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  • How can one use online backup with large amounts of static data?

    - by Billy ONeal
    I'd like to setup an offsite backup solution for about 500GB of data that's currently stored between my various machines. I don't care about data retention rates, as this is only a backup of, not primary storage, for my data. If the backup is stored on crappy non-redundant systems, that does not matter. The data set is almost entirely static, and mostly consists of things like installers for Visual Studio, and installer disk images for all of my games. I have found two services which meet most of this: Mozy Carbonite However, both services impose low bandwidth caps, on the order of 50kb/s, which prevent me from backing up a dataset of this size effectively (somewhere on the order of 6 weeks), despite the fact that I get multiple MB/s upload speeds everywhere else from this location. Carbonite has the additional problem that it tries to ignore pretty much every file in my backup set by default, because the files are mostly iso files and vmdk files, which aren't backed up by default. There are other services such as EC2 which don't have such bandwidth caps, but such services are typically stored in highly redundant servers, and therefore cost on the order of 10 cents/gb/month, which is insanely expensive for storage of this kind of data set. (At $50/month I could build my own NAS to hold the data which would pay for itself after ~2-3 months) (To be fair, they're offering quite a bit more service than I'm looking for at that price, such as offering public HTTP access to the data) Does anything exist meeting those requirements or am I basically hosed?

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  • Web browsing through SSH tunnel gets stuck/clogged

    - by endolith
    I use tools like Tunnelier to log into my home Tomato router through SSH, and then use it as a proxy for web browsing, tunnel for Remote Desktop/VNC, etc. Most days it works great, but some days every page I try to view gets stuck, like the tunnel is clogged. I load a web page and it seems to be loading, then stops, with the little loading icon spinning and nothing happening. I refresh the page, I reboot the router, I reboot the other computers on my home network and turn off any bandwidth-hogging services on them, I've turned on QoS on the router to prioritize SSH. I don't understand what's getting stuck. Rebooting or disconnecting/reconnecting the SSH tunnel improves responsiveness for a minute, but then it gets clogged again. It also seems to help if I don't do anything on the tunnel for a few minutes, then it will be responsive for a bit and then get clogged again. Trying to open a terminal console from Tunnelier is also unresponsive, so it's not just a web browsing problem. Likewise, connecting to http://192.168.1.1 in the browser (to the router's web config through its own tunnel) is also slow/laggy/halting. The realtime bandwidth reported by the router is nowhere near my DSL connection's limits, though it does show big spikes during the laggy times, and the connection is responsive when it shows low bandwidths. How do I troubleshoot something like this?

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  • openvpn port 53 bypasses allows restrictions ( find similar ports)

    - by user181216
    scenario of wifi : i'm using wifi in hostel which having cyberoam firewall and all the computer which uses that access point. that access point have following configuration default gateway : 192.168.100.1 primary dns server : 192.168.100.1 here, when i try to open a website the cyberoam firewall redirects the page to a login page (with correct login information, we can browse internet else not), and also website access and bandwidth limitations. once i've heard about pd-proxy which finds open port and tunnels through a port ( usually udp 53). using pd-proxy with UDP 53 port, i can browse internet without login, even bandwidth limit is bypassed !!! and another software called openvpn with connecting openvpn server through udp port 53 i can browse internet without even login into the cyberoam. both of softwares uses port 53, specially openvpn with port 53, now i've a VPS server in which i can install openvpn server and connect through the VPS server to browse internet. i know why that is happening because with pinging on some website(eb. google.com) it returns it's ip address that means it allows dns queries without login. but the problem is there is already DNS service is running on the VPS server on port 53. and i can only use 53 port to bypass the limitations as i think. and i can not run openvpn service on my VPS server on port 53. so how to scan the wifi for vulnerable ports like 53 so that i can figure out the magic port and start a openvpn service on VPS on the same port. ( i want to scan similar vulnerable ports like 53 on cyberoam in which the traffic can be tunneled, not want to scan services running on ports). improvement of the question with retags and edits are always welcomed... NOTE : all these are for Educational purpose only, i'm curious about network related knowledge.....

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  • openvpn port 53 bypasses allows restrictions ( find similar ports)

    - by user181216
    scenario of wifi : i'm using wifi in hostel which having cyberoam firewall and all the computer which uses that access point. that access point have following configuration default gateway : 192.168.100.1 primary dns server : 192.168.100.1 here, when i try to open a website the cyberoam firewall redirects the page to a login page (with correct login information, we can browse internet else not), and also website access and bandwidth limitations. once i've heard about pd-proxy which finds open port and tunnels through a port ( usually udp 53). using pd-proxy with UDP 53 port, i can browse internet without login, even bandwidth limit is bypassed !!! and another software called openvpn with connecting openvpn server through udp port 53 i can browse internet without even login into the cyberoam. both of softwares uses port 53, specially openvpn with port 53, now i've a VPS server in which i can install openvpn server and connect through the VPS server to browse internet. i know why that is happening because with pinging on some website(eb. google.com) it returns it's ip address that means it allows dns queries without login. but the problem is there is already DNS service is running on the VPS server on port 53. and i can only use 53 port to bypass the limitations as i think. and i can not run openvpn service on my VPS server on port 53. so how to scan the wifi for vulnerable ports like 53 so that i can figure out the magic port and start a openvpn service on VPS on the same port. ( i want to scan similar vulnerable ports like 53 on cyberoam in which the traffic can be tunneled, not want to scan services running on ports). improvement of the question with retags and edits are always welcomed... NOTE : all these are for Educational purpose only, i'm curious about network related knowledge.....

