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2

8 benefits of active monitoring in WebRTC

Call it WebRTC active monitoring or WebRTC synthetic monitoring, the concept is rather simple. What you are trying to do is run a scenario from real browsers the same way a user would. Why? So you can see (=track and monitor) your WebRTC application the way your customers do.

You know pingdom? It is a service that pings your website every couple of seconds. If it fails to get a response – you receive an email that your website is down. A simple and straightforward solution. There are many similar services out there and they work beautifully. If all you’re after is to answer the question “is my website still up?”

This, though is different than asking the question “is my website working properly?”

How do you go about monitoring a website for that? You dig one or two levels deeper, specifically, by putting on probes that load your webpages and look for indication that these pages are fresh and not erroneous. Why? Because a ping test of a website can be happy with this kind of a result:

That’s Google Calendar being down a few weeks back. I am not sure that a ping test would notice that, as a page does load.

The path to synthetic/active monitoring

What would an IT person do? Add more metrics that he can track. CPU use, memory use, network traffic. And then add more metrics from the application: page views, open sessions, etc.

These metrics are prone to two problems:

  1. Seasonality changes their behavior. Think weekend or holiday traffic versus regular days, or opening hours versus night time
  2. The lights might be on but there’s nobody home. All looks fine, but somehow a user is unable to login or get connected to a certain service due to breakage in the connection of two internal systems. Since monitoring is done on low level metrics, such cases might be missed

The next step for our IT person would be to have a probe act like a user to going through the system to understand its behavior. These probes conduct synthetic monitoring, where they act like real users going through the system.

The same applies to WebRTC applications as well.

8 benefits of active monitoring in WebRTC

Call it WebRTC active monitoring or WebRTC synthetic monitoring, the concept is rather simple. What you are trying to do is run a scenario from real browsers the same way a user would. Why? So you can see (=track and monitor) your WebRTC application the way your customers do. And once you automate it and run it frequently, you can gain insights and understanding that you just can’t get in any other way.

Here are 8 benefits that got customers like Vidyo to use testRTC for monitoring their WebRTC cloud deployment:

#1 – Predictability and Objectivity

When you run an active monitor you are in control. You know where the probes are coming from, what is the performance of the machines they use offer, and what their network conditions are. And if you don’t, then running that active monitor in the same scenario a couple of times will create the baseline you need.

With that information, you can now run the scenario as an active monitor, and if all goes well the results will be consistent. The moment something changes – there’s a pretty high level of confidence that something changed in your WebRTC deployment. That’s predictability.

The fact that the metrics collected and analyzed results are based on machine automation, you also gain objectivity. While it will be hard to say how bad a jitte value of 120 is versus 100, it will be easy to say that if you had a jitter value of 100 for a few months and now that has changed to 120 in the monitor you are running, then things changed for the worse, and it would be wise to check why.

#2 – End-to-End

When we deploy a monitor with a new client of ours at testRTC there’s almost always a learning period of a month or two. At that time, we need to assist our client to fine tune and tweak the script written for the monitor.

Common things we need to do is slow down button clicking or add retries in certain strategic places (like login procedures). Why? Because production WebRTC services sometimes receive 502 when people try to login, connect or start sessions. Real users would simply refresh the page by clicking F5 or retry clicking a button.

In some cases, our clients would go about hunting these bugs and fix them. In others, we’d build these retry mechanisms into the script used by the monitor.

The thing is though, that when a WebRTC session fails, it can fail a lot before it even started. Or it can work nicely, but screen sharing fails. Or screen sharing will work but PSTN dial-in won’t. Being able to define the most important WebRTC scenarios and synthetically monitor for them gives you an end-to-end solution.

#3 – Be the first to know

You need to be the first to know when there is an issue. That issue can be with the login, directory service, session initialization, media quality or any other problem that might arise.

If you are operating a contact center, then calls take place at certain times of the day (office hours). Understanding potential failures before they happen simply by running a monitor prior to a Monday morning shift starting the day would give you more time to resolve issues.

If you have millions of calls taking place a day on your system, then this might not be an issue for you – or more likely, your users would complain at the same time your service monitoring will notice a failure. In such a case, other reasons such as predictability would make more sense to using synthetic monitoring. This is doubly so since using predictable probes that create synthetic sessions should result consistent outcomes, as opposed to real users where you lack any control over their machine, location and network.

