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  • Network and Storage Devices Throughput Chart

    - by zroiy
    With all of the different storage and network devices that surround our day to day life, understanding these devices data transfer speeds can be somewhat confusing. Think about trying to identify your weakest link in the a chain that starts with an external USB hard drive (or a flash drive) that's connected to a 802.11g wifi router, can you quickly come up with an answer of where's the bottle neck in that chain , is it the router or the storage devices ? . Well, the following chart should give you an idea understanding different devices, protocols and interfaces maximum throughput speeds. Though these numbers can fluctuate (mostly for worse, but sometimes for the better) due to different kind of factors such as OS overhead (or caching and optimization) , multiple users or processes and so on , the chart can still serve to provide basic information on the theoretical throughput different devices and protocols can get to.. Enjoy.  Link to the full size chart   References:http://en.wikipedia.org/wiki/Sata#SATA_revision_1.0_.28SATA_1.5_Gbit.2Fs.29http://en.wikipedia.org/wiki/Usbhttp://en.wikipedia.org/wiki/Usb_3http://en.wikipedia.org/wiki/802.11http://mashable.com/2011/09/21/fastest-download-speeds-infographic/http://en.wikipedia.org/wiki/Thunderbolt_(interface)http://www.computerworld.com/s/article/9220434/Thunderbolt_vs._SuperSpeed_USB_3.0  Icons:http://openiconlibrary.sourceforge.net/gallery2/?./Icons/devices/drive-harddisk-3.png      

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  • Limit Windows PC Network/Internet Throughput

    - by Jon Cram
    I have a Vista x64 machine on a fairly fast Internet connection and either buggy drivers for the onboard Ethernet or faulty onboard Ethernet hardware. If I sustain too high a throughput on the Ethernet connection the network connection within Windows fails and I have to restart the machine to restore connectivity. I don't believe I can fix this issue (I'm erring towards faulty hardware) but would like to mitigate the effects by limiting my network throughput. I'm in a position where I would like to download a 5GB file from the Internet (a game install via Steam) and am certain that as this will take a few hours I will not be able to complete the download before my network connection within Windows fails. From downloading content through a BitTorrent client I have found that by limiting the download throughput to around 150 kilobytes per second I can maintain a steady network connection. I can't directly limit the throughput of the download through the Steam client and would instead like to find out how I can limit the throughput of my Ethernet connection within Windows. Any suggestions on how I can achieve this?

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  • Throughput tool with decent graphing.

    - by Cory J
    I've been looking through some of the tools available for measuring network throughput, namely iperf, bwping, ttcp, etc. I am planning on doing throughput tests over a long period of time, so what I really need is good graphing output, preferably rrd graphs. The Jperf frontend for iperf will generate a graph, and bmon has a nice command-line graph, but these simply count seconds since the test was started. I am trying to measure trends in throughput over times of the day, so a graph with times and days is necessary. So a way to get iperf to log to RRDs would be best, if this isn't possible could someone point me toward another solution?

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  • Very low throughput on 10GbE network

    - by aix
    I have two Linux machines, each equipped with a Solarflare SFN5122F 10GbE NIC. The two NICs are connected together with an SFP+ Direct Attach cable. I am using netperf to measure TCP throughput between the two machines. On one box, I run: netserver and on the other: netperf -t TCP_STREAM -H 192.168.x.x -- -m 32768 I get: MIGRATED TCP STREAM TEST from 0.0.0.0 (0.0.0.0) port 0 AF_INET to 192.168.x.x (192.168.x.x) port 0 AF_INET Recv Send Send Socket Socket Message Elapsed Size Size Size Time Throughput bytes bytes bytes secs. 10^6bits/sec 87380 16384 32768 10.02 1321.34 The measured throughput is 1.3Gb/s. This is 7.5x below the theoretical maximum, and only 30% faster than 1GbE. What steps can I take to troubleshoot this?

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  • Throughput; capacity planning help for C10K like design

    - by z8000
    I am designing a network service in which clients connect and stay connected -- the model is not far off from IRC less the s2s connections. I could use some help understanding how to do capacity planning, in particular with the system resource costs associated with handling messages from/to clients. There's an article that tried to get 1 million clients connected to the same server [1]. Of course, most of these clients were completely idle in the test. If the clients sent a message every 5 seconds or so the system would surely be brought to its knees. But... How do you do less hand-waving and you know, measure such a breaking point? We're talking about messages being sent by a client over a TCP socket, into the kernel, and read by an application. The data is shuffled around in memory from one buffer to another. Do I need to consider memory throughput ("5 GT/s" [2], etc.)? I'm pretty sure I have the ability to measure the basic memory requirements due to TCP/IP buffers, expected bandwidth, and CPU resources required to process messages. I'm a little dim on what I'm calling "thoughput". Help! Also, does anyone really do this? Or, do most people sort of hand-wave and see what the real world offers, and then react appropriately? [1] http://www.metabrew.com/article/a-million-user-comet-application-with-mochiweb-part-3/ [2] http://en.wikipedia.org/wiki/GT/s

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  • Cheapest server per gigabit throughput [closed]

    - by nethgirb
    I'm looking for a set of servers for performance testing a network, and secondarily testing some applications on the servers. Their most important task is simply to pump out data: from an application like memcached or just dumped from a large file in memory into a TCP flow (i.e., disk performance doesn't matter). This should happen over one or more 1 gigabit Ethernet ports, and the machines should run Linux (ideally), or perhaps Mac OS X or some other *nix. Other than that, there are few constraints (e.g., even something ARM-based could be fine). So here's the question: What's the cheapest server per gigabit? Price and power are both considerations.

