human labeled images left and the outputs of the final trained Faster-RCNN on LIME predictions right. Figure A comparison of difficult human labeled images left and the outputs of the final trained Faster-RCNN on LIME predictions right. However, some images still pose challenges to this method. In Figure 11, we examine several mistakes made by the object detector. In the top image, there are two discernable leopards in the frame, however Faster R-CNN is only able to detect the larger leopard.

This is due to the method used to convert LIME outputs to bounding boxes. More specifically, we use a simple method that bounds all selected superpixels with a single rectangle. As a result, our bounding box dataset has at most one box per image. To refine this procedure, one could potentially cluster the superpixels to identify if there are more than one object in the frame, then draw the bounding boxes.

Furthermore, some leopards are difficult to spot due to their camouflage and they slip by the detector. Part of this affect might be due to anthropic bias in Bing Search. Namely, Bing Image Search returns only the clearest pictures of leopards and these photos are much easier than your average camera trap photo.

To mitigate this effect, one could engage in rounds of hard negative mining, augment the Bing data with hard to see leopards, and upweight those examples which show difficult to spot leopards. Step 5: Deployment as a Web Service.

The final stage in our project is to deploy our trained object detector so that the Snow Leopard trust can get model predictions from anywhere in the world.

Announcing: Sub-millisecond Latency with Spark Serving. Today we are excited to announce a new platform for deploying Spark Computations as distributed web services. This framework, called Spark Serving , dramatically simplifies the serving process in Python, Scala, Java and R.

It supplies ultra-low latency services backed by a distributed and fault-tolerant Spark Cluster. Under the hood, Spark Serving takes care of spinning up and managing web services on each node of your Spark cluster. As part of the release of MMLSpark v0. Figure Spark Serving latency comparison. We can use this framework to take our deep object detector trained with Horovod on Spark , and deploy it with only a few lines of code.

To try deploying a SparkML model as a web service for yourself, please see our notebook example. Future Work. The next step of this project is to use this leopard detector to create a global database of individual snow leopards and their sightings across locations.

We plan to use a tool called HotSpotter to automatically identify individual leopards using their uniquely patterned spotted fur. With this information, researchers at the Snow Leopard Trust can get a much better sense of leopard behavior, habitat, and movement.

Furthermore, identifying individual leopards helps researcher understand population numbers, which are critical for justifying Snow Leopard protections. Through this project we have seen how new open source computing tools such as the Cognitive Services on Spark, Deep Transfer Learning, Distributed Model Interpretability, and the TensorFlow Object Detection API can work together to pull an domain specific object detector directly from Bing. We have also released three new software suites: The Cognitive Services on Spark, Distributed Model Interpretability, and Spark Serving, to make this analysis simple and performant on Spark Clusters like Azure Databricks.

To recap, our analysis consisted of the following main steps:. Here is a graphical representation of this analysis:. Figure Overview of the full architecture described in this blog post. Using Microsoft ML for Apache Spark , users can easily follow in our footsteps and repeat this analysis with their own custom data or Bing queries. We have published this work in the open source and invite others to try it for themselves, give feedback, and help us advance the field of distributed unsupervised learning.

We have applied this method to help protect and monitor the endangered snow leopard population, but we made no assumptions throughout this blog on the type of data used.

Within a few hours we were able to modify this workflow to create a gas station fire detection network for Shell Energy with similar success. Mark Hamilton, for the MMLSpark Team. First, we need to understand the Powershell cmdlets that we will use. There are two of them: Get-NetTCPSetting and Set-NetTCPsetting. Go ahead. Open a powershell window, type the cmdlet and pipe it through the Select command as shown in the example. You should see something like this:. Why are those two templates in strikethrough font?

The Automatic template is used for automatically switching between Internet and Datacenter templates. The Compat template is only for legacy applications and is not recommended for use with modern apps. Now we are down to four templates and this is getting closer to simplicity!

Figure 1 — TCP Template SettingNames. Referring to Figure 1 we see that there are really only 2 templates that can be customizable or not. The Internet template is used for connections with an RTT of more than 10 ms and the Datacenter template is used for connections with an RTT of 10 ms or less.

But, just for information sake the Automatic template is taking the initial RTT as measured by the TCP connection handshake and applying the appropriate template to the TCP connection. The Datacenter template is designed for low-latency LAN environments and the Internet template is designed for higher latency WAN environments. Look at all those settings you can tune! That is enough to make an uber geek giggle with joy!

Use Set-NetTCPSetting to change things. Like this:. Fair warning! If you get into trouble and want to reset to default:. Have fun and happy TCP tuning! Updates for Azure Service Fabric, a cloud based microservices platform that allows you to build, deploy, and operate mission critical microservices applications at scale, are now available.

Additional updates simplify the service-to-service communication, such as DNS resolution for stateful services, a new layer 7 gateway based on Envoy, and network isolation per application. Learn more about these new features, other updates, and the rest of the announcements. Azure Monitor insights for resource groups is now in preview. This new experience offers the best of Azure Monitor scoped to an individual resource group, reducing the time it takes to detect and remediate production issues.

Learn more about this new capability. The Azure engineering team is bringing increased native support to Azure for customers using Ansible.

