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Performance analysis of the Janus WebRTC gateway

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This paper takes an in-depth look at the performance of the Janus WebRTC gateway. Janus is a modular, open-source gateway allowing WebRTC clients to seamlessly interact with legacy real-time communication technologies, both standard and proprietary, and with each other. This is achieved by attaching technology-specific plugins on top of a barebones core implementing all of the functions and protocols mandated by the RTCWEB/WebRTC specification suites. The paper focuses on assessing the scalability of the Janus architecture, by selecting three representative use cases, followed by a detailed analysis of a real-world scenario associated with multi-point audio conferencing.
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Jattack: a WebRTC load testing tool
A. Amirante, T. Castaldi, L. Miniero, S. P. Romano
Meetecho S.r.l., Via C. Poerio 89/a, 80121 Napoli, Italy
{alex, lorenzo, tobia}@meetecho.com
University of Napoli Federico II, Computer Science Department, Via Claudio 21, 80125 Napoli, Italy
spromano@unina.it
Abstract—We present Jattack, an automated stressing tool for
the analysis of the performance of WebRTC-enabled server-side
components. Jattack has been initially conceived with the primary
objective of performing a thorough scalability analysis of the
well-known Janus WebRTC gateway. As such, it re-uses most of
the Janus core stack components in order to reliably emulate the
behavior of a dynamically adjustable number of WebRTC clients.
The specific testing scenario can indeed be programmatically
reproduced by writing a small “controller” component, which
takes on the responsibility of properly orchestrating the scenario
itself. The general-purpose nature of the tool, together with
its flexibility deriving from the controller-based programmable
approach, makes Jattack also suitable for stress-testing other
WebRTC-enabled servers.
I. INTRODUCTION
The motivation behind this paper stems from the concrete
need for a flexible, lightweight and reliable testing tool for
the assessment of the scalability of WebRTC-enabled servers.
These components definitely represent strategic assets of a
number of Web-based real-time multimedia systems that are
starting to be used at very large scales. The authors of this
paper are the developers of the JanusTM WebRTC gateway [1],
a well-known open source component that is currently lever-
aged by a number of small and medium size enterprises having
in common the need for WebRTC-enabled interaction among
their clients, as well as interoperability with “legacy” real-time
communication frameworks based, e.g., on SIP (Session Ini-
tiation Protocol) [2]. Just to cite a very well-known example,
SlackTM is currently using Janus for the implementation of the
audio call functionality of their application [3].
Given the increasing level of deployment of Janus as a
key backend component of the aforementioned systems, the
need has arisen to properly assess its scalability properties
in a reliable way. When we started to tackle such an issue,
we had to confront ourselves with a number of challenges,
especially when trying to automatically generate a suitable
stress testing load capable to properly mimic the behavior of
real-world WebRTC clients. Indeed, more often than not we
found ourselves struggling with the issue of properly scaling
the client-side of the integrated WebRTC system under test
before being capable of arriving at a load which might be
deemed suitable in order to assess the scalability of even a
single Janus instance. The testing campaigns, most of the times
deployed in the cloud, took us a lot of effort, in terms of both
platform configuration and cost.
We hence decided to work on a suitable load generator
which might be leveraged in a programmatic fashion in order
to rapidly prototype customized testing scenarios involving a
significant number of WebRTC client instances capable to re-
produce the typical behavioral profile of the users of a specific
Janus-enabled framework. The idea was to become able to
keep the load on the client-side as low as possible, hence
moving the bottleneck to the server-side of the integrated
system and allowing us to easily assess the scalability of the
Janus servers residing in the backend.
The tool we devised has been conceived at the outset as
a general purpose one. As it will be explained in the next
sections of the paper, it is made of two main components: (i)
a core implementing the fundamental WebRTC functionality
and related stack; (ii) a programmable controller allowing to
easily build and drive a specific testing scenario. The adoption
of the principle of separation of concerns, with special regard
to the availability of the programmable orchestrating part,
indeed allows to use our stress-testing tool for the assessment
of server-side WebRTC components other than Janus.