#4 – Simplicity

There’s something to be said about simple approaches for complex problems.

When users can’t connect to your service, do you know why that is? If they complain about quality, is it because of their device, network connection or your service? How do you even go about analyzing this?

WebRTC synthetic monitoring reduces a lot of the variables and brings predictability with it into the process. What you end up collecting and how you serve that to the IT person in charge is also quite important – there are so many metrics and parameters to look at with WebRTC that many don’t find their way around.

What we’re razor focused in testRTC is in making the analysis process as simple as possible to our clients. Letting them glean the insights they need with the least amount of effort on their part. Our upcoming release goes in that trend and is already being trialed by a few of our clients.

#5 – Debuggability

The monitor failed or alerted at an issue. Great. Now what? How do you make that alert an actionable one?

With passive monitoring of live users, there’s very little you can do in a lot of cases. Quality is a subjective thing that is affected quite a lot by the user’s own device and network. Move a meter or two farther away from your current position while in a call, and your Wifi connection might become unusable. In my house, using Wifi in the bedroom is quite the challenge. The living room and my home office? They’re guaranteed to give high network quality. At least up to the carrier. My desktop has its good days and bad days, depending on the number of Chrome tabs opened and the number of days since the last reboot.

If you run a synthetic monitor for WebRTC, then there are quite a few things at your disposal. Here are some that we’ve implemented in testRTC for our clients:

  • Collect all possible data, so developers can look at logs and figure out the issues. This includes WebRTC metrics, browser console logs, network events log, browser performance data and screenshots
  • Visualize the scenario and the metrics collected, keeping it simple at first glace with high level graphs and aggregations while enabling drill down to the minute details
  • Automate threshold on metrics, to make sure tests warn or fail on certain use cases and conditions that are suitable for you
  • Grab a screenshot at the time of failure, so you can see the moment the scenario fails
  • Execute the scenario again, so you can see the failure (since the scenario and probes are predictable, there’s a high likelihood the failure will occur again)
  • Join a running synthetic session via VNC, so you can see for yourself how the session progresses

#6 – No instrumentation

Synthetic monitoring requires no instrumentation of your service.

Since you end up using real browsers, running real scenarios, the only thing you’ll probably need is create certain users for running the monitor and that’s about it.

There’s no code you’ll need to inject into your service. No js file to include. No SDK to compile into your app.

That means it is faster to deploy to production than alternatives and the potential effect it has on your service due to the addition of external code is non-existent, since you’ve changed nothing in the code.

#7 – Privacy

A synthetic monitor collects synthetic metrics. It doesn’t sit on your live users, so there’s no live user data collection taking place. There’s also no real indication of the size of your deployment, the trajectory and growth of your service or anything similar associated with it.

We’ve seen reluctance of clients to share such data with cloud based services. These mostly stem from legal issues such as where the data gets collected and stored, but also from a business perspective of having a third party trusted with the day to day communications that takes place. In many cases, companies are happier having this part of the operation take place in-house.

With an active monitor, the only data collected and analyzed is the data generated by the browser of the active monitor itself and no one else. The users used by the active monitor are dummy users created for that purpose only.

#8 – Fixed investment

Talking about predictability… as your service grows, a WebRTC active monitor act in the same manner. This means your investment in running the monitor won’t be changing either. This is never the case with a passive monitor, where pricing is based on the size of the user base as well as the amount of traffic.

That means you can budget and plan ahead for longer periods of time at relatively low investment.

When will you need to grow your investment? When you want to deepen your analysis. This is done by deploying more monitors (to run from more geographic locations or to hit different data centers of your service), increasing the frequency of the monitors (to get alerted on issues earlier) or when you beef up monitors (by adding more probes to test larger video group calls for example).

testRTC’s active monitoring

If you are in need of better visibility of your WebRTC application, then by all means – explore passive monitoring and deploy it. But also check how active monitoring can improve your day-to-day operations and in the end, improve uptime and media quality for your users.

We’re here to help, so contact us for a demo.

Testing Firefox has just become easier (and other additions in testRTC)

We’ve pushed a new release for our testRTC service last month. This one has a lot of small polishes along with one large addition – support for Firefox.

I’d like to list some of the things you’ll be able to find in this new release.

Firefox

When we set out to build testRTC, we knew we will need to support multiple browsers. We started off with Chrome (just like most companies building applications with WebRTC), and somehow drilled down into more features, beefing up our execution, automation and analysis capabilities.