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  • torrent downloads not showing on Squid log

    - by noobroot
    hello, i have just a few months working as sysadmin, hence i still have lots to learn, first thing id like to do is as follows: We have an OpenBSD 4.5 box acting like firewall,dns,cache etc, the box has 2 network cards, one conected directly to the internet and the other to our switch, i used to work with sarg for the log analysis but then changed to the much faster free-sa. I use a daily free-sa report to check the bandwidth usage and report our top 5 bandwidth consumers (3 days a week being #1 and you will be buying the pizzas :D, we are a small company ~20 so we are very familiar). this was working really good until recently, one of us required to download some stuff via torrent (~3GB) and since the pizza rule is active for non-work related downloads, he told me (verified) that his download was indeed work related so i would dismiss that 3GB off his quota, but to my surprise the log didnt showed that 3GB, since his ip consumption was only around 290MB. More recently, since the FIFA world cup started, we know that some of the employees are watching the match's streaming, we know it and we dont care about it since, like already stated, we are a small company so we dont have restrictive policies, we all can chat, watch youtube, download anything we want BUT we are only allowed 300MB a day otherwise you'll get in the top5-pizza-board, anyway, that streaming consumption is also not showing in the free-sa reports. So my question is, why is these data being excluded from the reports? im thinking that the free-sa reports list only certain types of things but im also thinking if are the squid logs the ones that are not erm... logging these conections. Any help, guide, advice or clarification is appreciated.

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  • What is the best hosting option for Flash web-widget?

    - by par
    Our Flash web-widget has got highly popular. It is downloaded around 100,000 times per day. And that is the problem. Our server bandwidth is too narrow to deliver the widget to the clients fast. The widget is loaded very slow. Probably 20 times slower than before (at peak times). Probably I have choosen not the right hoster for my task - delivering 1 MB Flash widget to 100,000 users per day. What is the best hosting solution in my case? I'm not good at server administration so forgive me if I sound naive. The details are the following. Our hoster options: -Dedicated server, Ubuntu -10 Mbit Connection -monthly bandwidth limit: 2000 GB Widget size is 1 MB. The widget consists of the main SWF and a number of loaded SWF and data files. This is a part of Apache Status report taken right now ---- Server uptime: 1 hour 2 minutes 38 seconds Total accesses: 74865 - Total Traffic: 5.8 GB CPU Usage: u28 s7.78 cu0 cs0 - .952% CPU load 19.9 requests/sec - 1.6 MB/second - 81.1 kB/request 200 requests currently being processed, 0 idle workers WWWWWWWWWWWWWWWWWWWWWWWWCWWWWWWWWWWWWWWWWWWWWWWWWWWCWWWWWWWCWWWW WWWWWCWWWWWWWWWWWWWWCWWWWWWWWWWWWCWWWWWWWWCWWCWWWWWWWWWWWWWWWWWW WWWWWWWWWWWWWWWWCWWWWWWWWWWWWWWWWWWWWWWWWWWWCWCWWWWWWWWWWWWWWWCW WWWWWWWW........................................................ ----

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  • Simple Distributed Disconnected way to sync a directory

    - by Rory
    I want to start regularly backup my home directory on my ubuntu laptop, machine X. Suppose I have access to 2 different remote (linux) servers that I can backup to, machines A & B. Machine X will be the master, and should be synced to A and B. I could just regularly run rsync from X to A and then from X to B. That's all I need. However I'm curious if there's a more bandwidth effecient, and hence faster way to do it. Assuming X is going to be on residential style broadband lines, and since I don't want to soak up the bandwidth, I would limit the transfer from X. A and B will be on all the time, however X, will not be, so I'd also like to reduce the amount of time that X is transfering, potentially allowing A and B to spend more time transfering. Also, X won't be connected all the time. What's the best way to do this? rsync from X to A, then from A to B? Timing that right could be troublesome. I don't want to keep old files around, so if I was to rsync, then the --del option would be used. Could that mean something might get tranfered from A to B, then deleted from B, then transfered from A to B again? That's suboptimal. I know there are fancy distributed filesystems like gluster, but I think that's overkill in this case, and might not fit with the disconnected nature.