II. RELATED WORK
The automated testing of WebRTC applications and services
definitely represents a challenging issue. Being able to design
and execute a stress test campaign can be of paramount
importance when evaluating a new service, a new WebRTC
endpoint or any kind of WebRTC component. Stress testing,
in fact, allows to assess the scalability and the performance of
the target. Lately, there have been several efforts devoted to
this area of research.
The most popular means for testing WebRTC applications
in an automated fashion is by using the well known Selenium
Framework [4], which allows for a complete simulation of a
browser’s behavior. This framework, in fact, allows developers
to remotely control and drive browser instances through soft-
ware modules called “webdrivers”. A controller application,
then, can be written by leveraging different language bindings
(e.g., Java or Python) to decide things like which page to
open, what user input to simulate, and so on. As such, it is
usually fairly easy to deploy headless instances of browsers
(e.g., Chrome, Firefox) on dedicated servers and have them
open and “navigate” properly crafted web pages that allow
for a partial, if not full, automation of a user’s interaction.
When these interactions involve the creation of WebRTC
PeerConnections, this allows for the automated creation of
multiple WebRTC resources that can be used for the purpose.
The advantage of this solution is that it allows users to re-
use most of the resources already available when designing
a test campaign: in fact, most of the times you can run
tests on the very same pages that would actually be accessed
by users themselves, taking advantage of the scripting and
programmable features of both JavaScript and the language
used in the controlling application. We ourselves have used this
approach repeatedly in our tests, and have shared our results
in a recently published paper [5]. The same approach has been
adopted in [6], where the authors evaluated the performance of
Kurento Media Server [7] in various scenarios. To the best of
our knowledge, Selenium is also the foundation of a popular
WebRTC testing service as well, TestRTC [8]. That said,
while easy and effective, this approach is nonetheless quite
demanding in terms of resources. In fact, this requires actual
browser instances to be deployed on server machines that can
be used for the testing. Since browsers are typically not very
lightweight applications, and given the fact that there is a lot
of overhead in terms of what such browser instances actually
do when opening a target web page, the number of concurrent
PeerConnections that can be established and maintained on a
single machine is limited. This makes it hard to effectively
assess the scalability of a component without an adequate
number of client machines available for testing.
For this reason, when either the availability of resources
is constrained or there is a need for more efficiency, it is
in general advisable to rely on some kind of application
that allows for a better management of resources (e.g., to
only implement the WebRTC communication), while leaving
the rest of the interactions (e.g., authentication, signaling,
automated input, and so on) to other tools. Unfortunately, just
a few tools can satisfy this requirement. In fact, most of the
available automated web tools are specifically targeted at stress
testing the web-based behavior of an application, e.g., in terms
of HTTP and/or WebSocket [9] requests.
One notable exception is Jitsi Hammer[10], a tool devised
and implemented by Jitsi as a traffic generator for their
Videobridge application. This tool is a Java application that
can be configured to connect to a Jitsi-Meet conference, create
fake users, and generate/receive RTP traffic on behalf of the
simulated users within the room [11]. This is a very good
example of how resources can be better managed for the
purpose of assessing the performance of a component. In
fact, while it is true that the same results might be achieved
through the Selenium Framework (e.g., by having multiple
browser instances all access the same web page and provide
credentials in an automated way to join a meeting room), it is
fairly obvious that a dedicated application allows for a much
more optimized management of resources. In this case, the Jitsi
Hammer tool takes care of the interaction with the backend on
its own and, through configurable parameters, can be instructed
to behave in different ways, all using less resources than a full
browser would require. While a very effective tool, though,
to the best of our knowledge Jitsi Hammer is specifically
tailored to the Jitsi Videobridge, which means it cannot be
used, for instance, as a stress testing tool to interact with
different applications, possibly based on different backends.
Finally, there are some commercial solutions for testing
WebRTC services [12], [13]; unfortunately, to the best of our
knowledge, there is no public documentation on the tools they
leverage to do performance evaluation.
These were the considerations that eventually led us to
work on such a tool ourselves. While the main aim was, at
first, an efficient tool we could use to test Janus instances
while managing resources more efficiently, we soon found out
that it could actually be generalized, and used to assess the
performance of any WebRTC application, provided that the
test campaign is designed properly.