We tried adding Firefox about two years ago (and failed). This time, we’re taking it in “baby steps”. This first release of Firefox brings with it solid audio support with rudimentary video support. We aren’t pushing our own video content but rather generating it ad-hoc. This results less effective bitrates that we can reach.

The challenge with Firefox lies in the fact that it has no fake media support the same way Chrome does – there is no simple way to have it take up media files directly instead of the camera. We could theoretically create virtual camera drivers and work our way from there, but that’s exactly where we decided to stop. We wanted to ship something usable before making this a bigger adventure (which was our mistake in the past).

Where will you find Firefox? In the profile planning section under the test editor:

When you run the tests, you might notice that we alternate the colors of the video instead of pushing real video into it. Here’s how it looks like running Jitsi between Firefox and Chrome:

That’s a screenshot we’ve taken inside the test. That cyan color is what we push as the video source from Firefox. This will be improved over time.

On the audio side you can see the metrics properly:

If you need Firefox, then you can now start using testRTC to automate your WebRTC testing on Firefox.

How we count minutes

Up until now, our per minute pricing for tests was built around the notion of a minimum length per test of 10 minutes. If you wanted a test with 4 probes (that’s 4 browsers) concurrently, we calculated it as 4*10=40 minutes even if the test duration was only 3 minutes.

That has now changed. We are calculating the length of tests without any specific minimum. The only things we are doing is:

  1. Length is rounded up towards the nearest minute. If you had a test that is 2:30 minutes long, we count it as 3 minutes
  2. We add to the test length our overhead of initiation for the test and teardown. Teardown includes uploading results to our servers and analyzing them. It doesn’t add much for smaller tests, but it can add a few minutes on the larger tests

End result? You can run more tests with the minutes allotted to your account.

This change is automatic across all our existing customers – there’s nothing you need to do to get it.

Monitoring tweaks

We’ve added two new capabilities to monitoring, due to requests of our customers.

#1 – Automated run counter

At times, you’ll want to alternate information you use in a test based on when it gets running.

One example is using multiple users to login to a service. If you run a high frequency monitor, which executes a test every 2-5 minutes, using the same user won’t be the right thing to do:

  • You might end up not leaving the first session when running the next monitor a couple of minutes later
  • Your service might leave session information for longer (webinars tend to do that, waiting for the instructors to join the same session for ten or more minutes after he leaves)
  • If a monitor fails, it might cause a transient state for that user until some internal timeout

For these, we tend to suggest clients to use multiple users and alternate between them as they run the monitors.

Another example is when you want in each round of execution to touch a different part of your infrastructure – alternating across your data centers, machines, etc.

Up until today, we’ve used to do this using Firebase as an external database source that knows which user was last used – we even have that in our knowledge base.

While it works well, our purpose is to make the scripts you write shorter and easier to maintain, so we added a new (and simple) environment variable to our tests called RTC_RUN_COUNT. The only thing it does is return the value of an iterator indicating how many times the test has been executed – either as a test or as a monitor.

It is now easy to use by calculating the modulu value of RTC_RUN_COUNT with the number of users you created.

You can learn more about RTC_RUN_COUNT and our other environment variables in our knowledge base.

#2 – Additional information

We had a customer recently who wanted to know within every run of a monitor specific parameters of that run – in his case, it was the part of his infrastructure that gets used during the execution.

He could have used rtcInfo(), but then he’ll need to dig into the logs to find that information, which would take him too long. He needed that when the monitors are running in order to quickly pinpoint the source of failures on his end.

We listened, and added a new script command – rtcSetAdditionalInfo(). Whatever you place in that command during runtime gets stored and “bubbled up” – to the top of test run results pages as well as to the test results webhook. This means that if you connect the monitor to your own monitoring dashboards for the service, you can insert that specific information there, making it easily accessible to your DevOps teams.

Onwards

We will be looking for bugs (and fixing them) around our Firefox implementation, and we’re already hard at work on a totally new product and on some great new analysis features for our test results views.

If you are looking for a solid, managed testing and monitoring solution for your WebRTC application, then try us out.

Monitoring WebRTC apps just got a lot more powerful

As we head into 2019, I noticed that we haven’t published much around here. We doubled down on helping our customers (and doing some case studies with them) and on polishing our service.