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  • Throughput and why do ISPs sell too much bandwidth?

    - by jonescb
    I hope the question made sense how I worded it. :) I've been wondering, maximum theoretical bandwidth is measured as RWIN/RTT (Window size / round trip time) Source 1 and Souce 2 So if a major city only 100 miles away gives me a ping of 50ms, and I have the default 64kb TCP window size then my maximum throughput will be 12.5Mb/s. Everything further away would give me a higher ping and therefore a lower throughput. Is there any reason to buy something like FiOS with a 50Mb/s or greater connection? Will you ever be able to reach that kind of speed? I know you can increase the TCP window size to increase throughput, but it has to be at both ends which is a deal breaker because you can't control the server. I'm assuming other network protocols like UDP aren't quite as affected by latency as TCP is, but how much of overall network traffic does non-TCP make up vs TCP. Am I just misguided about how throughput works? But if the above is correct, then why should a consumer like me buy way more bandwidth than can be realistically used. Maybe the only reason is for downloading multiple things at once, or one thing from multiple servers/peers?

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  • Exploring TCP throughput with DTrace

    - by user12820842
    One key measure to use when assessing TCP throughput is assessing the amount of unacknowledged data in the pipe. This is sometimes termed the Bandwidth Delay Product (BDP) (note that BDP is often used more generally as the product of the link capacity and the end-to-end delay). In DTrace terms, the amount of unacknowledged data in bytes for the connection is the different between the next sequence number to send and the lowest unacknoweldged sequence number (tcps_snxt - tcps_suna). According to the theory, when the number of unacknowledged bytes for the connection is less than the receive window of the peer, the path bandwidth is the limiting factor for throughput. In other words, if we can fill the pipe without the peer TCP complaining (by virtue of its window size reaching 0), we are purely bandwidth-limited. If the peer's receive window is too small however, the sending TCP has to wait for acknowledgements before it can send more data. In this case the round-trip time (RTT) limits throughput. In such cases the effective throughput limit is the window size divided by the RTT, e.g. if the window size is 64K and the RTT is 0.5sec, the throughput is 128K/s. So a neat way to visually determine if the receive window of clients may be too small should be to compare the distribution of BDP values for the server versus the client's advertised receive window. If the BDP distribution overlaps the send window distribution such that it is to the right (or lower down in DTrace since quantizations are displayed vertically), it indicates that the amount of unacknowledged data regularly exceeds the client's receive window, so that it is possible that the sender may have more data to send but is blocked by a zero-window on the client side. In the following example, we compare the distribution of BDP values to the receive window advertised by the receiver (10.175.96.92) for a large file download via http. # dtrace -s tcp_tput.d ^C BDP(bytes) 10.175.96.92 80 value ------------- Distribution ------------- count -1 | 0 0 | 6 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 3 512 | 0 1024 | 0 2048 | 9 4096 | 14 8192 | 27 16384 | 67 32768 |@@ 1464 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 32396 131072 | 0 SWND(bytes) 10.175.96.92 80 value ------------- Distribution ------------- count 16384 | 0 32768 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 17067 65536 | 0 Here we have a puzzle. We can see that the receiver's advertised window is in the 32768-65535 range, while the amount of unacknowledged data in the pipe is largely in the 65536-131071 range. What's going on here? Surely in a case like this we should see zero-window events, since the amount of data in the pipe regularly exceeds the window size of the receiver. We can see that we don't see any zero-window events since the SWND distribution displays no 0 values - it stays within the 32768-65535 range. The explanation is straightforward enough. TCP Window scaling is in operation for this connection - the Window Scale TCP option is used on connection setup to allow a connection to advertise (and have advertised to it) a window greater than 65536 bytes. In this case the scaling shift is 1, so this explains why the SWND values are clustered in the 32768-65535 range rather than the 65536-131071 range - the SWND value needs to be multiplied by two since the reciever is also scaling its window by a shift factor of 1. Here's the simple script that compares BDP and SWND distributions, fixed to take account of window scaling. #!/usr/sbin/dtrace -s #pragma D option quiet tcp:::send / (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { @bdp["BDP(bytes)", args[2]-ip_daddr, args[4]-tcp_sport] = quantize(args[3]-tcps_snxt - args[3]-tcps_suna); } tcp:::receive / (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { @swnd["SWND(bytes)", args[2]-ip_saddr, args[4]-tcp_dport] = quantize((args[4]-tcp_window)*(1 tcps_snd_ws)); } And here's the fixed output. # dtrace -s tcp_tput_scaled.d ^C BDP(bytes) 10.175.96.92 80 value ------------- Distribution ------------- count -1 | 0 0 | 39 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 3 512 | 0 1024 | 0 2048 | 4 4096 | 9 8192 | 22 16384 | 37 32768 |@ 99 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 3858 131072 | 0 SWND(bytes) 10.175.96.92 80 value ------------- Distribution ------------- count 512 | 0 1024 | 1 2048 | 0 4096 | 2 8192 | 4 16384 | 7 32768 | 14 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 1956 131072 | 0

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  • ASP.NET Frameworks and Raw Throughput Performance