III. A GENERAL-PU RPOSE L OAD T ESTIN G TOOL
Jattack (which stands for “Janus Attack”, or the French
“J’attaque”) is a tool we conceived to quickly generate mul-
tiple WebRTC connections, for the purpose of stress testing
WebRTC applications. We took the Janus WebRTC Gateway’s
open source core and slightly modified it for the purpose. As
such, it is mainly composed of the same WebRTC stack of
Janus, with a few differences.
Jattack is supposed to be a generic WebRTC stressing client
tool, thus it is not strictly limited to Janus itself. Given its
programmable nature, with a few customizations it can be
easily fit to interact with different server-side components.
By itself, Jattack does no signaling at all: it only works
when used with a controller that handles that part for it, and
which orchestrates its actions. This means that, in general,
users will have their own application talking to Janus (or their
Janus-based service) and handling the signaling (JSEP [14],
SDP [15], trickle ICE candidates [16]), and then bridging this
info between Janus and Jattack. This allows for the realization
of heterogeneous scenarios: for instance, the controller can or-
chestrate the creation of multiple PeerConnections of different
types to simulate real scenarios (e.g., a multi-party conference,
or a large webinar). Multiple Jattack instances can be used by
the same controller for larger scale scenarios. Furthermore,
multiple PeerConnections can share the same media source in
Jattack, thus allowing for a very lightweight setup of active
sources. It is worth noting that, with this approach, Jattack is
completely relieved of any encoding/decoding burden, which
is known to be the most resource-intensive task a WebRTC
client has to perform.
The Jattack architecture is depicted in Fig. 1.
IV. CON TRO LL ER:T HE B RAIN
Jattack uses WebSockets to connect to the controller, receive
commands, and send responses/events. This means that a
wannabe controller needs to implement the server-side of the
jattack-protocol we conceived.
While we will not delve into the details of the protocol
itself for the sake of brevity, it may be worth summarizing
its syntax, with special regard to the inner workings of the
request/response mechanism, as well as to how asynchronous
Fig. 1: Jattack architecture
notifications can be delivered within the context of an exist-
ing session. Jattack commands (requests) have the following
syntax:
{
jattack: ’<command>’, // Command name, mandatory
transaction: ’<unique ID>’, // Transaction ID, mandatory
id: <session ID>, // Media session ID, optional
body: { // Command specific payload, optional
[..]
}
}
A command, when handled, always results in a response
from Jattack to the controller. Responses are formatted like
this:
{
jattack: ’success|error’, // Command result
transaction: ’<unique ID>’, // Transaction ID, same as request
id: <session ID>, // Media session ID, optional
body: { // Command specific response, optional
[..]
}
}
As anticipated, though, not all the communication between
Jattack and a controller is synchronous. Commands sent to
Jattack can subsequently result in asynchronous events gener-
ated by the tool (e.g., to notify new candidates or ICE state
changes). Events are formatted like this:
{
jattack: ’<event>’, // Event name
id: <session ID>, // Media session ID, optional
body: { // Event specific payload, optional
[..]
}
}
We notice that events do not have a “transaction” field,
as they are, again, asynchronous, and hence never explicitly
related to any command. These events can be quite helpful
to track the “life” of a PeerConnection, and at the same time
collect statistics that can provide valuable information.
For the testing campaign we conducted, we wrote differ-
ent controller logics, all implementing, on the Jattack side,
the above mentioned protocol. Such controller logics han-
dled signaling toward different Janus plugins, e.g., the Janus
Streaming,AudioBridge,VideoRoom and EchoTest plugins.
For the sake of simplicity, we chose to focus on the Janus
Streaming and VideoRoom plugins for this paper, as they
are the most commonly used by developers for their Janus-
based applications. As to the controller implementation, we
chose node.js as the runtime environment, and talked to Janus
through its WebSocket transport module.