In the recent round of updates, we added 3 very powerful capabilities to testRTC that can be used in both monitoring and testing, but make a lot of sense for our monitoring customers. How do I know that? Because the requests for these features came from our customers.

Here’s what got added in this round:

1. HAR files support

HAR stands for HTTP Archive. It is a file format that browsers and certain viewer apps support. When your web application gets loaded by a browser, all network activity gets logged by the browser and can be collected by a HAR file that can later be retrieved and viewed.

Our focus has always been WebRTC, so collecting network traffic information that isn’t directly WebRTC wasn’t on our minds. This changed once customers approached us asking for assistance with sporadic failures that were hard to reproduce and hard to debug.

In one case, a customer knew there’s a 502 failure due to the failure screenshot we generate, but it wasn’t that easy to know which of his servers and services was the one causing it. Since the failure is sporadic and isn’t consistent, he couldn’t get to the bottom of it. By using the HAR files we can collect in his monitor, the moment this happens again, he will have all the network traces for that 502, making it easier to catch.

Here’s how to enable it on your tests/monitors:

Go to the test editor, and add to the run options the term #har-file

 

Once there and the test/monitor runs next, it will create a new file that can be found under the Logs tab of the test results for each probe:

We don’t handle visualization for HAR files for the moment, but you can download the file and place it on a visual tool.

I use netlog-viewer.

Here’s what I got for appr.tc:

2. Retry mechanism

There are times when tests just fail with no good reason. This is doubly true for automating web UI, where minor time differences may cause problems or when user behavior is just different than an automated machine. A good example is a person who couldn’t login – usually, he will simply retry.

When running a monitor, you don’t want these nagging failures to bog you down. What you are most interested in isn’t bug squashing (at least not everyone) it is uptime and quality of service. Towards that goal, we’ve added another run option – #try

If you add this run option to your monitor, with a number next to it, that monitor will retry the test a few more times before reporting a failure. #try:3 for example, will retry twice the same script before reporting a failure.

What you’ll get in your monitor might be something similar to this:

The test reports a success, and the reason indicates a few times where it got retried.

3. Scoring of monitor runs

We’ve started to add a scoring system to our tests. This feature is still open only to select customers (want to join in on the fun? Contact us)

This scoring system places a test based on its media metrics collected on a scale of 0-10. We decided not to go for the traditional MOS scoring of 1-5 because of various reasons:

  1. MOS scoring is usually done for voice, and we want to score video
  2. We score the whole tests and not only a single channel
  3. MOS is rather subjective, and while we are too, we didn’t want to get into the conversation of “is 3.2 a good result or a bad result?”

The idea behind our scores is not to look at the value as good or bad (we can’t tell either) but rather look at the difference between the value across probes or across runs.

Two examples of where it is useful:

  1. You want to run a large stress test. Baseline it with 1-2 probes. See the score value. Now run with 100 or 1000 probes. Check the score value. Did it drop?
  2. You are running a monitor. Did today’s runs fair better than yesterday’s runs? Worse? The same?

What we did in this release was add the score value to the webhook. This means you can now run your monitors and collect the media quality scores we create and then trendline them in your own monitoring service – splunk, elastic search, datadog, whatever.

Here’s how the webhook looks like now:

The rank field in the webhook indicates the media score of this session. In this case, it is an AppRTC test that was forced to run on simulated 3G and poor 4G networks for the users.

As with any release, a lot more got squeezed into the release. These are just the ones I wanted to share here this time.

If you are interested in a monitoring service that provides predictable synthetics WebRTC clients to run against your service, checking for uptime and quality – check us out.

How Nexmo Integrated testRTC into their Test Automation for the Nexmo Voice API

Nexmo found in testRTC a solution to solve its end-to-end media testing challenges for their Nexmo Voice API product, connecting PSTN to WebRTC and vice versa.

Nexmo is one of the top CPaaS vendors out there providing cloud communication APIs to developers, enabling enterprises to add communication capabilities into their products and applications.

One of Nexmo’s capabilities involves connecting voice calls between regular phone numbers (PSTN) to browsers (using WebRTC) and vice versa. This capability is part of the Nexmo Voice API.

