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  • Windows Azure: Import/Export Hard Drives, VM ACLs, Web Sockets, Remote Debugging, Continuous Delivery, New Relic, Billing Alerts and More

    - by ScottGu
    Two weeks ago we released a giant set of improvements to Windows Azure, as well as a significant update of the Windows Azure SDK. This morning we released another massive set of enhancements to Windows Azure.  Today’s new capabilities include: Storage: Import/Export Hard Disk Drives to your Storage Accounts HDInsight: General Availability of our Hadoop Service in the cloud Virtual Machines: New VM Gallery, ACL support for VIPs Web Sites: WebSocket and Remote Debugging Support Notification Hubs: Segmented customer push notification support with tag expressions TFS & GIT: Continuous Delivery Support for Web Sites + Cloud Services Developer Analytics: New Relic support for Web Sites + Mobile Services Service Bus: Support for partitioned queues and topics Billing: New Billing Alert Service that sends emails notifications when your bill hits a threshold you define All of these improvements are now available to use immediately (note that some features are still in preview).  Below are more details about them. Storage: Import/Export Hard Disk Drives to Windows Azure I am excited to announce the preview of our new Windows Azure Import/Export Service! The Windows Azure Import/Export Service enables you to move large amounts of on-premises data into and out of your Windows Azure Storage accounts. It does this by enabling you to securely ship hard disk drives directly to our Windows Azure data centers. Once we receive the drives we’ll automatically transfer the data to or from your Windows Azure Storage account.  This enables you to import or export massive amounts of data more quickly and cost effectively (and not be constrained by available network bandwidth). Encrypted Transport Our Import/Export service provides built-in support for BitLocker disk encryption – which enables you to securely encrypt data on the hard drives before you send it, and not have to worry about it being compromised even if the disk is lost/stolen in transit (since the content on the transported hard drives is completely encrypted and you are the only one who has the key to it).  The drive preparation tool we are shipping today makes setting up bitlocker encryption on these hard drives easy. How to Import/Export your first Hard Drive of Data You can read our Getting Started Guide to learn more about how to begin using the import/export service.  You can create import and export jobs via the Windows Azure Management Portal as well as programmatically using our Server Management APIs. It is really easy to create a new import or export job using the Windows Azure Management Portal.  Simply navigate to a Windows Azure storage account, and then click the new Import/Export tab now available within it (note: if you don’t have this tab make sure to sign-up for the Import/Export preview): Then click the “Create Import Job” or “Create Export Job” commands at the bottom of it.  This will launch a wizard that easily walks you through the steps required: For more comprehensive information about Import/Export, refer to Windows Azure Storage team blog.  You can also send questions and comments to the [email protected] email address. We think you’ll find this new service makes it much easier to move data into and out of Windows Azure, and it will dramatically cut down the network bandwidth required when working on large data migration projects.  We hope you like it. HDInsight: 100% Compatible Hadoop Service in the Cloud Last week we announced the general availability release of Windows Azure HDInsight. HDInsight is a 100% compatible Hadoop service that allows you to easily provision and manage Hadoop clusters for big data processing in Windows Azure.  This release is now live in production, backed by an enterprise SLA, supported 24x7 by Microsoft Support, and is ready to use for production scenarios. HDInsight allows you to use Apache Hadoop tools, such as Pig and Hive, to process large amounts of data in Windows Azure Blob Storage. Because data is stored in Windows Azure Blob Storage, you can choose to dynamically create Hadoop clusters only when you need them, and then shut them down when they are no longer required (since you pay only for the time the Hadoop cluster instances are running this provides a super cost effective way to use them).  You can create Hadoop clusters using either the Windows Azure Management Portal (see below) or using our PowerShell and Cross Platform Command line tools: The import/export hard drive support that came out today is a perfect companion service to use with HDInsight – the combination allows you to easily ingest, process and optionally export a limitless amount of data.  We’ve also integrated HDInsight with our Business Intelligence tools, so users can leverage familiar tools like Excel in order to analyze the output of jobs.  You can find out more about how to get started with HDInsight here. Virtual Machines: VM Gallery Enhancements Today’s update of Windows Azure brings with it a new Virtual Machine gallery that you can use to create new VMs in the cloud.  You can launch the gallery by doing New->Compute->Virtual Machine->From Gallery within the Windows Azure Management Portal: The new Virtual Machine Gallery includes some nice enhancements that make it even easier to use: Search: You can now easily search and filter images using the search box in the top-right of the dialog.  For example, simply type “SQL” and we’ll filter to show those images in the gallery that contain that substring. Category Tree-view: Each month we add more built-in VM images to the gallery.  You can continue to browse these using the “All” view within the VM Gallery – or now quickly filter them using the category tree-view on the left-hand side of the dialog.  