    - by Rick Strahl
    A few days ago I had a curious thought: With all these different technologies that the ASP.NET stack has to offer, what's the most efficient technology overall to return data for a server request? When I started this it was mere curiosity rather than a real practical need or result. Different tools are used for different problems and so performance differences are to be expected. But still I was curious to see how the various technologies performed relative to each just for raw throughput of the request getting to the endpoint and back out to the client with as little processing in the actual endpoint logic as possible (aka Hello World!). I want to clarify that this is merely an informal test for my own curiosity and I'm sharing the results and process here because I thought it was interesting. It's been a long while since I've done any sort of perf testing on ASP.NET, mainly because I've not had extremely heavy load requirements and because overall ASP.NET performs very well even for fairly high loads so that often it's not that critical to test load performance. This post is not meant to make a point  or even come to a conclusion which tech is better, but just to act as a reference to help understand some of the differences in perf and give a starting point to play around with this yourself. I've included the code for this simple project, so you can play with it and maybe add a few additional tests for different things if you like. Source Code on GitHub I looked at this data for these technologies: ASP.NET Web API ASP.NET MVC WebForms ASP.NET WebPages ASMX AJAX Services  (couldn't get AJAX/JSON to run on IIS8 ) WCF Rest Raw ASP.NET HttpHandlers It's quite a mixed bag, of course and the technologies target different types of development. What started out as mere curiosity turned into a bit of a head scratcher as the results were sometimes surprising. What I describe here is more to satisfy my curiosity more than anything and I thought it interesting enough to discuss on the blog :-) First test: Raw Throughput The first thing I did is test raw throughput for the various technologies. This is the least practical test of course since you're unlikely to ever create the equivalent of a 'Hello World' request in a real life application. The idea here is to measure how much time a 'NOP' request takes to return data to the client. So for this request I create the simplest Hello World request that I could come up for each tech. Http Handler The first is the lowest level approach which is an HTTP handler. public class Handler : IHttpHandler { public void ProcessRequest(HttpContext context) { context.Response.ContentType = "text/plain"; context.Response.Write("Hello World. Time is: " + DateTime.Now.ToString()); } public bool IsReusable { get { return true; } } } WebForms Next I added a couple of ASPX pages - one using CodeBehind and one using only a markup page. The CodeBehind page simple does this in CodeBehind without any markup in the ASPX page: public partial class HelloWorld_CodeBehind : System.Web.UI.Page { protected void Page_Load(object sender, EventArgs e) { Response.Write("Hello World. Time is: " + DateTime.Now.ToString() ); Response.End(); } } while the Markup page only contains some static output via an expression:<%@ Page Language="C#" AutoEventWireup="false" CodeBehind="HelloWorld_Markup.aspx.cs" Inherits="AspNetFrameworksPerformance.HelloWorld_Markup" %> Hello World. Time is <%= DateTime.Now %> ASP.NET WebPages WebPages is the freestanding Razor implementation of ASP.NET. Here's the simple HelloWorld.cshtml page:Hello World @DateTime.Now WCF REST WCF REST was the token REST implementation for ASP.NET before WebAPI and the inbetween step from ASP.NET AJAX. I'd like to forget that this technology was ever considered for production use, but I'll include it here. Here's an OperationContract class: [ServiceContract(Namespace = "")] [AspNetCompatibilityRequirements(RequirementsMode = AspNetCompatibilityRequirementsMode.Allowed)] public class WcfService { [OperationContract] [WebGet] public Stream HelloWorld() { var data = Encoding.Unicode.GetBytes("Hello World" + DateTime.Now.ToString()); var ms = new MemoryStream(data); // Add your operation implementation here return ms; } } WCF REST can return arbitrary results by returning a Stream object and a content type. The code above turns the string result into a stream and returns that back to the client. ASP.NET AJAX (ASMX Services) I also wanted to test ASP.NET AJAX services because prior to WebAPI this is probably still the most widely used AJAX technology for the ASP.NET stack today. Unfortunately I was completely unable to get this running on my Windows 8 machine. Visual Studio 2012  removed adding of ASP.NET AJAX services, and when I tried to manually add the service and configure the script handler references it simply did not work - I always got a SOAP response for GET and POST operations. No matter what I tried I always ended up getting XML results even when explicitly adding the ScriptHandler. So, I didn't test this (but the code is there - you might be able to test this on a Windows 7 box). ASP.NET MVC Next up is probably the most popular ASP.NET technology at the moment: MVC. Here's the small controller: public class MvcPerformanceController : Controller { public ActionResult Index() { return View(); } public ActionResult HelloWorldCode() { return new ContentResult() { Content = "Hello World. Time is: " + DateTime.Now.ToString() }; } } ASP.NET WebAPI Next up is WebAPI which looks kind of similar to MVC. Except here I have to use a StringContent result to return the response: public class WebApiPerformanceController : ApiController { [HttpGet] public HttpResponseMessage HelloWorldCode() { return new HttpResponseMessage() { Content = new StringContent("Hello World. Time is: " + DateTime.Now.ToString(), Encoding.UTF8, "text/plain") }; } } Testing Take a minute to think about each of the technologies… and take a guess which you think is most efficient in raw throughput. The fastest should be pretty obvious, but the others - maybe not so much. The testing I did is pretty informal since it was mainly to satisfy my curiosity - here's how I did this: I used Apache Bench (ab.exe) from a full Apache HTTP installation to run and log the test results of hitting the server. ab.exe is a small executable that lets you hit a URL repeatedly and provides counter information about the number of requests, requests per second etc. ab.exe and the batch file are located in the \LoadTests folder of the project. An ab.exe command line  looks like this: ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorld which hits the specified URL 100,000 times with a load factor of 20 concurrent requests. This results in output like this:   It's a great way to get a quick and dirty performance summary. Run it a few times to make sure there's not a large amount of varience. You might also want to do an IISRESET to clear the Web Server. Just make sure you do a short test run to warm up the server first - otherwise your first run is likely to be skewed downwards. ab.exe also allows you to specify headers and provide POST data and many other things if you want to get a little more fancy. Here all tests are GET requests to keep it simple. I ran each test: 100,000 iterations Load factor of 20 concurrent connections IISReset before starting A short warm up run for API and MVC to make sure startup cost is mitigated Here is the batch file I used for the test: IISRESET REM make sure you add REM C:\Program Files (x86)\Apache Software Foundation\Apache2.2\bin REM to your path so ab.exe can be found REM Warm up ab.exe -n100 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldJsonab.exe -n100 -c20 http://localhost/aspnetperf/api/HelloWorldJson ab.exe -n100 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorld ab.exe -n100000 -c20 http://localhost/aspnetperf/handler.ashx > handler.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/HelloWorld_CodeBehind.aspx > AspxCodeBehind.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/HelloWorld_Markup.aspx > AspxMarkup.txt ab.exe -n100000 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorld > Wcf.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldCode > Mvc.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorld > WebApi.txt I ran each of these tests 3 times and took the average score for Requests/second, with the machine otherwise idle. I did see a bit of variance when running many tests but the values used here are the medians. Part of this has to do with the fact I ran the tests on my local machine - result would probably more consistent running the load test on a separate machine hitting across the network. I ran these tests locally on my laptop which is a Dell XPS with quad core Sandibridge I7-2720QM @ 2.20ghz and a fast SSD drive on Windows 8. CPU load during tests ran to about 70% max across all 4 cores (IOW, it wasn't overloading the machine). Ideally you can try running these tests on a separate machine hitting the local machine. If I remember correctly IIS 7 and 8 on client OSs don't throttle so the performance here should be Results Ok, let's cut straight to the chase. Below are the results from the tests… It's not surprising that the handler was fastest. But it was a bit surprising to me that the next fastest was WebForms and especially Web Forms with markup over a CodeBehind page. WebPages also fared fairly well. MVC and WebAPI are a little slower and the slowest by far is WCF REST (which again I find surprising). As mentioned at the start the raw throughput tests are not overly practical as they don't test scripting performance for the HTML generation engines or serialization performances of the data engines. All it really does is give you an idea of the raw throughput for the technology from time of request to reaching the endpoint and returning minimal text data back to the client which indicates full round trip performance. But it's