Testing @ Nexmo

Catering to so many customers with ongoing deployments to production means that Nexmo needs to take testing seriously. One of the things Nexmo did early on was introduce automated testing, using the pytest framework. Part of this automated testing includes a set of regression tests –  a huge amount of tests that provide very high test coverage. Regression tests get executed whenever the Nexmo team has a new version to release, but these tests can also be launched “on demand” by any engineer, they can also be triggered by the Jenkins CI pipeline upon a merge to a particular branch.

At Nexmo, development teams are in charge of the quality of their code, so there is no separate QA team.

In many cases, launching these regression tests first creates a new environment, where the Nexmo infrastructure is launched dynamically on cloud servers. This enables developers to run multiple test sessions in parallel, each in front of their own sandboxed environment, running a different version of the service.

When WebRTC was added to Nexmo Voice API, there was a need to extend the testing environment to include support for browsers and for WebRTC technology.

On Selecting testRTC

“When it comes to debugging, when something has gone wrong, testRTC is the first place we’d go look. There’s a lot of information there”

Jamie Chapman, Voice API Engineer at Nexmo

Nexmo needed WebRTC end-to-end tests as part of their regression test suite for the Nexmo Voice API platform. These end-to-end tests were around two main scenarios:

  1. Dialing a call from PSTN and answering it inside a browser using WebRTC
  2. Calling a PSTN number directly from a browser using WebRTC

In both cases, their client side SDKs get loaded by a web page and tested as part of the scenario.

Nexmo ended up using testRTC as their tool of choice because it got the job done and it was possible to integrate it into their existing testing framework:

  • The python script used to define and execute a test scenario used testRTC’s API to dynamically create a test and run it on the testRTC platform
  • Environment variables specific to the dynamically created test environment got injected into the test
  • testRTC’s test result was then returned back to the python script to be recorded as part of the test execution result

This approach allowed Nexmo to integrate testRTC right into their current testing environment and test scripts.

Catering for Teams

The Voice API engineering team is a large oneAll these users have access to testRTC and they are able to launch regression tests that end up running testRTC scripts as well as using the testRTC dashboard to debug issues that are found.

The ability to have multiple users, each with their own credentials, running tests on demand when needed enabled increased productivity without dealing with coordination issues across the team members. The test results themselves get hosted on a single repository, accessible to the whole team, so all developers  can easily share faulty test results with the team .

Debugging WebRTC Issues

Nexmo has got regression testing for WebRTC off the ground by using testRTC. It does so by integrating with the testRTC APIs, scheduling and launching tests on demand from Nexmo’s own test environment. The tests today are geared towards providing end-to-end validation of media and connectivity between the PSTN network and WebRTC. Validation that testRTC takes care of by default.

When things break, developers check the results collected by testRTC. As Jamie Chapman, Voice API engineer at Nexmo said: “When it comes to debugging, when something has gone wrong, testRTC is the first place we’d go look. There’s a lot of information there”.

testRTC takes screenshots during the test run, as well as upon failure. It collects browser logs and webrtc-internals dump files, visualizing it all and making it available for debugging purposes. This makes testRTC a valuable tool in the development process at Nexmo.

On the Horizon

Nexmo is currently making use of the basic scripting capabilities of testRTC. It has invested in the API integration, but there is more that can be done.

Nexmo are planning to increase their use of testRTC in several ways in the near future:

7

How do WebRTC Media Servers Behave on Packet Loss?

Differently from each other.

Whenever I see people comparing WebRTC media servers, they tend to focus on scale:

– How many sessions can you cram in parallel?

– How many streams can you serve from a single machine?

– How much bitrate can you pump out?

All of these are very important questions – they end up in your sizing calculation that then go into your pricing model for your service. Oh, and we did cover this a bit here when talking about handling WebRTC browsers synchronization at scale.

Now that our new version is taking shape (still in staging, so if you want access – ping us), it is time to play a bit with a few new toys we’ve added for our beloved community of sadists (you may know them as test engineers, but the good ones are sadists – they like inflicting pain upon digital products and services).

What I am talking about here is a combination of two script commands we have:

  1. rtcEvent() – place a vertical event in the graphs
  2. rtcSetNetworkProfile() – change network profiles in runtime

You’ll see how it looks in a second.

What Packet Loss Does?

Packet loss is bad.

You don’t control it. And it can happen at any time. Come and go as it pleases.

The moment you have packet loss, there will be some degradation in the quality of the media. Lost packets means lost data. Means can’t playback something. It might be minor. It might be important.