For example, by selecting “Oracle” in the tree-view you can now quickly filter to see the official Oracle supplied images. MSDN and Supported checkboxes: With today’s update we are also introducing filters that makes it easy to filter out types of images that you may not be interested in. The first checkbox is MSDN: using this filter you can exclude any image that is not part of the Windows Azure benefits for MSDN subscribers (which have highly discounted pricing - you can learn more about the MSDN pricing here). The second checkbox is Supported: this filter will exclude any image that contains prerelease software, so you can feel confident that the software you choose to deploy is fully supported by Windows Azure and our partners. Sort options: We sort gallery images by what we think customers are most interested in, but sometimes you might want to sort using different views. So we’re providing some additional sort options, like “Newest,” to customize the image list for what suits you best. Pricing information: We now provide additional pricing information about images and options on how to cost effectively run them directly within the VM Gallery. The above improvements make it even easier to use the VM Gallery and quickly create launch and run Virtual Machines in the cloud. Virtual Machines: ACL Support for VIPs A few months ago we exposed the ability to configure Access Control Lists (ACLs) for Virtual Machines using Windows PowerShell cmdlets and our Service Management API. With today’s release, you can now configure VM ACLs using the Windows Azure Management Portal as well. You can now do this by clicking the new Manage ACL command in the Endpoints tab of a virtual machine instance: This will enable you to configure an ordered list of permit and deny rules to scope the traffic that can access your VM’s network endpoints. For example, if you were on a virtual network, you could limit RDP access to a Windows Azure virtual machine to only a few computers attached to your enterprise. Or if you weren’t on a virtual network you could alternatively limit traffic from public IPs that can access your workloads: Here is the default behaviors for ACLs in Windows Azure: By default (i.e. no rules specified), all traffic is permitted. When using only Permit rules, all other traffic is denied. When using only Deny rules, all other traffic is permitted. When there is a combination of Permit and Deny rules, all other traffic is denied. Lastly, remember that configuring endpoints does not automatically configure them within the VM if it also has firewall rules enabled at the OS level.  So if you create an endpoint using the Windows Azure Management Portal, Windows PowerShell, or REST API, be sure to also configure your guest VM firewall appropriately as well. Web Sites: Web Sockets Support With today’s release you can now use Web Sockets with Windows Azure Web Sites.  This feature enables you to easily integrate real-time communication scenarios within your web based applications, and is available at no extra charge (it even works with the free tier).  Higher level programming libraries like SignalR and socket.io are also now supported with it. You can enable Web Sockets support on a web site by navigating to the Configure tab of a Web Site, and by toggling Web Sockets support to “on”: Once Web Sockets is enabled you can start to integrate some really cool scenarios into your web applications.  Check out the new SignalR documentation hub on www.asp.net to learn more about some of the awesome scenarios you can do with it. Web Sites: Remote Debugging Support The Windows Azure SDK 2.2 we released two weeks ago introduced remote debugging support for Windows Azure Cloud Services. With today’s Windows Azure release we are extending this remote debugging support to also work with Windows Azure Web Sites. With live, remote debugging support inside of Visual Studio, you are able to have more visibility than ever before into how your code is operating live in Windows Azure. It is now super easy to attach the debugger and quickly see what is going on with your application in the cloud. Remote Debugging of a Windows Azure Web Site using VS 2013 Enabling the remote debugging of a Windows Azure Web Site using VS 2013 is really easy.  Start by opening up your web application’s project within Visual Studio. Then navigate to the “Server Explorer” tab within Visual Studio, and click on the deployed web-site you want to debug that is running within Windows Azure using the Windows Azure->Web Sites node in the Server Explorer.  Then right-click and choose the “Attach Debugger” option on it: When you do this Visual Studio will remotely attach the debugger to the Web Site running within Windows Azure.  The debugger will then stop the web site’s execution when it hits any break points that you have set within your web application’s project inside Visual Studio.  For example, below I set a breakpoint on the “ViewBag.Message” assignment statement within the HomeController of the standard ASP.NET MVC project template.  When I hit refresh on the “About” page of the web site within the browser, the breakpoint was triggered and I am now able to debug the app remotely using Visual Studio: Note above how we can debug variables (including autos/watchlist/etc), as well as use the Immediate and Command Windows. In the debug session above I used the Immediate Window to explore some of the request object state, as well as to dynamically change the ViewBag.Message property.  When we click the the “Continue” button (or press F5) the app will continue execution and the Web Site will render the content back to the browser.  This makes it super easy to debug web apps remotely. Tips for Better Debugging To get the best experience while debugging, we recommend publishing your site using the Debug configuration within Visual Studio’s Web Publish dialog. This will ensure that debug symbol information is uploaded to the Web Site which will enable a richer debug experience within Visual Studio.  