V. JATTACK :THE ARM
The actions to undertake in order to create and manage a
Jattack-based testing campaign are briefly listed below:
1) Jattack connects to, and registers at, the controller;
2) the controller properly handles Jattack’s registration re-
quest;
3) one or more media sources are created, if needed (more
on that later);
4) as many sessions as needed get created in a dynamic
fashion (each session will be associated with a Peer-
Connection);
5) one or more Janus instances get contacted and instructed
to create as many PeerConnections as needed with the
available Jattack instances;
6) if Jattack instances need to generate media, a media
source is attached to them;
7) optionally, Jattack instances can be instructed to record
the incoming frames (not mandatory, you can receive
without recording).
The following subsections dig further into the details of the
above mentioned procedures.
A. Registration
Upon connection, Jattack sends a register message to
the controller. This is the only time that Jattack sends an active
command. In general it will always just receive commands,
while sending back responses and/or events.
B. Commands
Jattack can receive the following commands:
add-media-source: add a new source for active
RTP sessions (e.g., an external GStreamer/FFmpeg script
sending media); such a source can be shared across
multiple sessions at the same time; without a source, a
Jattack session is receive-only;
remove-media-source: destroy an existing media
source;
create-session: create a new media session; this is
basically the same as a session+handle in Janus, meaning
that a session is associated with a single PeerConnection;
destroy-session: destroy an existing media session
(destroys PeerConnection too, if it existed);
generate-offer: have Jattack generate an offer in a
session to create a new PeerConnection;
generate-answer: have Jattack generate an answer
in a session to create a new PeerConnection;
handle-offer: pass a remote offer to Jattack in a
session to create a new PeerConnection;
handle-answer: pass a remote answer to Jattack in a
session to create a new PeerConnection;
hangup: hangup an existing PeerConnection;
get-state: get the internals of an existing session
(very similar to Janus admin API info).
Amedia source, in Jattack, is whatever can be used to
have Jattack actively generate RTP data. Jattack does not
capture/generate media by itself, meaning it does not access
your microphone, your webcam, a file or anything like that.
The only way to have a Jattack PeerConnection send media
is by creating a media source and attaching it to the session.
A media source listens on a couple of ports, and everything
it receives on them (normally valid RTP) is sent over the
session’s PeerConnection by Jattack. Sending media to such
ports can be achieved through external tools like GStreamer or
FFmpeg. As already anticipated, multiple sessions can share
the same media source in Jattack, which is completely relieved
of any encoding/decoding task.
A Jattack session is the main purpose of Jattack itself. This
is where a media session is created, that is something that will
be strictly associated with a PeerConnection in Jattack itself.
In Janus terms, it can be seen as a condensed session+handle,
i.e., a session that only contains a single handle.
When a session is created, there is no PeerConnection yet.
A PeerConnection becomes only available after a successful
WebRTC negotiation. This involves SDP offers and answers,
trickle ICE candidates, ICE connectivity checks, DTLS hand-
shakes, and so on.
In order to create a PeerConnection in Jattack, and more
specifically within a specific session that has been created,
we need to first decide who will send the offer and who
will instead provide the answer. This typically depends on the
scenario we want to achieve and, in case Janus is involved, on
the plugin that will be used for the purpose. To use either
the Janus EchoTest, or the AudioBridge or even to create
a VideoRoom publisher, for instance, the client is supposed
to send the offer and the target plugin module on the Janus
side to answer back. To create a VideoRoom subscriber or a
Streaming viewer, instead, the offer always has to come from
the Janus plugin, and the client has to answer back. For this
reason, when Jattack acts as the offerer, a generate-offer
command is always followed by a handle-answer; when
the peer is the offerer, instead, a handle-offer is followed
by a generate-answer. In both cases, the result is an
attempted setup of a PeerConnection. This means that Jattack
starts gathering and trickling ICE candidates, tries to send
connectivity checks when it has enough information, starts a
DTLS handshake when ICE is done and exchanges RTP/RTCP
packets when WebRTC is ready. During the negotiation pro-
cess, handle-offer,handle-answer, and responses to
generate-offer and generate-answer have to carry
an SDP body. As anticipated, this negotiation process starts
a chain of activities and events that eventually lead to the
setup of a WebRTC PeerConnection. All these activities will
be notified to the controller via events so that the controller can
use them as appropriate. A notable example of an important
event to handle is trickle, as it is the event used to report
ICE candidates Jattack has gathered for itself: the controller
must forward those trickle candidates to the peer, or the
WebRTC connectivity will most likely fail.