Next thing that happens? WebRTC (or most other VoIP products for that matter) will start lowering bitrates. Why? Because it assumes there’s congestion on the network, and it is trying to play nice with everyone.

But what happens once that packet loss is gone? Does things go back to normal? And if they do, then how fast will that happen?

My Experiment

I decided to devise a simple enough experiment to get some answers here. I chose the following steps:

  1. Connect to a service
  2. Run for a full minute
  3. Set packet loss to 10% for a full minute
  4. Go back to normal – no packet loss
  5. Wait two minutes

That’s it. What I am interested in is less of what happens during the second minute, but more what happens in the last two minutes, and how that is different than what we have in the first minute of the session.

In general, I decided to place 5 users in the same session, to get that media server working a bit. And I also decided to focus on the SFU kind.

The services I tinkered with are:

  1. AppRTC, just as a baseline for this exercise
  2. Janus, an open source media framework, that can act as an SFU
  3. Jitsi Videobridge, an open source SFU
  4. mediasoup, a relatively new open source SFU
  5. SwitchRTC, a commercial SFU
  6. appear.in, a service that recently added its own self-developed SFU (in beta at the moment)

If you are looking for Kurento or other SFUs – they weren’t included not because I didn’t want to, but because there was no readily available installation out there that I could just use.

I’ll be happy to add more SFUs to the comparison, so give us a shout out if you want to run such an analysis.

Let the fun begin.

AppRTC – My Favorite Baseline

For our baseline, I decide to use AppRTC.

This time, I had to use only 2 browsers, as AppRTC doesn’t support any group calling capabilities.

What it does do is offer the vinyl WebRTC experience.

I started with writing a simple script to fit my needs:

var roomUrl = process.env.RTC_SERVICE_URL + "testRTC" + process.env.RTC_SESSION_IDX + '?vsc=VP8';

var agentType = Number(process.env.RTC_IN_SESSION_ID);
var recuperationTime = 60; // in seconds

client
   .rtcInfo(roomUrl)
   .rtcProgress('open ' + roomUrl)
   .url(roomUrl)
   .waitForElementVisible('body', 60000)
   .pause(2000)
   .click('#confirm-join-button')
   .waitForElementVisible('#videos', 20000)
// Minute 1
   .pause(recuperationTime * 500)
   .rtcScreenshot('Phase 1')
   .rtcProgress('Phase 1')
   .pause(recuperationTime * 500);

// Minute 2
   if (agentType === 1) {
   client
       .rtcEvent('10% Packet Loss start', 'global')
           .rtcSetNetworkProfile('custom', 'packet loss', 10, 'both', 'both'); // 10% packet loss
   }

client
   .pause(recuperationTime * 500)
   .rtcScreenshot('Phase 2')
   .rtcProgress('Phase 2')
   .pause(recuperationTime * 500)

   if (agentType === 1) {
    client
       .rtcSetNetworkProfile('') // back to pristine network conditions
       .rtcEvent('10% Packet Loss End', 'global');
   }

// Minute 3-4
client
   .pause(recuperationTime * 1000)
   .rtcScreenshot('Phase 3')
   .rtcProgress('Phase 3')
   .pause(recuperationTime * 1000);

A few things to note here:

  1. All test scripts on this post can be found on our github account. Easiest way to use them is to import them into your testRTC account
  2. I decided to force VP8 here. VP9 is erratic a bit in its bitrate so I wanted to go for VP8 – hence the addition of ‘?vsc=VP8’ in the first line of this script (check out all of AppRTC’s parameters here)
  3. When the second minute is up, the first probe in each session will generate a global rtcEvent and set packet loss in both directions to 10% (look at lines 23-27)
  4. After an additional second is over, the first probe in each session will generate another global rtcEvent and remove all packet loss and network constraints that might have been used (look at lines 35-39)

Running that using testRTC yields these results once you drill into one of these sessions:

Above you see two things:

  1. The green vertical lines – these are the result of the rtcEvent() calls
  2. The blue and red bars, showing incoming and outgoing packet loss percentage, which averages at 10%

Above you see the video bitrate graph, with the two horizontal lines on it.

Notice how the outgoing bitrate tries going up in the beginning and then drops from 2.5mbps to 1mbps in 60 seconds?

The other thing that interest me is the time it takes for WebRTC/AppRTC to get back to 2.5mbps. And that’s somewhere in the range of 15-20 seconds.