You can find this option on the Web Publish dialog on the Settings tab: When you ultimately deploy/run the application in production we recommend using the “Release” configuration setting – the release configuration is memory optimized and will provide the best production performance.  To learn more about diagnosing and debugging Windows Azure Web Sites read our new Troubleshooting Windows Azure Web Sites in Visual Studio guide. Notification Hubs: Segmented Push Notification support with tag expressions In August we announced the General Availability of Windows Azure Notification Hubs - a powerful Mobile Push Notifications service that makes it easy to send high volume push notifications with low latency from any mobile app back-end.  Notification hubs can be used with any mobile app back-end (including ones built using our Mobile Services capability) and can also be used with back-ends that run in the cloud as well as on-premises. Beginning with the initial release, Notification Hubs allowed developers to send personalized push notifications to both individual users as well as groups of users by interest, by associating their devices with tags representing the logical target of the notification. For example, by registering all devices of customers interested in a favorite MLB team with a corresponding tag, it is possible to broadcast one message to millions of Boston Red Sox fans and another message to millions of St. Louis Cardinals fans with a single API call respectively. New support for using tag expressions to enable advanced customer segmentation With today’s release we are adding support for even more advanced customer targeting.  You can now identify customers that you want to send push notifications to by defining rich tag expressions. With tag expressions, you can now not only broadcast notifications to Boston Red Sox fans, but take that segmenting a step farther and reach more granular segments. This opens up a variety of scenarios, for example: Offers based on multiple preferences—e.g. send a game day vegetarian special to users tagged as both a Boston Red Sox fan AND a vegetarian Push content to multiple segments in a single message—e.g. rain delay information only to users who are tagged as either a Boston Red Sox fan OR a St. Louis Cardinal fan Avoid presenting subsets of a segment with irrelevant content—e.g. season ticket availability reminder to users who are tagged as a Boston Red Sox fan but NOT also a season ticket holder To illustrate with code, consider a restaurant chain app that sends an offer related to a Red Sox vs Cardinals game for users in Boston. Devices can be tagged by your app with location tags (e.g. “Loc:Boston”) and interest tags (e.g. “Follows:RedSox”, “Follows:Cardinals”), and then a notification can be sent by your back-end to “(Follows:RedSox || Follows:Cardinals) && Loc:Boston” in order to deliver an offer to all devices in Boston that follow either the RedSox or the Cardinals. This can be done directly in your server backend send logic using the code below: var notification = new WindowsNotification(messagePayload); hub.SendNotificationAsync(notification, "(Follows:RedSox || Follows:Cardinals) && Loc:Boston"); In your expressions you can use all Boolean operators: AND (&&), OR (||), and NOT (!).  Some other cool use cases for tag expressions that are now supported include: Social: To “all my group except me” - group:id && !user:id Events: Touchdown event is sent to everybody following either team or any of the players involved in the action: Followteam:A || Followteam:B || followplayer:1 || followplayer:2 … Hours: Send notifications at specific times. E.g. Tag devices with time zone and when it is 12pm in Seattle send to: GMT8 && follows:thaifood Versions and platforms: Send a reminder to people still using your first version for Android - version:1.0 && platform:Android For help on getting started with Notification Hubs, visit the Notification Hub documentation center.  Then download the latest NuGet package (or use the Notification Hubs REST APIs directly) to start sending push notifications using tag expressions.  They are really powerful and enable a bunch of great new scenarios. TFS & GIT: Continuous Delivery Support for Web Sites + Cloud Services With today’s Windows Azure release we are making it really easy to enable continuous delivery support with Windows Azure and Team Foundation Services.  Team Foundation Services is a cloud based offering from Microsoft that provides integrated source control (with both TFS and Git support), build server, test execution, collaboration tools, and agile planning support.  It makes it really easy to setup a team project (complete with automated builds and test runners) in the cloud, and it has really rich integration with Visual Studio. With today’s Windows Azure release it is now really easy to enable continuous delivery support with both TFS and Git based repositories hosted using Team Foundation Services.  This enables a workflow where when code is checked in, built successfully on an automated build server, and all tests pass on it – I can automatically have the app deployed on Windows Azure with zero manual intervention or work required. The below screen-shots demonstrate how to quickly setup a continuous delivery workflow to Windows Azure with a Git-based ASP.NET MVC project hosted using Team Foundation Services. Enabling Continuous Delivery to Windows Azure with Team Foundation Services The project I’m going to enable continuous delivery with is a simple ASP.NET MVC project whose source code I’m hosting using Team Foundation Services.  I did this by creating a “SimpleContinuousDeploymentTest” repository there using Git – and then used the new built-in Git tooling support within Visual Studio 2013 to push the source code to it.  Below is a screen-shot of the Git repository hosted within Team Foundation Services: I can access the repository within Visual Studio 2013 and easily make commits with it (as well as branch, merge and do other tasks).  