A PeerConnection stays alive until we destroy the session
or simply hangup. Destroying the session means we would
have to create a new one if we wanted to create a new
PeerConnection, while only hanging it up allows us to re-use
the existing session for a new PeerConnection. Tearing down
a PeerConnection results in events sent to the controller.
In order to check the internal state of a Jattack session,
including the state of the associated WebRTC PeerConnection,
we introduced an ad-hoc request called get-state, which
acts like the Janus admin API and returns similar info.
C. Events
Jattack can generate several different events towards the
controller at any time. These include events related to any
change in the state of the PeerConnection, the health of the
media session, etc.. No action is needed from the controller
when an event is received: it just needs to be prepared to
receive and optionally handle them.
The events Jattack can generate are the following:
source-done: a previously created source is over;
trickle: Jattack gathered a new candidate in a session
(the controller must forward it);
ice-state: the ICE state of a Jattack session PeerCon-
nection changed;
selected-pair: an ICE pair was selected in a Jattack
session PeerConnection;
webrtcup: a PeerConnection in Jattack has just become
active;
media: Jattack started/stopped receiving media from the
peer;
recording: a Jattack recording state changed
(started/completed);
hangup: a PeerConnection in Jattack was just closed;
destroyed: a Jattack session was just destroyed.
All these events can be very helpful to follow the evolution
of an existing PeerConnection, and controllers may choose to
save/store them in a structured way for offline evaluations.
VI. J’ATTAQUE! A CAMPAIGN AGAINST THE JANUS
WEB RTC GATEWAY
In this section we show the results of the testing campaign
we conducted against Janus. Our objective was to assess the
performance of both Janus and Jattack itself. The primary pa-
rameters we took into account are the CPU and memory loads
on both ends. We also leveraged the information provided by
Jattack’s get-state command in order to roughly estimate
the quality perceived by the simulated users, more specifi-
cally by analyzing the number of negative acknowledgments
(NACKs) sent/received on each PeerConnection. NACKs, in
fact, are used to inform a sender of the loss of particular RTP
packets, and thus represent a quality indicator of the media
streams flowing across a PeerConnection.
A. The testbed
The testbed we set up uses Docker containers [17] in
order to isolate the single functions described in the previous
sections. Namely, we made use of docker-compose to
build a microservices-oriented architecture. The testbed was
deployed on a machine equipped with 8 Intel Core i7-4770S
CPUs @ 3.10GHz and 16 GB of RAM. All containers
are based upon the Ubuntu 16.04 OS. We also leveraged
the Docker’s cpuset option to dedicate different CPUs to
different containers, so that the performance statistics of Janus
were not impacted by Jattack, and vice-versa. Specifically, we
assigned 4 cores to Janus, 3 cores to Jattack and 1 core to the
controller.
We note that Janus, Jattack and the controller all ran on the
same physical host, so all network communication happened
in localhost. We did this on purpose, as we did not want
network performance and congestion to affect the results of our
analysis. Our aim was twofold: (i) evaluate the performance
of the Jattack tool itself; (ii) spot any performance issue that
strictly depends on the Janus source code, which can then be
fixed/optimized.
B. Stressing the VideoRoom plugin
Fig. 2 shows the CPU evolution of both Janus and Jattack
in the presence of 10 publishers and 90 subscribers. In this
experiment we had Jattack generate clients with a frequency
of 10 per second, starting with the publishers and then al-
locating viewers. As the VideoRoom plugin implements the
Selective Forwarding Unit (SFU) logic, we had a total number
of 1000 PeerConnnections maintained by Janus and Jattack;
furthermore, as each client has to establish 10 PeerConnections
when it connects to Janus, we were approximatively generating
one PeerConnection per second. We observe the CPU levels
are very similar to each other, as we expected since Jattack
has been built upon the Janus core. Such a scenario took up
to 200% of CPU on the Janus’ side, and slightly more on
the Jattack’s side. We recall that Janus was running on four
dedicated cores, while Jattack on three. Hence, Janus could
take up to a value of 400% and Jattack up to 300%.
Fig. 3 shows the memory load, which proved to be very
low in both cases.