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  • Centralized backup method recommendation for SMEs with various OSes

    - by Akinator
    Hi I was wondering what in your opinion is the "best" method for having "everything" backed-up in the following situation. We are a SMEs with 10 computers in total. Three of those computers are MACs The rest are windows (1 vista, 4 win7 and 2 XPs) I'm very open to what the method should be but you should also consider the follwing: Very limited resources Quite "small" bandwidth (4 MBs for all (download) 0.4 MBs (upload, yep, thats it)- though this might get, a little bit better) One of the main thing to back up would be the mails, considerations: All windows computers use outlook, mainly 2003 There is one mac that uses outlook too (for mac of course - not 2011 yet) We also have to backup the files: Not a huge amount Very few very big files Very organizes (by machine) What I would like is to hear your opinions as to which would be the best method (or combination of methods - preferably one of course) considering. We are not sure what do we need and I'm open to suggestions, though an online (cloud based applications) would be great, remember the the bandwidth is unbearable. Last think to consider, it that we would like to do weekly updates (unless the method is very easy of course). Thanks in advance!! I tried to be as specific as possible, but if anything is needed I'll gladly update, please ask for any clarification needed! Please avoid any answers like upgrade all to windows 7 and throw away your macs :) our's may not be an ideal situation, but it is what it is, and right now, it would be impossible for us to change it for a lot of circumstances.

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  • moving files and directories between two machine, via a third, preserving permissions and usernames

    - by Jarmund
    The situation is as follows: Machine A has a file repository accessible via rsync Machine B needs the above mentioned files with all permissions and ownerships intact (including groups etc) Machine C has access to both A and B, but has a completely different set of users. Normally, i would just rsync everything over, directly between A and B, but due to severely limited bandwidth at the moment, i need something different, as rsync times out after building the list of the 430 files (49Mb uncompressed... can be compressed down to ~7Mb). What i've tried so far: rsync everything over from A to C, tar it, copy the tarball over, and then untar it, however, this messes up the ownership and/or the permissions. To rsync it from A to C, i run this command: rsync --numeric-ids --password-file=/root/rsync_pwd_file -oaPvu rsync://[email protected]/portal_2/ ./portal_2/ ...and from the looks of things, they do end up on C with the correct ownerships/permissions/flags/everything (not 100% sure, though.. are there any more switches i can throw in there? did i miss something?) copying the tarball over is simple enough (slow as a one-legged turtle due to the bandwidth, but it checksums out alright) What i'm unsure of is the flags and switches for creating and extracting the tarball, so could someone please provide the full commands for creating a tarball from /root/portal_2 on machine C (with everything intact) and extracting the tarball into /var/ex/portal_2 on machine B? ? Also, are there any other approaches worth mentioning that could allow me to perform this? I have root access to A and C, whereas i only have rsync access to B. PS: I'm running rsync v2.6.9 on machine B, and unfortunately i do not have the oportunity to upgrade to v3

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  • Centralized backup method recommendation for SMEs with various OSes

    - by Akinator
    Hi I was wondering what in your opinion is the "best" method for having "everything" backed-up in the following situation. We are a SMEs with 10 computers in total. Three of those computers are MACs The rest are windows (1 vista, 4 win7 and 2 XPs) I'm very open to what the method should be but you should also consider the follwing: Very limited resources Quite "small" bandwidth (4 MBs for all (download) 0.4 MBs (upload, yep, thats it)- though this might get, a little bit better) One of the main thing to back up would be the mails, considerations: All windows computers use outlook, mainly 2003 There is one mac that uses outlook too (for mac of course - not 2011 yet) We also have to backup the files: Not a huge amount Very few very big files Very organizes (by machine) What I would like is to hear your opinions as to which would be the best method (or combination of methods - preferably one of course) considering. We are not sure what do we need and I'm open to suggestions, though an online (cloud based applications) would be great, remember the the bandwidth is unbearable. Last think to consider, it that we would like to do weekly updates (unless the method is very easy of course). Thanks in advance!! I tried to be as specific as possible, but if anything is needed I'll gladly update, please ask for any clarification needed! Please avoid any answers like upgrade all to windows 7 and throw away your macs :) our's may not be an ideal situation, but it is what it is, and right now, it would be impossible for us to change it for a lot of circumstances.