still interesting to see that Web Forms performs better in throughput than either MVC, WebAPI or WebPages. It'd be interesting to try this with a few pages that actually have some parsing logic on it, but that's beyond the scope of this throughput test. But what's also amazing about this test is the sheer amount of traffic that a laptop computer is handling. Even the slowest tech managed 5700 requests a second, which is one hell of a lot of requests if you extrapolate that out over a 24 hour period. Remember these are not static pages, but dynamic requests that are being served. Another test - JSON Data Service Results The second test I used a JSON result from several of the technologies. I didn't bother running WebForms and WebPages through this test since that doesn't make a ton of sense to return data from the them (OTOH, returning text from the APIs didn't make a ton of sense either :-) In these tests I have a small Person class that gets serialized and then returned to the client. The Person class looks like this: public class Person { public Person() { Id = 10; Name = "Rick"; Entered = DateTime.Now; } public int Id { get; set; } public string Name { get; set; } public DateTime Entered { get; set; } } Here are the updated handler classes that use Person: Handler public class Handler : IHttpHandler { public void ProcessRequest(HttpContext context) { var action = context.Request.QueryString["action"]; if (action == "json") JsonRequest(context); else TextRequest(context); } public void TextRequest(HttpContext context) { context.Response.ContentType = "text/plain"; context.Response.Write("Hello World. Time is: " + DateTime.Now.ToString()); } public void JsonRequest(HttpContext context) { var json = JsonConvert.SerializeObject(new Person(), Formatting.None); context.Response.ContentType = "application/json"; context.Response.Write(json); } public bool IsReusable { get { return true; } } } This code adds a little logic to check for a action query string and route the request to an optional JSON result method. To generate JSON, I'm using the same JSON.NET serializer (JsonConvert.SerializeObject) used in Web API to create the JSON response. WCF REST   [ServiceContract(Namespace = "")] [AspNetCompatibilityRequirements(RequirementsMode = AspNetCompatibilityRequirementsMode.Allowed)] public class WcfService { [OperationContract] [WebGet] public Stream HelloWorld() { var data = Encoding.Unicode.GetBytes("Hello World " + DateTime.Now.ToString()); var ms = new MemoryStream(data); // Add your operation implementation here return ms; } [OperationContract] [WebGet(ResponseFormat=WebMessageFormat.Json,BodyStyle=WebMessageBodyStyle.WrappedRequest)] public Person HelloWorldJson() { // Add your operation implementation here return new Person(); } } For WCF REST all I have to do is add a method with the Person result type.   ASP.NET MVC public class MvcPerformanceController : Controller { // // GET: /MvcPerformance/ public ActionResult Index() { return View(); } public ActionResult HelloWorldCode() { return new ContentResult() { Content = "Hello World. Time is: " + DateTime.Now.ToString() }; } public JsonResult HelloWorldJson() { return Json(new Person(), JsonRequestBehavior.AllowGet); } } For MVC all I have to do for a JSON response is return a JSON result. ASP.NET internally uses JavaScriptSerializer. ASP.NET WebAPI public class WebApiPerformanceController : ApiController { [HttpGet] public HttpResponseMessage HelloWorldCode() { return new HttpResponseMessage() { Content = new StringContent("Hello World. Time is: " + DateTime.Now.ToString(), Encoding.UTF8, "text/plain") }; } [HttpGet] public Person HelloWorldJson() { return new Person(); } [HttpGet] public HttpResponseMessage HelloWorldJson2() { var response = new HttpResponseMessage(HttpStatusCode.OK); response.Content = new ObjectContent<Person>(new Person(), GlobalConfiguration.Configuration.Formatters.JsonFormatter); return response; } } Testing and Results To run these data requests I used the following ab.exe commands:REM JSON RESPONSES ab.exe -n100000 -c20 http://localhost/aspnetperf/Handler.ashx?action=json > HandlerJson.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/MvcPerformance/HelloWorldJson > MvcJson.txt ab.exe -n100000 -c20 http://localhost/aspnetperf/api/HelloWorldJson > WebApiJson.txt ab.exe -n100000 -c20 http://localhost/AspNetPerf/WcfService.svc/HelloWorldJson > WcfJson.txt The results from this test run are a bit interesting in that the WebAPI test improved performance significantly over returning plain string content. Here are the results:   The performance for each technology drops a little bit except for WebAPI which is up quite a bit! From this test it appears that WebAPI is actually significantly better performing returning a JSON response, rather than a plain string response. Snag with Apache Benchmark and 'Length Failures' I ran into a little snag with Apache Benchmark, which was reporting failures for my Web API requests when serializing. As the graph shows performance improved significantly from with JSON results from 5580 to 6530 or so which is a 15% improvement (while all others slowed down by 3-8%). However, I was skeptical at first because the WebAPI test reports showed a bunch of errors on about 10% of the requests. Check out this report: Notice the Failed Request count. What the hey? Is WebAPI failing on roughly 10% of requests when sending JSON? Turns out: No it's not! But it took some sleuthing to figure out why it reports these failures. At first I thought that Web API was failing, and so to make sure I re-ran the test with Fiddler attached and runiisning the ab.exe test by using the -X switch: ab.exe -n100 -c10 -X localhost:8888 http://localhost/aspnetperf/api/HelloWorldJson which showed that indeed all requests where returning proper HTTP 200 results with full content. However ab.exe was reporting the errors. After some closer inspection it turned out that the dates varying in size altered the response length in dynamic output. For example: these two results: {"Id":10,"Name":"Rick","Entered":"2012-09-04T10:57:24.841926-10:00"} {"Id":10,"Name":"Rick","Entered":"2012-09-04T10:57:24.8519262-10:00"} are different in length for the number which results in 68 and 69 bytes respectively. The same URL produces different result lengths which is what ab.exe reports. I didn't notice at first bit the same is happening when running the ASHX handler with JSON.NET result since it uses the same serializer that varies the milliseconds. Moral: You can typically ignore Length failures in Apache Benchmark and when in doubt check the actual output with Fiddler. Note that the other failure values are accurate though. Another interesting Side Note: Perf drops over Time As I was running these tests repeatedly I was finding that performance steadily dropped from a startup peak to a 10-15% lower stable level. IOW, with Web API I'd start out with around 6500 req/sec and in subsequent runs it keeps dropping until it would stabalize somewhere around 5900 req/sec occasionally jumping lower. For these tests this is why I did the IIS RESET and warm up for individual tests. This is a little puzzling. Looking at Process Monitor while the test are running memory very quickly levels out as do handles and threads, on the first test run. Subsequent runs everything stays stable, but the performance starts going downwards. This applies to all the technologies - Handlers, Web Forms, MVC, Web API - curious to see if others test this and see similar results. Doing an IISRESET then resets everything and performance starts off at peak again… Summary As I stated at the outset, these were informal to satiate my curiosity not to prove that any technology is better or even faster than another. While there clearly are differences in performance the differences (other than WCF REST which was by far the slowest and the raw handler which was by far the highest) are relatively minor, so there is no need to feel that any one technology is a runaway standout in raw performance. Choosing a technology is about more than pure performance but also about the adequateness for the job and the easy of implementation. The strengths of each technology will make for any minor performance difference we see in these tests. However, to me it's important to get an occasional reality check and compare where new technologies are heading. Often times old stuff that's been optimized and designed for a time of less horse power can utterly blow the doors off newer tech and simple checks like this let you compare. Luckily we're seeing that much of the new stuff performs well even in V1.0 which is great. To me it was very interesting to see Web API perform relatively badly with plain string content, which originally led me to think that Web API might not be properly optimized just yet. For those that caught my Tweets late last week regarding WebAPI's slow responses was with String content which is in fact considerably slower. Luckily where it counts with serialized JSON and XML WebAPI actually performs better. But I do wonder what would make generic string content slower than serialized code? This stresses another point: Don't take a single test as the final gospel and don't extrapolate out from a single set of tests. Certainly Twitter can make you feel like a fool when you post something immediate that hasn't been fleshed out a little more <blush>. Egg on my face. As a result I ended up screwing around with this for a few hours today to compare different scenarios. Well worth the time… I hope you found this useful, if not for the results, maybe for the process of quickly testing a few requests for performance and charting out a comparison. Now onwards with more serious stuff… Resources Source Code on GitHub Apache HTTP Server Project (ab.exe is part of the binary distribution)© Rick Strahl, West Wind Technologies, 2005-2012Posted in ASP.NET  Web Api   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • Exploring TCP throughput with DTrace (2)