Fig. 4 depicts the CPU load when there was only one
publisher and 1000 viewers, i.e., 1000 PeerConnections main-
tained by Janus and Jattack. It is very similar to the one
attained in the scenario envisaging 10 publishers and 90
viewers, as the overall number of PeerConnections is the same.
During the campaign described above, we periodically
issued get-state requests to Jattack in order to retrieve
information about the health state of each PeerConnection.
Fig. 5 shows a client’s perspective with respect to negative ac-
knowledgments (NACKs) sent. We notice that no NACKs were
sent/received until the overall number of PeerConnections
approached 800. Then, NACKs started flowing, indicating a
degradation of the quality perceived by the simulated users.
One of the possible causes might be the inability for the
media router to enqueue, on that specific instance, 800 packets
Fig. 2: VideoRoom CPU with 10 publishers and 90 viewers.
Fig. 3: VideoRoom RAM with 10 publishers and 90 viewers.
Fig. 4: VideoRoom CPU with 1 publisher and 1000 viewers.
Fig. 5: Number of NACKs sent over a PeerConnection
for delivery to different recipients in the short time between
two packets. Another possible cause might be ascribed to the
significant number of CPU context switches: at the time of this
writing, in fact, Janus spawns a dedicated thread to transmit
packets to each receiver. We have already started investigating
solutions to this problem. One possible approach, which we are
working on in a development branch of Janus, is to delegate
the one-to-many routing job within the Janus plugin to helper
threads, which can then alleviate the burden a single thread
has to bear right now. An alternative approach might be to
leverage a pool of sender threads handling multiple recipients
each.
C. Stressing the Streaming plugin
When we addressed our testing campaign towards the
Streaming plugin, we obtained the very same results discussed
in the previous subsection about the VideoRoom plugin when
a single publisher was envisaged. So, we do not present any
new chart regarding these specific experiments.
VII. CONSIDERATIONS AND FUTURE WORK
We introduced a general-purpose WebRTC stressing tool
called Jattack. We explained the motivations that led us to
design and implement such a tool, and also described some
sample test campaigns, aimed at assessing the performance
of our Janus WebRTC server. In this context, we focused our
attention on both the performance of the target (Janus) and the
stressing tool (Jattack), in order to evaluate the effectiveness
of Jattack as a tool for performance assessment purposes.
We were delighted to find out that Jattack behaved exactly
as we hoped it would, and that it was actually able to
simulate the activities of multiple (both passive and active)
WebRTC sessions. The greatest result was probably in terms
of resources needed on the client side to implement the test
campaign. While implementing a Jattack controller required
some more complexity in terms of managing signaling and
out-of-band communication with the target, when compared
to implementing a Selenium controller (where, as anticipated,
you typically can re-use existing web pages and JavaScript
code), in order to set up 1000 PeerConnections we needed
more than 10 servers when using Selenium. We only needed
a single one to setup the same number of PeerConnections on
Jattack, instead, and it was not even the maximum number of
sessions it could create on the server we hosted it on. This
whole order of magnitude can prove extremely helpful when
it is time to scale up the number of tests and start simulating
hundreds of thousands of users, as in this case you can leverage
multiple Jattack instances, each of which can support a very
high number of sessions.
That said, there is room for improvement. In fact, as
anticipated, we currently only tested Jattack to test the Janus
server. Considering they share most of the same code-base in
terms of WebRTC stack, it might be actually more interesting
to start using it in different contexts as well. This includes
using it both in purely peer-to-peer scenarios (i.e., to browsers
directly) and to interact with third-party components, as a tool
to help assess the performance of those as well.
Another interesting use case for Jattack is actually not
related to stress testing, but to WebRTC clients implementation
in general. In fact, the configurable nature of Jattack and the
fact that it can relay plain RTP media via WebRTC on behalf
of a programmable controller means it can also be used as a
simple, while effective, client-side WebRTC gateway.
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Provisioning of Candidates for the Interactive Connectivity Establishment
(ICE) Protocol draft-ietf-ice-trickle-03, July 2016 (work in progress).