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  • Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

    - by Shaun
    Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • How to Buy an SD Card: Speed Classes, Sizes, and Capacities Explained

    - by Chris Hoffman
    Memory cards are used in digital cameras, music players, smartphones, tablets, and even laptops. But not all SD cards are created equal — there are different speed classes, physical sizes, and capacities to consider. Different devices require different types of SD cards. Here are the differences you’ll need to keep in mind when picking out the right SD card for your device. Speed Class In a nutshell, not all SD cards offer the same speeds. This matters for some tasks more than it matters for others. For example, if you’re a professional photographer taking photos in rapid succession on a DSLR camera saving them in high-resolution RAW format, you’ll want a fast SD card so your camera can save them as fast as possible. A fast SD card is also important if you want to record high-resolution video and save it directly to the SD card. If you’re just taking a few photos on a typical consumer camera or you’re just using an SD card to store some media files on your smartphone, the speed isn’t as important. Manufacturers use “speed classes” to measure an SD card’s speed. The SD Association that defines the SD card standard doesn’t actually define the exact speeds associated with these classes, but they do provide guidelines. There are four different speed classes — 10, 8, 4, and 2. 10 is the fastest, while 2 is the slowest. Class 2 is suitable for standard definition video recording, while classes 4 and 6 are suitable for high-definition video recording. Class 10 is suitable for “full HD video recording” and “HD still consecutive recording.” There are also two Ultra High Speed (UHS) speed classes, but they’re more expensive and are designed for professional use. UHS cards are designed for devices that support UHS. Here are the associated logos, in order from slowest to fastest:       You’ll probably be okay with a class 4 or 6 card for typical use in a digital camera, smartphone, or tablet. Class 10 cards are ideal if you’re shooting high-resolution videos or RAW photos. Class 2 cards are a bit on the slow side these days, so you may want to avoid them for all but the cheapest digital cameras. Even a cheap smartphone can record HD video, after all. An SD card’s speed class is identified on the SD card itself. You’ll also see the speed class on the online store listing or on the card’s packaging when purchasing it. For example, in the below photo, the middle SD card is speed class 4, while the two other cards are speed class 6. If you see no speed class symbol, you have a class 0 SD card. These cards were designed and produced before the speed class rating system was introduced. They may be slower than even a class 2 card. Physical Size Different devices use different sizes of SD cards. You’ll find standard-size CD cards, miniSD cards, and microSD cards. Standard SD cards are the largest, although they’re still very small. They measure 32x24x2.1 mm and weigh just two grams. Most consumer digital cameras for sale today still use standard SD cards. They have the standard “cut corner”  design. miniSD cards are smaller than standard SD cards, measuring 21.5x20x1.4 mm and weighing about 0.8 grams. This is the least common size today. miniSD cards were designed to be especially small for mobile phones, but we now have a smaller size. microSD cards are the smallest size of SD card, measuring 15x11x1 mm and weighing just 0.25 grams. These cards are used in most cell phones and smartphones that support SD cards. They’re also used in many other devices, such as tablets. SD cards will only fit into marching slots. You can’t plug a microSD card into a standard SD card slot — it won’t fit. However, you can purchase an adapter that allows you to plug a smaller SD card into a larger SD card’s form and fit it into the appropriate slot. Capacity Like USB flash drives, hard drives, solid-state drives, and other storage media, different SD cards can have different amounts of storage. But the differences between SD card capacities don’t stop there. Standard SDSC (SD) cards are 1 MB to 2 GB in size, or perhaps 4 GB in size — although 4 GB is non-standard. The SDHC standard was created later, and allows cards 2 GB to 32 GB in size. SDXC is a more recent standard that allows cards 32 GB to 2 TB in size. You’ll need a device that supports SDHC or SDXC cards to use them. At this point, the vast majority of devices should support SDHC. In fact, the SD cards you have are probably SDHC cards. SDXC is newer and less common. When buying an SD card, you’ll need to buy the right speed class, size, and capacity for your needs. Be sure to check what your device supports and consider what speed and capacity you’ll actually need. Image Credit: Ryosuke SEKIDO on Flickr, Clive Darra on Flickr, Steven Depolo on Flickr

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  • Coded ui to measure performance

    - by Mike Weber
    I have been tasked with using coded UI to measure performance on a proprietary windows desktop application. The need is to measure how long it takes for the next page/screen to display after a user clicks on a control. For example - a user enters their ID and PW and clicks sign-in. The need is to measure how long it takes for the next screen to display when the user clicks the sign-in button. I understand the need to define what indicates the screen is loaded and ready for use. One approach is to use control.WaitForControlReady and use BeginTimer/EndTimer. Is coded ui a dependable and accurate way of measuring time? Is WaitForControlReady the best method to determine when a control is ready for use?

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  • Benchmark for website speed optimization

    - by gowri
    I working on website speed optimization. I mostly used 3 tools for analyzing speed of optimization. Speed analyzing Tools: Google pagespeed tool Yslow Firefox extenstion Web Page Performance Test I am measuring performance using above tool and benchmark result as below like before and after. Before optimization : Google PageSpeed Insights score : 53/100 Web Page Performance Test : 55/100 (First View : 10.710s, Repeat view : 6.387s ) Yahoo Overall performance score : 68 Stage 1 After optimization : Google PageSpeed Insights score : 88/100 Web Page Performance Test : 88/100 (First View : 6.733s, Repeat view : 1.908s ) Yahoo Overall performance score : 80 My question is ? Am i doing correct way ? What is the best way of benchmark for speed optimization ? Is there any standard ? Is there any much better tool for analyzing speed ?