    - by user12820842
    Last time, I described how we can use the overlap in distributions of unacknowledged byte counts and send window to determine whether the peer's receive window may be too small, limiting throughput. Let's combine that comparison with a comparison of congestion window and slow start threshold, all on a per-port/per-client basis. This will help us Identify whether the congestion window or the receive window are limiting factors on throughput by comparing the distributions of congestion window and send window values to the distribution of outstanding (unacked) bytes. This will allow us to get a visual sense for how often we are thwarted in our attempts to fill the pipe due to congestion control versus the peer not being able to receive any more data. Identify whether slow start or congestion avoidance predominate by comparing the overlap in the congestion window and slow start distributions. If the slow start threshold distribution overlaps with the congestion window, we know that we have switched between slow start and congestion avoidance, possibly multiple times. Identify whether the peer's receive window is too small by comparing the distribution of outstanding unacked bytes with the send window distribution (i.e. the peer's receive window). I discussed this here. # dtrace -s tcp_window.d dtrace: script 'tcp_window.d' matched 10 probes ^C cwnd 80 10.175.96.92 value ------------- Distribution ------------- count 1024 | 0 2048 | 4 4096 | 6 8192 | 18 16384 | 36 32768 |@ 79 65536 |@ 155 131072 |@ 199 262144 |@@@ 400 524288 |@@@@@@ 798 1048576 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 3848 2097152 | 0 ssthresh 80 10.175.96.92 value ------------- Distribution ------------- count 268435456 | 0 536870912 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5543 1073741824 | 0 unacked 80 10.175.96.92 value ------------- Distribution ------------- count -1 | 0 0 | 1 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 3 512 | 0 1024 | 0 2048 | 4 4096 | 9 8192 | 21 16384 | 36 32768 |@ 78 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5391 131072 | 0 swnd 80 10.175.96.92 value ------------- Distribution ------------- count 32768 | 0 65536 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 5543 131072 | 0 Here we are observing a large file transfer via http on the webserver. Comparing these distributions, we can observe: That slow start congestion control is in operation. The distribution of congestion window values lies below the range of slow start threshold values (which are in the 536870912+ range), so the connection is in slow start mode. Both the unacked byte count and the send window values peak in the 65536-131071 range, but the send window value distribution is narrower. This tells us that the peer TCP's receive window is not closing. The congestion window distribution peaks in the 1048576 - 2097152 range while the receive window distribution is confined to the 65536-131071 range. Since the cwnd distribution ranges as low as 2048-4095, we can see that for some of the time we have been observing the connection, congestion control has been a limiting factor on transfer, but for the majority of the time the receive window of the peer would more likely have been the limiting factor. However, we know the window has never closed as the distribution of swnd values stays within the 65536-131071 range. So all in all we have a connection that has been mildly constrained by congestion control, but for the bulk of the time we have been observing it neither congestion or peer receive window have limited throughput. Here's the script: #!/usr/sbin/dtrace -s tcp:::send / (args[4]-tcp_flags & (TH_SYN|TH_RST|TH_FIN)) == 0 / { @cwnd["cwnd", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_cwnd); @ssthresh["ssthresh", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_cwnd_ssthresh); @unacked["unacked", args[4]-tcp_sport, args[2]-ip_daddr] = quantize(args[3]-tcps_snxt - args[3]-tcps_suna); @swnd["swnd", args[4]-tcp_sport, args[2]-ip_daddr] = quantize((args[4]-tcp_window)*(1 tcps_snd_ws)); } One surprise here is that slow start is still in operation - one would assume that for a large file transfer, acknowledgements would push the congestion window up past the slow start threshold over time. The slow start threshold is in fact still close to it's initial (very high) value, so that would suggest we have not experienced any congestion (the slow start threshold is adjusted when congestion occurs). Also, the above measurements were taken early in the connection lifetime, so the congestion window did not get a changes to get bumped up to the level of the slow start threshold. A good strategy when examining these sorts of measurements for a given service (such as a webserver) would be start by examining the distributions above aggregated by port number only to get an overall feel for service performance, i.e. is congestion control or peer receive window size an issue, or are we unconstrained to fill the pipe? From there, the overlap of distributions will tell us whether to drill down into specific clients. For example if the send window distribution has multiple peaks, we may want to examine if particular clients show issues with their receive window.