Using VS 2013 I can also setup automated builds to take place in the cloud using Team Foundation Services every time someone checks in code to the repository: The cool thing about this is that I don’t have to buy or rent my own build server – Team Foundation Services automatically maintains its own build server farm and can automatically queue up a build for me (for free) every time someone checks in code using the above settings.  This build server (and automated testing) support now works with both TFS and Git based source control repositories. Connecting a Team Foundation Services project to Windows Azure Once I have a source repository hosted in Team Foundation Services with Automated Builds and Testing set up, I can then go even further and set it up so that it will be automatically deployed to Windows Azure when a source code commit is made to the repository (assuming the Build + Tests pass).  Enabling this is now really easy.  To set this up with a Windows Azure Web Site simply use the New->Compute->Web Site->Custom Create command inside the Windows Azure Management Portal.  This will create a dialog like below.  I gave the web site a name and then made sure the “Publish from source control” checkbox was selected: When we click next we’ll be prompted for the location of the source repository.  We’ll select “Team Foundation Services”: Once we do this we’ll be prompted for our Team Foundation Services account that our source repository is hosted under (in this case my TFS account is “scottguthrie”): When we click the “Authorize Now” button we’ll be prompted to give Windows Azure permissions to connect to the Team Foundation Services account.  Once we do this we’ll be prompted to pick the source repository we want to connect to.  Starting with today’s Windows Azure release you can now connect to both TFS and Git based source repositories.  This new support allows me to connect to the “SimpleContinuousDeploymentTest” respository we created earlier: Clicking the finish button will then create the Web Site with the continuous delivery hooks setup with Team Foundation Services.  Now every time someone pushes source control to the repository in Team Foundation Services, it will kick off an automated build, run all of the unit tests in the solution , and if they pass the app will be automatically deployed to our Web Site in Windows Azure.  You can monitor the history and status of these automated deployments using the Deployments tab within the Web Site: This enables a really slick continuous delivery workflow, and enables you to build and deploy apps in a really nice way. Developer Analytics: New Relic support for Web Sites + Mobile Services With today’s Windows Azure release we are making it really easy to enable Developer Analytics and Monitoring support with both Windows Azure Web Site and Windows Azure Mobile Services.  We are partnering with New Relic, who provide a great dev analytics and app performance monitoring offering, to enable this - and we have updated the Windows Azure Management Portal to make it really easy to configure. Enabling New Relic with a Windows Azure Web Site Enabling New Relic support with a Windows Azure Web Site is now really easy.  Simply navigate to the Configure tab of a Web Site and scroll down to the “developer analytics” section that is now within it: Clicking the “add-on” button will display some additional UI.  If you don’t already have a New Relic subscription, you can click the “view windows azure store” button to obtain a subscription (note: New Relic has a perpetually free tier so you can enable it even without paying anything): Clicking the “view windows azure store” button will launch the integrated Windows Azure Store experience we have within the Windows Azure Management Portal.  You can use this to browse from a variety of great add-on services – including New Relic: Select “New Relic” within the dialog above, then click the next button, and you’ll be able to choose which type of New Relic subscription you wish to purchase.  For this demo we’ll simply select the “Free Standard Version” – which does not cost anything and can be used forever:  Once we’ve signed-up for our New Relic subscription and added it to our Windows Azure account, we can go back to the Web Site’s configuration tab and choose to use the New Relic add-on with our Windows Azure Web Site.  We can do this by simply selecting it from the “add-on” dropdown (it is automatically populated within it once we have a New Relic subscription in our account): Clicking the “Save” button will then cause the Windows Azure Management Portal to automatically populate all of the needed New Relic configuration settings to our Web Site: Deploying the New Relic Agent as part of a Web Site The final step to enable developer analytics using New Relic is to add the New Relic runtime agent to our web app.  We can do this within Visual Studio by right-clicking on our web project and selecting the “Manage NuGet Packages” context menu: This will bring up the NuGet package manager.  You can search for “New Relic” within it to find the New Relic agent.  Note that there is both a 32-bit and 64-bit edition of it – make sure to install the version that matches how your Web Site is running within Windows Azure (note: you can configure your Web Site to run in either 32-bit or 64-bit mode using the Web Site’s “Configuration” tab within the Windows Azure Management Portal): Once we install the NuGet package we are all set to go.  We’ll simply re-publish the web site again to Windows Azure and New Relic will now automatically start monitoring the application Monitoring a Web Site using New Relic Now that the application has developer analytics support with New Relic enabled, we can launch the New Relic monitoring portal to start monitoring the health of it.  We can do this by clicking on the “Add Ons” tab in the left-hand side of the Windows Azure Management Portal.  