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  • New VS2012 Book: Pro Application Lifecycle Management with Visual Studio 2012

    - by Jakob Ehn
    During the spring/summer I have been involved with reviewing a new book about Visual Studio 2012 ALM from Apress called “Pro Application Lifecycle Management with Visual Studio 2012” The book is written by a fellow Visual Studio ALM MVP Mathias Olausson and his colleague Joachim Rossberg. It is a very comprehensive book that covers both all aspects of ALM in general and also how to implement these practices with Visual Studio 2012. The book also has several chapters dedicated to measuring your improvements by using ALM assessments and metrics. Read more about the book here on Mathias blog: http://msmvps.com/blogs/molausson/archive/2012/07/17/book-project-pro-application-lifecycle-management-with-visual-studio-2012-completed.aspx You can pre-order the book here at Amazon: http://www.amazon.com/Application-Lifecycle-Management-Visual-Professional/dp/1430243449/ Check it out!

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  • Benchmarking CPU processing power

    - by Federico Zancan
    Provided that many tools for computers benchmarking are available already, I'd like to write my own, starting with processing power measurement. I'd like to write it in C under Linux, but other language alternatives are welcome. I thought starting from floating point operations per second, but it is just a hint. I also thought it'd be correct to keep track of CPU number of cores, RAM amount and the like, to more consistently associate results with CPU architecture. How would you proceed to the task of measuring CPU computing power? And on top of that: I would worry about a properly minimum workload induced by concurrently running services; is it correct to run benchmarking as a standalone (and possibly avulsed from the OS environment) process?

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  • Webinar: Integrated Sales & Marketing - An Impossible Dream?

    - by charles.knapp
    Are you making the most of the latest B2B marketing thinking? Are your marketing tactics, your outbound email campaigns and your SEO generating enough of the prospects and leads that your sales teams need? Are your sales and marketing functions aligned and working together with optimised results? In this Webinar with MarketingWeek Magazine, find out how: - To ensure your marketers create and deliver consistently effective, and targeted campaigns - You can triple the customer intelligence your marketers gather, ensuring your sales teams are better informed and qualified than ever before - Generate up to 200% growth in lead volume and start measuring marketing effectiveness against increase in sales and size of an average deal - And hear how BPI OnDemand has delivered integrated sales & marketing across industries, with results such as 100% ROI on system cost for Heal's after just one campaign

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  • What is a good way to measure game virality?

    - by Chris Garrett
    I have added some social features to an iPhone game (Lexitect if you're curious), such as email, Twitter, and Facebook integration for sharing high scores. Along with these features, I am measuring how many times users make it to each step. The goal of these features are to make the game more viral, and I am trying to get to a measure of game virality. I would think that a game virality metric would produce a number based on 1.0, where 1.0 = zero viral growth, and 1.01 would represent 1% viral growth over some unit of time. How is virality normally measured, and in what units? How is time capped on the metric? i.e. if I gave each player a year to determine how many recommendations they make, I wouldn't get any real numbers for a year from the time I start tracking it. Are there any standards for tracking virality in a meaningful way?

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  • Dot Net Code Coverage Test Tools - there is now a choice

    - by TATWORTH
    I have been pleasantly surprised this week to discover that there is a choice of tools for measuring Code Coverage. If you have Visual Studio Team edition, then if you are using MSTEST, then you have built-in code coverage, however even then you may need a standalone tool. The tools I have found are (costs are per seat): 1) NCover  http://www.ncover.com/ (from $199 to $658 per seat) I have used it but it is very expensive. 2) PartCover http://sourceforge.net/projects/partcover/ - Free!  Steep initial learning curve to get it to work. 3) Dot Cover from http://www.jetbrains.com/dotcover/ - Personal licence - normally $99 but at a introductory price of $75 and free for OpenSource Developers (details at http://www.jetbrains.com/dotcover/buy/buy.jsp#opensource_) 4) Test Matrix from http://submain.com/products/testmatrix.aspx - $149 for a licence

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  • is there any elegant way to analyze an engineer's process?

    - by NewAlexandria
    Plenty of sentiment exists that measuring commits is inappropriate. Has any study been done that tries to draw in more sources than commits - such as: browsing patterns IDE work (pre-commit) idle time multitasking I can't think of an easy way to do these measures, but I wonder if any study has been done. On a personal note, I do believe that reflection on one's own 'metrics' could be valuable regardless of (or in the absence of) using these for performance eval. I.E. an un-biased way to reflect on your habits. But this is a discussion matter beyond Q&A.