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  • Linux: Limiting data throughput (pipe) in bytes per second?

    - by sdaau
    Hi all, I was wandering if there is a Linux program that can limit data throughput of a pipe - in actual bytes per second?. From what I gather, applicable for the purposes would be bfr, however, it has been removed from Debian (Removal candidate: bfr) cpipe, however, it seems the lowest resolution it will support is kB/s, meaning that buffer writes can still reach MB/s ([SOLVED] Is there a program to limit terminal pipe speed? - Page 2 - Ubuntu Forums) What I'd want is to be able to specify something like cat example.txt | ratelimit -Bps 100 > /dev/ttyUSB0 ... and actually have a single byte from example.txt sent each 1/100 = 0.01 sec (or 10 ms) to 'output'.. Thanks in advance for any suggestions, Cheers!

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  • to measure throughput of testing device connect to server via AP

    - by samantha
    Description of project- I have a test tool to which DUT connects. The test tool has an access point in it and once DUT get connected to it via mac address we check RSSI and some other features of WiFi of DUT. Now I am wondering is there is any way I can measure throughput of Device under test via mac address of DUT from server side. Test-tool has LINUX fedora 11 in it and major coding is done in c/C++ and json command. Previously, I have tried to install ftp server on test-tool and DUT can connect to it and we can measure the throughput or data transfer rate, but this is not feasible solution as it requires lot of intervention from DUT. What I am interested in is 1) To run some script on server side /test tool and it gives me throughput of bandwidth of connected device may be via mac address of DUT OR 2) Server script transfer some files/packets to DUT and we can measure the throughput. Coding is not a major challenge at this stage , I just need some tool which requires minimum intervention from DUT.