Oh, and because I know you’ll be interested in this – also remember this screenshot of the video average delay we had:

Before we move on to the media servers – remember that what I tried doing with AppRTC is provide a baseline. And the baseline here is “picture perfect”. I didn’t really expect any of the SFUs that I’ve used to be able to match AppRTC with its metrics.

Janus

Janus is an open source media server created and maintained by Meetecho.

They have an online demo running that supports a simple video room.

So we just hooked our script on top of that to get the results we needed. We aimed for 5 browsers in a single room – which will be the norm from now on in this article.

The Janus demo has somewhat of a single room, and I had to end up with a J3rry user in there, though he seemed harmless with no camera or bitrate in my session.

You can see above that the bitrates are rather low – around 140 kbps for each video stream coming into this room. And that’s even before I started adding packet loss.

During packet loss and after it, we “lost” two participants. Here’s a screenshot taken a minute after I stopped packet loss altogether:

The graphs in testRTC show a grim picture:

Janus reports packet losses at higher intervals than what WebRTC does, which is why we see the spikes on the outgoing reporting that go up to 50% and more. The weird thing is the two incoming channels that show around 10% of packet loss as well. Which is weird – more about this later.

Here’s how video bitrates look like for some of the streams (one outgoing and two incoming):

No change even though we have packet loss.

And here’s what happens in the two other incoming streams:

Apparently, these two incoming streams are the ones showing packet loss from the start. They somehow decided to drop to 0 the moment we cranked up the artificial packet loss from 0 to 10% – but never recuperated from it.

Looking at the average delay for the video…

Things can’t be good, but seems like this has nothing to do with my packet loss shenanigans.

It might be Janus and it might just be the demo machine. If I could, I’d reboot it and start all over again.

Jitsi

For me the Jitsi Videobridge is where I go first to run demos and tests on an SFU with testRTC:

  • It is out there
  • It is easy to automate
  • And I am a creature of habit…

To run our test here, we’ve directed 5 of our probes into a single room on the Jitsi meet online service/demo.

After a few attempts, I decided it would be better to disable simulcast, using this prefix to the URL: ‘#config.disableSimulcast=true’. I didn’t do it because simulcast is a bad thing, but because it made analyzing the results much harder for what I had in mind.

If we look at the packet loss graph, it will tell a similar story to what we’ve seen so far:

While there are some packet losses out of the one minute killzone I created, they are negligible (or at least sporadic). That negative values you see for packet losses in the red color? They are reports of the browser’s outgoing stream from the machine we induced packet loss on. This is most probably related to a Chrome bug (HT to Philipp Hancke).

I’ve split the video bitrate graphs here into two graphs – the outgoing one and the incoming ones since they tell two separate stories.

This one caught me by surprise – the outgoing bitrate shows no signs of a change due to packet loss. I wonder what Jitsi is doing (or not doing) to have packet loss ignored in such a way. So I decided to look at it from the receiving end of one of the other four browsers in the same session:

Bitrate drops to 0 for a duration of almost a full minute before coming back up.

Back to the browser with the trashed network, let’s see what happens to the incoming video streams:

Things drop down from around 2mbps to almost 0 on all incoming channels, taking around 40-60 seconds to get back to normal.

One last glance before we move on – check out video average delay:

Jitsi had some hard time recuperating from that packet loss.

It should be noted that I’ve played around with Jitsi before their recent updates – especially the ones including adaptivity.

Mediasoup

mediasoup is a rather new player in the open source SFU space. It is built in C++ as a Node.js module. After a quick Twitter chat, Iñaki Baz Castillo was kind enough to configure it to my needs (specifically, allowing for more bandwidth on the online demo).

Starting as always with packet loss:

The graph seems fine. Percentages are low because of the way packet losses are reported back from the media server. Probably some FEC / retransmissions are involved as well (this would be the case with many of the media servers out there).

Looking at the video bitrate, we see an interesting picture:

There’s a hiccup in the outgoing bitrate (the red line), but that for some reason takes place close to the end of the 60 seconds packet loss window.

There’s also a reduction in incoming bitrate for one of the video stream. It starts around 20 seconds into the packet loss zone, but it doesn’t recover even when we remove the packet losses.

Video delay is also a bit problematic:

It starts off nicely, goes up when packet losses start and never recuperates.