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  • A tale of two viewports &#8212; part one

    Back in November I started complicated research into measuring the widths and heights of variousinteresting elements in mobile browsers. This research kept me occupied for months and months; and frankly I becamea bit afraid of it because the subject is so complicated.Besides, when I re-did some tests in MarchI pretty quickly figured out I’d made some nasty mistakes in my original tests. Back to thedrawing board.However, after a review round by some browser vendors and some rewriting it’s done now.Today...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • What do UI developers in the US, working in Imperial measurements, use for decimalised fractions of an Inch? [migrated]

    - by Preet Sangha
    Internally we work with metric units and use decimal fractions for sub units, e.g. 1cm or 0.35cm or 23mm) We're building a user oriented design tool for laying out reports and was wondering what the most most common approach taken by UI developers who are still working in Imperial measurements (Inches etc.) when it comes to decimalised fractions. Most of my cultural references point to people using 1/2, 1/4, 1/8 or 1/32 inch when measuring fractions. But when faced with decimal equivalent what do people tend to do? For example do people use 0.5, 0.25, 0.125 etc or do you people roll these up to say 0.5, 03, and 0.1 inch? Sorry for the confusing question.

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  • If Nvidia Shield can stream a game via WiFi (~150-300Mbps), where is the 1-10Gbps wired streaming?

    - by Enigma
    Facts: It is surprising and uncharacteristic that a wireless game streaming solution is the *first to hit the market when a 1000mbps+ Ethernet connection would accomplish the same feat with roughly 6x the available bandwidth. 150-300mbps WiFi is in no way superior to a 1000mbps+ LAN connection aside from well wireless mobility. Throughout time, (since the internet was created) wired services have **always come first yet in this particular case, the opposite seems to be true. We had wired internet first, wired audio streaming, and wired video streaming all before their wireless counterparts. Why? Largely because the wireless bandwidth was and is inferior. Even today despite being significantly better and capable of a lot more, it is still inferior to a wired connection. Situation: Chief among these is that NVIDIA’s Shield handheld game console will be getting a microconsole-like mode, dubbed “Shield Console Mode”, that will allow the handheld to be converted into a more traditional TV-connected console. In console mode Shield can be controlled with a Bluetooth controller, and in accordance with the higher resolution of TVs will accept 1080p game streaming from a suitably equipped PC, versus 720p in handheld mode. With that said 1080p streaming will require additional bandwidth, and while 720p can be done over WiFi NVIDIA will be requiring a hardline GigE connection for 1080p streaming (note that Shield doesn’t have Ethernet, so this is presumably being done over USB). Streaming aside, in console mode Shield will also support its traditional local gaming/application functionality. - http://www.anandtech.com/show/7435/nvidia-consolidates-game-streaming-tech-under-gamestream-brand-announces-shield-console-mode ^ This is not acceptable to me for a number of reasons not to mention the ridiculousness of having a little screen+controller unit sitting there while using a secondary controller and screen instead. That kind of redundant absurdity exemplifies how wrong of a solution that is. They need a second product for this solution without the screen or controller for it to make sense... at which point your just buying a little computer that does what most other larger computers do better. While this secondary project will provide a wired connection, it still shouldn't be necessary to purchase a Shield to have this benefit. Not only this but Intel's WiDi claims game streaming support as well - wirelessly. Where is the wired streaming? All that is required, by my understanding, is the ability to decode H.264 video compression and transmit control/feedback so by any logical comparison, one (Nvidia especially) should have no difficulty in creating an application for PC's (win32/64 environment) that does the exact same thing their android app does. I have 2 video cards capable of streaming (encoding) H.264 so by right they must be capable of decoding it I would think. I should be able to stream to my second desktop or my laptop both of which by hardware comparison are superior to the Shield. I haven't found anything stating plans to allow non-shield owners to do this. Can a third party create this software or does it hinge on some limitation that only Nvidia can overcome? Reiteration of questions: Is there a technical reason (non marketing) for why Nvidia opted to bottleneck the streaming service with a wireless connection limiting the resolution to 720p and introducing intermittent video choppiness when on a wired connection one could achieve, presumably, 1080p with significantly less or zero choppiness? Is there anything limiting developers from creating a PC/Desktop application emulating the same H.264 decoding functionality that circumvents the need to get an Nvidia Shield altogether? (It is not a matter of being too cheap to support Nvidia - I have many Nvidia cards that aren't being used. One should not have to purchase specialty hardware when = hardware already exists) Same questions go for Intel Widi also. I am just utterly perplexed that there are wireless live streaming solution and yet no wired. How on earth can wireless be the goto transmission medium? Is there another solution that takes advantage of H.264 video compression allowing live streaming over a wired connection? (*) - Perhaps this isn't the first but afaik it is the first complete package. (**) - I cant back that up with hard evidence/links but someone probably could. Edit: Maybe this will be the solution I am looking for but I still find it hard to believe that they would be the first and after wireless solutions already exist. In-home Streaming You can play all your Windows and Mac games on your SteamOS machine, too. Just turn on your existing computer and run Steam as you always have - then your SteamOS machine can stream those games over your home network straight to your TV! - http://store.steampowered.com/livingroom/SteamOS/

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  • Weighted round robins via TTL - possible?