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  • How the SPARC T4 Processor Optimizes Throughput Capacity: A Case Study

    - by Ruud
    This white paper demonstrates the architected latency hiding features of Oracle’s UltraSPARC T2+ and SPARC T4 processors That is the first sentence from this technical white paper, but what does it exactly mean? Let's consider a very simple example, the computation of a = b + c. This boils down to the following (pseudo-assembler) instructions that need to be executed: load @b, r1 load @c, r2 add r1,r2,r3 store r3, @a The first two instructions load variables b and c from an address in memory (here symbolized by @b and @c respectively). These values go into registers r1 and r2. The third instruction adds the values in r1 and r2. The result goes into register r3. The fourth instruction stores the contents of r3 into the memory address symbolized by @a. If we're lucky, both b and c are in a nearby cache and the load instructions only take a few processor cycles to execute. That is the good case, but what if b or c, or both, have to come from very far away? Perhaps both of them are in the main memory and then it easily takes hundreds of cycles for the values to arrive in the registers. Meanwhile the processor is doing nothing and simply waits for the data to arrive. Actually, it does something. It burns cycles while waiting. That is a waste of time and energy. Why not use these cycles to execute instructions from another application or thread in case of a parallel program? That is exactly what latency hiding on the SPARC T-Series processors does. It is a hardware feature totally transparent to the user and application. As soon as there is a delay in the execution, the hardware uses these otherwise idle cycles to execute instructions from another process. As a result, the throughput capacity of the system improves because idle cycles are no longer wasted and therefore more jobs can be run per unit of time. This feature has been in the SPARC T-series from the beginning, so why this paper? The difference with previous publications on this topic is in the amount of detail given. How this all works under the hood is fully explained using two example programs. Starting from the assembly language instructions, it is demonstrated in what way these programs execute. To really see what is happening we go down to the processor pipeline level, where the gaps in the execution are, and show in what way these idle cycles are filled by other copies of the same program running simultaneously. Both the SPARC T4 as well as the older UltraSPARC T2+ processor are covered. You may wonder why the UltraSPARC T2+ is included. The focus of this work is on the SPARC T4 processor, but to explain the basic concept of latency hiding at this very low level, we start with the UltraSPARC T2+ processor because it is architecturally a much simpler design. From the single issue, in-order pipelines of this processor we then shift gears and cover how this all works on the much more advanced dual issue, out-of-order architecture of the T4. The analysis and performance experiments have been conducted on both processors. The results depend on the processor, but in all cases the theoretical estimates are confirmed by the experiments. If you're interested to read a lot more about this and find out how things really work under the hood, you can download a copy of the paper here. A paper like this could not have been produced without the help of several other people. I want to thank the co-author of this paper, Jared Smolens, for his very valuable contributions and our highly inspiring discussions. I'm also indebted to Thomas Nau (Ulm University, Germany), Shane Sigler and Mark Woodyard (both at Oracle) for their feedback on earlier versions of this paper. Karen Perkins (Perkins Technical Writing and Editing) and Rick Ramsey at Oracle were very helpful in providing editorial and publishing assistance.

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  • Throughput measurements

    - by dotsid
    I wrote simple load testing tool for testing performance of Java modules. One problem I faced is algorithm of throughput measurements. Tests are executed in several thread (client configure how much times test should be repeated), and execution time is logged. So, when tests are finished we have following history: 4 test executions 2 threads 36ms overall time - idle * test execution 5ms 9ms 4ms 13ms T1 |-*****-*********-****-*************-| 3ms 6ms 7ms 11ms T2 |-***-******-*******-***********-----| <-----------------36ms---------------> For the moment I calculate throughput (per second) in a following way: 1000 / overallTime * threadCount. But there is problem. What if one thread will complete it's own tests more quickly (for whatever reason): 3ms 3ms 3ms 3ms T1 |-***-***-***-***----------------| 3ms 6ms 7ms 11ms T2 |-***-******-*******-***********-| <--------------32ms--------------> In this case actual throughput is much better because of measured throughput is bounded by the most slow thread. So, my question is how should I measure throughput of code execution in multithreaded environment.

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  • What is a usable throughput for a home media server

    - by Craig
    I am looking to setup a home server that will act as a media server. This will include both video (possibly HD) and audio. The clients will be a fun mix of hardware but that is a different question. What I want to know is what is the minimum throughput for streaming video without hitches? Is there a "sweet" spot for throughput (price vs. throughput)? I am determining my budget for this "upgrade" and I need to evaluate wether or not upgrading to a 1 Gbps home LAN is required. Sure, it would be sweet and easily handle the traffic but I don't want to do it unless it is necesary.

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  • Throughput = BS * IOPS?

    - by Marvin
    I've seen in many places that throughput = bs * iops should be true. For example writing at 128k block size to a SAS disk that can support 190 IOPS should give a throughput of ~23 MBps - 23.75(MBs) = 128(BS)*190(SAS-15 IOPS)/1024. Now when I tested it in a VM against a monster NetApp filer I got theses results: # dd if=/dev/zero of=/tmp/dd.out bs=4k count=2097152 8589934592 bytes (8.6 GB) copied, 61.5996 seconds, 139 MB/s To view the IO rate of the VM I used iostat and esxtop, and they both showed around 250 IOPS. So to my understanding the throughput was supposed to be ~1000k: 1000(KBs) = 4(BS)*250(IOPS). dd of 8GB is twice the size of RAM of course, so no page caching here. What am I missing? Thanks!