Ansible 2. For more information, please visit the Ansible on Azure documentation hub. Manage the Azure SQL information protection policy for your entire tenant centrally within Azure Security Center. This capability provides flexibility and control over sensitive data in your systems and enables you to align the sensitivity labels and classification classes to your organizational needs, helping you meet compliance requirements such as the General Data Protection Regulation GDPR. Learn more about information protection policy management in Azure Security Center.

Azure SQL Database reserved capacity is now available for SQL Database managed instance. This new pricing option saves you up to 33 percent compared to license-included pricing by prepaying for your SQL Database vCores for a one-year or three-year term.

Save up to 80 percent when you combine reserved capacity savings with Azure Hybrid Benefit for SQL Server. Improve budgeting and forecasting with a single upfront payment, making it easy to calculate your investments. See our pricing page and documentation for more information.

Data Migration Assistant: Support for Azure SQL Database Managed Instance now in preview. Now in preview, Data Migration Assistant provides support to help you to migrate on-premises SQL Server databases to Azure SQL Database Managed Instance. Data Migration Assistant detects compatibility and feature parity issues that can impact database functionality in target Azure SQL Database Managed Instance databases.

Learn more about Data Migration Assistant. This post was authored by Garrett Watumull, PM on the Windows Server team at Microsoft. Follow him GarrettWatumull on Twitter. In Windows Server , we introduced System Insights , a new predictive analytics feature for Windows Server. System Insights ships with four default capabilities designed to help you proactively and efficiently forecast resource consumption.

It collects historical usage data and implements robust data analytics to accurately predict resource usage, without requiring you to write any scripts or create custom visualizations. System Insights is designed to run on all Windows Server instances, across physical and guest instances, across hypervisors, and across clouds.

Because most Windows Server instances are unclustered, we focused on implementing storage forecasting capabilities for local storage – the volume consumption forecasting capability predicts storage consumption for local volumes, and the total storage consumption forecasting capability predicts storage consumption across all local drives.

After hearing your feedback, however, we realized we needed to extend this functionality to clustered storage. And with the latest Windows Admin Center and Windows Server GA releases, we\’re excited to announce support for forecasting on clustered storage.

Cluster administrators can now use System Insights to forecast clustered storage consumption. When you install System Insights on a failover cluster, the default behavior of System Insights remains unchanged – the storage capabilities only analyze local volumes and disks.

You can, however, easily enable forecasting on clustered storage, and System Insights will immediately start collecting all accessible clustered storage information:. Once enough data has been collected, System Insights will start forecasting on clustered storage data.

Lastly, before describing how to enable this functionality, there are a couple last things to point out:. Windows Admin Center provides a simple, straightforward method to enable forecasting on clustered storage. Clicking Install turns on data collection and forecasting for clustered storage.

Alternatively, you can also use the new Clustered storage button to adjust the clustered storage forecasting settings. This button is now visible if failover clustering is enabled on a server:. Once you click on Clustered storage, you can adjust the data collection settings, as well as the specific forecasting behavior of the volume and the total storage consumption capabilities. For each storage forecasting capability, you can specify local or clustered storage predictions:.

For those of you looking to use PowerShell instead, we\’ve exposed three registry keys to enable this functionality. Together, these help you manage clustered storage data collection, volume forecasting behavior, and total storage forecasting behavior:. To adjust the behavior of the total storage consumption capability, use the following registry key:.

You can use the New-ItemProperty or the Set-ItemProperty cmdlets to configure these registry keys. For example:. We\’re really excited to introduce this new functionality in System Insights and Windows Admin Center. With the latest releases, cluster users can now use System Insights to proactively predict clustered storage consumption, and these settings can be managed both in Windows Admin Center and PowerShell. Please keep providing feedback, so we can keep adding new functionality to System Insights that\’s relevant to you:.

This post is the third part of a series on the \”Check Permissions\” function. It\’s focused on Trusted Provider authentication aka: SAML-claims. The way \”Check Permissions\” works varies by authentication method.

For Windows or FBA auth, see my other posts:. Please see the \”Why should you care? That should provide some good background on External Tokens and interactive vs non-interactive refresh of the External Token, which should help explain why \”Check Permissions\” failures can be intermittent when the user gets their permission via group membership role claim.

With Trusted Provider auth, the \”Check Permissions\” functionality is completely dependent on your Custom Claims Provider CCP. This claims augmentation is something that is implemented within the code of the CCP. That\’s important to keep in mind. It\’s quite possible that your CCP was not written to do augmentation, in which case there\’s nothing you can do in SharePoint to fix it. The CCP would have to be updated to support this functionality. In order for this to work at all, you need to be passing group membership as a role claim, and you also need a Custom Claims Provider.

Get-SPTrustedIdentityTokenIssuer select claimtypeinformation -ExpandProperty claimtypeinformation. I hope so. If not, your People Picker functionality on your Trusted Provider auth web applications is probably not great.

Get-SPTrustedIdentityTokenIssuer select name, claimprovidername. My Custom Claims Provider is LDAPCP:. This is going to depend on your Custom Claims Provider.

As I mentioned earlier, the CCP is wholly responsible for providing this functionality. If it\’s not written to do this correctly or maybe not at all , then you\’ll need to be in touch with the developers. LDAPCP is an excellent custom claims provider. It covers most LDAP people picking scenarios out-of-box, and since it\’s an open-source project, you can tweak it to meet your needs.

I\’m going to go into some depth for troubleshooting \”Check Permissions\” with LDAPCP because: 1.

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