    - by Joe Hopfgartner
    I currently use DNS round robin for load balancing, which works great. The records look like this (I have a ttl of 120 seconds) ;; ANSWER SECTION: orion.2x.to. 116 IN A 80.237.201.41 orion.2x.to. 116 IN A 87.230.54.12 orion.2x.to. 116 IN A 87.230.100.10 orion.2x.to. 116 IN A 87.230.51.65 I learned that not every ISP / device treats such a response the same way. For example some DNS servers rotate the addresses randomly or always cycle them through. Some just propagate the first entry, others try to determine which is best (regionally near) by looking at the ip address. However if the userbase is big enough (spreads over multiple ISPs etc) it balances pretty well. The discrepancies from highest to lowest loaded server hardly every exceeds 15%. However now I have the problem that I am introducing more servers into the systems, that not all have the same capacities. I currently only have 1gbps servers, but I want to work with 100mbit and also 10gbps servers too. So what I want is I want to introduce a server with 10 GBps with a weight of 100, a 1 gbps server with a weight of 10 and a 100 mbit server with a weight of 1. I used to add servers twice to bring more traffic to them (which worked nice. the bandwidth doubled almost.) But adding a 10gbit server 100 times to DNS is a bit rediculous. So I thought about using the TTL. If I give server A 240 seconds ttl and server B only 120 seconds (which is about about the minimum to use for round robin, as a lot of dns servers set to 120 if a lower ttl is specified.. so i have heard) I think something like this should occour in an ideal scenario: first 120 seconds 50% of requests get server A -> keep it for 240 seconds. 50% of requests get server B -> keep it for 120 seconds second 120 seconds 50% of requests still have server A cached -> keep it for another 120 seconds. 25% of requests get server A -> keep it for 240 seconds 25% of requests get server B -> keep it for 120 seconds third 120 seconds 25% will get server A (from the 50% of Server A that now expired) -> cache 240 sec 25% will get server B (from the 50% of Server A that now expired) -> cache 120 sec 25% will have server A cached for another 120 seconds 12.5% will get server B (from the 25% of server B that now expired) -> cache 120sec 12.5% will get server A (from the 25% of server B that now expired) -> cache 240 sec fourth 120 seconds 25% will have server A cached -> cache for another 120 secs 12.5% will get server A (from the 25% of b that now expired) -> cache 240 secs 12.5% will get server B (from the 25% of b that now expired) -> cache 120 secs 12.5% will get server A (from the 25% of a that now expired) -> cache 240 secs 12.5% will get server B (from the 25% of a that now expired) -> cache 120 secs 6.25% will get server A (from the 12.5% of b that now expired) -> cache 240 secs 6.25% will get server B (from the 12.5% of b that now expired) -> cache 120 secs 12.5% will have server A cached -> cache another 120 secs ... i think i lost something at this point but i think you get the idea.... As you can see this gets pretty complicated to predict and it will for sure not work out like this in practice. But it should definitely have an effect on the distribution! I know that weighted round robin exists and is just controlled by the root server. It just cycles through dns records when responding and returns dns records with a set propability that corresponds to the weighting. My DNS server does not support this, and my requirements are not that precise. If it doesnt weight perfectly its okay, but it should go into the right direction. I think using the TTL field could be a more elegant and easier solution - and it deosnt require a dns server that controls this dynamically, which saves resources - which is in my opinion the whole point of dns load balancing vs hardware load balancers. My question now is... are there any best prectices / methos / rules of thumb to weight round robin distribution using the TTL attribute of DNS records? Edit: The system is a forward proxy server system. The amount of Bandwidth (not requests) exceeds what one single server with ethernet can handle. So I need a balancing solution that distributes the bandwidth to several servers. Are there any alternative methods than using DNS? Of course I can use a load balancer with fibre channel etc, but the costs are rediciulous and it also increases only the width of the bottleneck and does not eliminate it. The only thing i can think of are anycast (is it anycast or multicast?) ip addresses, but I don't have the means to set up such a system.

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  • Apache on Win32: Slow Transfers of single, static files in HTTP, fast in HTTPS