Then select the New Relic add-on we signed-up for within it.  The Windows Azure Management Portal will provide some default information about the add-on when we do this.  Clicking the “Manage” button in the tray at the bottom will launch a new browser tab and single-sign us into the New Relic monitoring portal associated with our account: When we do this a new browser tab will launch with the New Relic admin tool loaded within it: We can now see insights into how our app is performing – without having to have written a single line of monitoring code.  The New Relic service provides a ton of great built-in monitoring features allowing us to quickly see: Performance times (including browser rendering speed) for the overall site and individual pages.  You can optionally set alert thresholds to trigger if the speed does not meet a threshold you specify. Information about where in the world your customers are hitting the site from (and how performance varies by region) Details on the latency performance of external services your web apps are using (for example: SQL, Storage, Twitter, etc) Error information including call stack details for exceptions that have occurred at runtime SQL Server profiling information – including which queries executed against your database and what their performance was And a whole bunch more… The cool thing about New Relic is that you don’t need to write monitoring code within your application to get all of the above reports (plus a lot more).  The New Relic agent automatically enables the CLR profiler within applications and automatically captures the information necessary to identify these.  This makes it super easy to get started and immediately have a rich developer analytics view for your solutions with very little effort. If you haven’t tried New Relic out yet with Windows Azure I recommend you do so – I think you’ll find it helps you build even better cloud applications.  Following the above steps will help you get started and deliver you a really good application monitoring solution in only minutes. Service Bus: Support for partitioned queues and topics With today’s release, we are enabling support within Service Bus for partitioned queues and topics. Enabling partitioning enables you to achieve a higher message throughput and better availability from your queues and topics. Higher message throughput is achieved by implementing multiple message brokers for each partitioned queue and topic.  The  multiple messaging stores will also provide higher availability. You can create a partitioned queue or topic by simply checking the Enable Partitioning option in the custom create wizard for a Queue or Topic: Read this article to learn more about partitioned queues and topics and how to take advantage of them today. Billing: New Billing Alert Service Today’s Windows Azure update enables a new Billing Alert Service Preview that enables you to get proactive email notifications when your Windows Azure bill goes above a certain monetary threshold that you configure.  This makes it easier to manage your bill and avoid potential surprises at the end of the month. With the Billing Alert Service Preview, you can now create email alerts to monitor and manage your monetary credits or your current bill total.  To set up an alert first sign-up for the free Billing Alert Service Preview.  Then visit the account management page, click on a subscription you have setup, and then navigate to the new Alerts tab that is available: The alerts tab allows you to setup email alerts that will be sent automatically once a certain threshold is hit.  For example, by clicking the “add alert” button above I can setup a rule to send myself email anytime my Windows Azure bill goes above $100 for the month: The Billing Alert Service will evolve to support additional aspects of your bill as well as support multiple forms of alerts such as SMS.  Try out the new Billing Alert Service Preview today and give us feedback. Summary Today’s Windows Azure release enables a ton of great new scenarios, and makes building applications hosted in the cloud even easier. If you don’t already have a Windows Azure account, you can sign-up for a free trial and start using all of the above features today.  Then visit the Windows Azure Developer Center to learn more about how to build apps with it. Hope this helps, Scott P.S. In addition to blogging, I am also now using Twitter for quick updates and to share links. Follow me at: twitter.com/scottgu

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  • 256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

    - by Alan Smith
    For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour.  This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue   static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005>     define wooden texture { noise surface { ambient 0.2  diffuse 0.7  specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1  lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7  } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } }   viewpoint {     from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60     resolution 640, 480 aspect 1.6 image_format 0 }       light <-10, 30, 20> light <-10, 30, -20>   object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden }   object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome }   After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue }   In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000   As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72   Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: ·         On-Premise o   Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o   Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o   Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o   Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. ·         Windows Azure o   Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment.   The architecture of each worker role is shown below.   