SwitchRTC

Moving on from open source to commercial, there’s SwitchRTC.

It started by me asking for a 2mbps bitrate limit. Now, the way this was set up and without simulcast, it meant the browser is going to need to encode 2mbps and decode 4 streams of 2mbps each. This turned out to be a bit too much for the way we configure our machines (and frankly – probably too much for almost any use case you plan on deploying when it comes to assuming what your typical customer may have).

The end result of it was graphs that went all over the place – each stream and each browser tried hard to compete on resources that were limited, and it wasn’t really nice.

So we dialed back down to 1mbps bitrate limit.

As always, let’s first look at the packet loss graph:

Two things here to note:

  1. One of the incoming video streams has packet losses outside the packet loss zone. Not unheard of, but a bit off the charges compared to others. I think that is due to the data centers used by SwitchRTC for this demo
  2. There’s negative packet losses on the outgoing video stream. This is due to the way SwitchRTC handles packet loss reporting (or more likely filtering packet loss reporting)

For bitrate, I took two screenshots. One for the incoming video streams and one for the outgoing video stream.

On the incoming stream we see an interesting phenomena.

When packet loss starts, bitrate picks up, most likely to overcome the packet loss. It makes sense, since we didn’t limit bitrates, so that seems like the correct strategy. Would be interesting to see what will happen if we limit bitrate as well.

The second thing, is that we have one of the incoming stream dropping down to almost zero and then picking up again. This is the same stream that shows high packet losses. I wonder what causes that.

The graph above shows the outgoing video stream. This is almost textbook behavior for the outgoing video. Once it notices there’s issues, it starts increasing bitrate to compensate, and when that fails – it drops down slowly. It is similar, though not as smooth as what you see with AppRTC.

appear.in

appear.in have a beta SFU, which Philipp Hancke was kind enough to let me use.

Now, appear.in isn’t a media server or a component you can use in your own service – it is a full service, which makes this comparison a bit unfair – checking demos and comparing them to a commercial service.

But then I wanted to check this one out, as it isn’t based on any external framework – it was self developed in house at appear.in

The results are interesting.

Packet loss graph looks rather nice, if a tad low in the percentage:

This shows how far appear.in goes in gauging and polishing the way they make use of network resources.

Video bitrate stays at the 600kbps vicinity – not showing any real effects from my additional packet loss:

Best part though is that the video delay graph doesn’t look erratic:

I am not sure how to compare these results to the rest. I will need more time to check this out – time that I just didn’t have available for this experiment of mine. I will leave it for some future tinkering.

Summing things up

Different media servers will act differently. Especially when putting them under different network conditions.

What I wanted to show here, is how you can use testRTC to goof around with whatever setting you want. Here are a few other ideas:

  1. Drop the network down to 0 bitrate. Wait a bit. Put it back up. Did media return? How quickly did it come up again?
  2. Limit bitrates to different levels. Check if your media server adapts things like resolutions and other interesting parameters to fit the needs
  3. Go down to 50 or 100 kbps. Does video persist or is the media server shutting it down in favor of audio?
  4. Limit bitrate and add a bit of packet loss at the same time (this would be closest to real life). See what happens then – how will the media server behave?
  5. Do the above while adding some load on the server. Does it start fidgeting or is it handling this nicely?

A few things to remember here:

This isn’t an apples to apples comparison

I haven’t taken each and every media server and installed it on my own on the same server configuration. I just used the online demos each of these vendors had. At times, asking for assistance and a bit of configuration from the vendor.

What was different:

  • The server(s) the media server was installed on
  • The configuration of the server, especially what max bitrate it allows

What was similar:

  • I tried disabling simulcast in all servers. Assume that’s a bad thing to do, but I wanted a level playing field on that front
  • The browser used. It was the same for all tests. This includes their version, the machine they were installed on, the network they used, their geographical location – everything
  • The scenario itself. I essentially executed the same scenario over and over again in front of different media servers

Where do we go from here?

Media servers are hard to develop. They are hard to tweak and optimize. And they are hard when it comes to making sizing decisions with them.

They are also pretty good. Most of the ones shown here are running in production services with live customers.

When you go tomorrow to pick the media server for your own project. Or when you want to plan how to size capacities per machine. Or if you want to check your media server in real life scenarios – we’ve got your back.

Check us out. I am sure we can be of help to you.

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