    - by Michael Lackner
    I have a weird problem with Apache 2.2.15 on Windows 2000 Server SP4. Basically, I am trying to serve larger static files, images, videos etc. The download seems to be capped at around 550kB/s even over 100Mbit LAN. I tried other protocols (FTP/FTPS/FTP+ES/SCP/SMB), and they are all in the multi-megabyte range. The strangest thing is that, when using Apache with HTTPS instead of HTTP, it serves very fast, around 2.7MByte/s! I also tried the AnalogX SimpleWWW server just to test the plain HTTP speed of it, and it gave me a healthy 3.3Mbyte/s. I am at a total loss here. I searched the web, and tried to change the following Apache configuration directives in httpd.conf, one at a time, mostly to no avail at all: SendBufferSize 1048576 #(tried multiples of that too, up to 100Mbytes) EnableSendfile Off #(minor performance boost) EnableMMAP Off Win32DisableAcceptEx HostnameLookups Off #(default) I also tried to tune the following registry parameters, setting their values to 4194304 in decimal (they are REG_DWORD), and rebooting afterwards: HKLM\SYSTEM\CurrentControlSet\Services\AFD\Parameters\DefaultReceiveWindow HKLM\SYSTEM\CurrentControlSet\Services\AFD\Parameters\DefaultSendWindow Additionally, I tried to install mod_bw, which sets the event timer precision to 1ms, and allows for bandwidth throttling. According to some people it boosts static file serving performance when set to unlimited bandwidth for everybody. Unfortunately, it did nothing for me. So: AnalogX HTTP: 3300kB/s Gene6 FTPD, plain: 3500kB/s Gene6 FTPD, Implicit and Explicit SSL, AES256 Cipher: 1800-2000kB/s freeSSHD: 1100kB/s SMB shared folder: about 3000kB/s Apache HTTP, plain: 550kB/s Apache HTTPS: 2700kB/s Clients that were used in the bandwidth testing: Internet Explorer 8 (HTTP, HTTPS) Firefox 8 (HTTP, HTTPS) Chrome 13 (HTTP, HTTPS) Opera 11.60 (HTTP, HTTPS) wget under CygWin (HTTP, HTTPS) FileZilla (FTP, FTPS, FTP+ES, SFTP) Windows Explorer (SMB) Generally, transfer speeds are not too high, but that's because the server machine is an old quad Pentium Pro 200MHz machine with 2GB RAM. However, I would like Apache to serve at at least 2Mbyte/s instead of 550kB/s, and that already works with HTTPS easily, so I fail to see why plain HTTP is so crippled. I am using a Kerio Winroute Firewall, but no Throttling and no special filters peeking into HTTP traffic, just the plain Firewall functionality for blocking/allowing connections. The Apache error.log (Loglevel info) shows no warnings, no errors. Also nothing strange to be seen in access.log. I have already stripped down my httpd.conf to the bare minimum just to make sure nothing is interfering, but that didn't help either. If you have any idea, help would be greatly appreciated, since I am totally out of ideas! Thanks! Edit: I have now tried a newer Apache 2.2.21 to see if it makes any difference. However, the behaviour is exactly the same. Edit 2: KM01 has requested a sniff on the HTTP headers, so here comes the LiveHTTPHeaders output (an extension to Firefox). The Output is generated on downloading a single file called "elephantsdream_source.264", which is an H.264/AVC elementary video stream under an Open Source license. I have taken the freedom to edit the URL, removing folders and changing the actual servers domain name to www.mydomain.com. Here it is: LiveHTTPHeaders, Plain HTTP: http://www.mydomain.com/elephantsdream_source.264 GET /elephantsdream_source.264 HTTP/1.1 Host: www.mydomain.com User-Agent: Mozilla/5.0 (Windows NT 5.2; WOW64; rv:6.0.2) Gecko/20100101 Firefox/6.0.2 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: de-de,de;q=0.8,en-us;q=0.5,en;q=0.3 Accept-Encoding: gzip, deflate Accept-Charset: ISO-8859-1,utf-8;q=0.7,*;q=0.7 Connection: keep-alive HTTP/1.1 200 OK Date: Wed, 21 Dec 2011 20:55:16 GMT Server: Apache/2.2.21 (Win32) mod_ssl/2.2.21 OpenSSL/0.9.8r PHP/5.2.17 Last-Modified: Thu, 28 Oct 2010 20:20:09 GMT Etag: "c000000013fa5-29cf10e9-493b311889d3c" Accept-Ranges: bytes Content-Length: 701436137 Keep-Alive: timeout=15, max=100 Connection: Keep-Alive Content-Type: text/plain LiveHTTPHeaders, HTTPS: https://www.mydomain.com/elephantsdream_source.264 GET /elephantsdream_source.264 HTTP/1.1 Host: www.mydomain.com User-Agent: Mozilla/5.0 (Windows NT 5.2; WOW64; rv:6.0.2) Gecko/20100101 Firefox/6.0.2 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: de-de,de;q=0.8,en-us;q=0.5,en;q=0.3 Accept-Encoding: gzip, deflate Accept-Charset: ISO-8859-1,utf-8;q=0.7,*;q=0.7 Connection: keep-alive HTTP/1.1 200 OK Date: Wed, 21 Dec 2011 20:56:57 GMT Server: Apache/2.2.21 (Win32) mod_ssl/2.2.21 OpenSSL/0.9.8r PHP/5.2.17 Last-Modified: Thu, 28 Oct 2010 20:20:09 GMT Etag: "c000000013fa5-29cf10e9-493b311889d3c" Accept-Ranges: bytes Content-Length: 701436137 Keep-Alive: timeout=15, max=100 Connection: Keep-Alive Content-Type: text/plain

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