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  • Optimizing Transaction Log Throughput

    As a DBA, it is vital to manage transaction log growth explicitly, rather than let SQL Server auto-growth events "manage" it for you. If you undersize the log, and then let SQL Server auto-grow it in small increments, you'll end up with a very fragmented log. Examples in the article, extracted from SQL Server Transaction Log Management by Tony Davis and Gail Shaw, demonstrate how this can have a significant impact on the performance of any SQL Server operations that need to read the log.

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  • Maximizing Throughput with TVPs

    TVPs offer several performance optimization possibilities that other bulk operations do not allow, and these operations may allow for TVP performance to exceed other bulk operations by an order of magnitude, especially for a pattern where subsets of the data are frequently updated. Want to work faster with SQL Server?If you want to work faster try out the SQL Toolbelt. "The SQL Toolbelt provides tools that database developers as well as DBAs should not live without." William Van Orden. Download the SQL Toolbelt here.

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  • Optimizing Transaction Log Throughput

    As a DBA, it is vital to manage transaction log growth explicitly, rather than let SQL Server auto-growth events "manage" it for you. If you undersize the log, and then let SQL Server auto-grow it in small increments, you'll end up with a very fragmented log. This article demonstrates how this can have a significant impact on the performance of any SQL Server operations that need to read the log.

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  • How does iperf calculate throughput and jitter?

    - by Someone
    I've read that iperf basically tries to send as much information down a connection as quickly as possible reporting on the throughput achieved. This tool is especially useful in determining the volume of data that links between two machines can supply. is it possible to gather the same results by sending regular data, as in not testing data? what I'm trying to do is this; sending data in the foreground while in the back ground gather statistics (throughput and jitter). so can anyone tell me how iperf calculates these two values ?

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  • TCP/IP & throughput between FreeNAS (BSD) server & other LAN machines

    - by Tim Dickerson
    I have got a question for someone that knows BSD a bit better than me that are in regards to my LAN setup at home/work here outside Chicago. I can't seem to fully optimize my network's (LAN) thoughput via my FreeNAS (BSD based) file server. It runs with the latest FreeBSD release which is modified to support several protocols for file transfers and more. Every machine that is behind my Smoothwall (Linux based) router is on the usual 192.168.0.x subnet and for most part works just fine. Behind the Smoothwall box, all machines are connected to a GB HP unmanaged switch. I host a large WISP here and have an OC-3 connection here at home/work and have no issues with downloading/uploading from/to the 'net'. My problem is with throughput. When I try and transfer large files...really any for that matter..between any of the machines to/and from the FreeNAS server via FTP, the max throughput I can achieve say between a Win 7 or a Linux box is ~65Mbit/sec. All machines are running Intel Pro 1000 GB NIC's and all cable is CAT6. Each is set to 'auto negotiation' and each shows 1500 MTU Full Duplex @1GB so I know the hardware is okay. I have not adjusted the MTU on any machine as I understand it to be pointless unless certain configurations are used (I assume I am not one of those). My settings for the FreeNAS machine are the following: # FreeNAS /etc/sysctl.conf - pertinent settings shown kern.ipc.maxsockbuf=262144 kern.ipc.nmbclusters=32768 kern.ipc.somaxconn=8192 kern.maxfiles=65536 kern.maxfilesperproc=32768 net.inet.tcp.delayed_ack=0 net.inet.tcp.inflight.enable=0 net.inet.tcp.path_mtu_discovery=0 net.inet.tcp.recvbuf_auto=1 net.inet.tcp.recvbuf_inc=524288 net.inet.tcp.recvbuf_max=16777216 net.inet.tcp.recvspace=65536 net.inet.tcp.rfc1323=1 net.inet.tcp.sendbuf_inc=16384 net.inet.tcp.sendbuf_max=16777216 net.inet.tcp.sendspace=65536 net.inet.udp.recvspace=65536 net.local.stream.recvspace=65536 net.local.stream.sendspace=65536 net.inet.tcp.hostcache.expire=1 From what I can tell, that looks to be a somewhat optimized profile for a typical BSD machine acting as a server for a LAN. I might be wrong and just wanted to find out from someone that knows BSD better than I do if indeed that is ok or if something is out of tune or what. Are there other ways I would find better for P2P file transfers? I honestly do not know what I SHOULD be looking for with respect to throughput between the NAS box and another client when xferring files via FTP, but I am told that what I get on average (40-70MB/sec) is too low for what it could be. I have thought about adding another NIC in the FreeNAS box as well as the Win7 machine and use a X-over cable via a static route, but wanted to check with someone first to see if that might be worth it or not. I don't know if doing that would bypass the HP GB switch and allow for a machine to machine xfer anyways. The FTP client I use is: Filezilla and have tried both active and passive modes with no real gain over each other. The NAS box runs ProFTPD.

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  • Poor TCP loopback throughput on Windows

    - by Yodan Tauber
    I measured the throughput of a locally bound TCP socket connection on my computer (Intel Q9550, 64 GB RAM, Windows XP 64 bit) using iperf. I got dissatisfying results (around 1.6 Gbit/s) each time, no matter how I tweaked the TCP settings (buffer length, window size, max segment size, no delay). I got similar results when I tried netperf. Now, I understand (from sources like these) that the average throughput of a loopback connection should be around 5 Gbit/s. What could be the reasons for such poor performance?

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