The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" >   <WorkerRole name="CloudRayWorkerRole" vmsize="Small">     <Imports>     </Imports>     <ConfigurationSettings>       <Setting name="DataConnectionString" />     </ConfigurationSettings>     <LocalResources>       <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" />     </LocalResources>   </WorkerRole> </ServiceDefinition>     The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1.       The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2.       PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3.       DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4.       The JPG file is uploaded from local storage to the images blob container. 5.       A message is placed on the images queue to indicate a new image is available for download. 6.       The job message is deleted from the job queue. 7.       The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() {     // Set environment variables     string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation);     string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation);       LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder");     string localStorageRootPath = rayStorage.RootPath;       JobQueue jobQueue = new JobQueue("renderjobs");     JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs");     CloudRayBlob sceneBlob = new CloudRayBlob("scenes");     CloudRayBlob imageBlob = new CloudRayBlob("images");     RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource();       Frames = 0;       while (true)     {         // Get the render job from the queue         CloudQueueMessage jobMsg = jobQueue.Get();           if (jobMsg != null)         {             // Get the file details             string sceneFile = jobMsg.AsString;             string tgaFile = sceneFile.Replace(".pi", ".tga");             string jpgFile = sceneFile.Replace(".pi", ".jpg");               string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile);             string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile);             string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile);               // Copy the scene file to local storage             sceneBlob.DownloadFile(sceneFilePath);               // Run the ray tracer.             string polyrayArguments =                 string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath);             Process polyRayProcess = new Process();             polyRayProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath);             polyRayProcess.StartInfo.Arguments = polyrayArguments;             polyRayProcess.Start();             polyRayProcess.WaitForExit();               // Convert the image             string dtaArguments =                 string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath));             Process dtaProcess = new Process();             dtaProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath);             dtaProcess.StartInfo.Arguments = dtaArguments;             dtaProcess.Start();             dtaProcess.WaitForExit();               // Upload the image to blob storage             imageBlob.UploadFile(jpgFilePath);               // Add a download job.             downloadQueue.Add(jpgFile);               // Delete the render job message             jobQueue.Delete(jobMsg);               Frames++;         }         else         {             Thread.Sleep(1000);         }           // Log the worker role activity.         roleLifecycleDataSource.Alive             ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames);     } }     Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage.   public class RoleLifecycle : TableServiceEntity {     public string ServerName { get; set; }     public string Status { get; set; }     public DateTime StartTime { get; set; }     public DateTime EndTime { get; set; }     public long SecondsRunning { get; set; }     public DateTime LastActiveTime { get; set; }     public int Frames { get; set; }     public string Comment { get; set; }       public RoleLifecycle()     {     }       public RoleLifecycle(string roleName)     {         PartitionKey = roleName;         RowKey = Utils.GetAscendingRowKey();         Status = "Started";         StartTime = DateTime.UtcNow;         LastActiveTime = StartTime;         EndTime = StartTime;         SecondsRunning = 0;         Frames = 0;     } }     A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="16" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames.   Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation.   With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes.     At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances.   <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="256" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames.   Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds.   We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes.   The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each.   Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles   The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. ·         Windows Azure can provide massive compute power, on demand, in a matter of minutes. ·         The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. ·         Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. ·         No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. ·         Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. ·         Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. ·         Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. ·         Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. ·         If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. ·         Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. ·         Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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