URL https://tools.ietf.org/html/draft-ietf-ice-trickle-03
[17] Docker web site, http://www.docker.com
... However, WebRTC might not be efficient enough when there are too many peers. If there are N peers, then N-1 peer connections are necessary to initiate and maintain each peer [11] to process the corresponding multimedia stream receiving and sending process, such as AppRTC [12]. For example, as shown in Figure 2, if we want to construct a real-time streaming live broadcast application that has multiple peers, we use the WebRTC architecture. ...
... In examining WebRTC features such as crossplatform protocols, browser compatibility, stability, low latency, and plug-in independency, many studies have been helpful. The authors of [11] assessed the problem of the pure p2p characteristics of WebRTC not meeting the actual requirements in some scenarios and developed a centralized control center conference system based on the WebRTC protocol. The results of multiple experiments conducted in [11] showed that the overall system has low latency, stability and reliability. ...
... The authors of [11] assessed the problem of the pure p2p characteristics of WebRTC not meeting the actual requirements in some scenarios and developed a centralized control center conference system based on the WebRTC protocol. The results of multiple experiments conducted in [11] showed that the overall system has low latency, stability and reliability. ...
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The WebRTC protocol can provide live streaming of peer-to-peer connections via JavaScript (JS) application programming interface (API) calls to a web browser. However, the protocol is restricted to a small number of peers because there is no simple way to mix real-time streams from multiple peers and then distribute the mixed stream to a large number of audiences. For example, it is necessary to mix audio and video streams from peers in a conversation and broadcast the real-time mixed stream to more than 10k audiences who only watch the video. It is also necessary to blend synchronous content (e.g., logos, music) to the live conversation stream. WebRTC does not currently provide a mechanism to easily support these cases. This paper proposes a method for the synchronized mixing of real-time audio/video streams from multiple peers while minimizing latency. This method enables the implementation of an online live conversation system that is able to mix live conversation streams from multiple peers and then rebroadcast the mixed stream to a large number of audiences.
... Respecto de la Tabla II, la primera prueba fue realizada con el servidor Nginx y el protocolo HLS [33] los cuales generaron una latencia que oscilaba entre 10 a 20 segundos. Debido a esta alta latencia para la interacción de los estudiantes, se probaron otras alternativas hasta la selección del Gateway Janus WebRTC [34], [35]. Janus transforma la transmisión de video codificado de la cámara de la Raspberry Pi en protocolo UDP al estándar WebRTC para los usuarios. ...
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This article describes the development and assessment of RaspyLab which is a low-cost Remote Laboratory (RL) to learn and teach programming with Raspberry Pi and Python language. The RL is composed of 16 stations or nodes that contain hardware components such as display LCD, robotic arm, temperature sensor, among others, and two modes of programming (graphical and text-based) for the students to experiment with their designed algorithms. The concept of the RL was conceived as a pedagogical tool to support the students of Engineering and Computer Science (CS) in an online learning format, given the context of the COVID-19 pandemic. The laboratory has been used by (n=30) CS students during the second semester of 2020 in the subject of mathematical logic through the methodology of Problem-Based Learning (PBL). To evaluate preliminary the laboratory, it was used a survey with 3 open-ended questions and 12 closed-ended questions on a Likert scale according to the Technology Acceptance Model (TAM). The outcomes show a good reception of the laboratory, an enhancement of the students' learning regarding the concepts addressed in the course, and an interest of the students for the laboratory to be included in other subjects of the curricula.
... Regarding Table II, the first test was performed with a Nginx Server with HLS protocol [33] which generated a latency that oscillated between 10 to 20 secs. Due to this high latency for the students' interaction, we tried other alternatives until the selection of Janus WebRTC gateway [34], [35]. Janus transforms the encoded stream of the Raspberry Pi camera in UDP protocol to WebRTC standard for the users. ...
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This article describes the development and assessment of RaspyLab which is a low-cost Remote Laboratory (RL) to learn and teach programming with Raspberry Pi and Python language. The RL is composed of 16 stations or nodes that contain hardware components such as display LCD, robotic arm, temperature sensor, among others, and two modes of programming (graphical and text-based) for the students to experiment with their designed algorithms. The concept of the RL was conceived as a pedagogical tool to support the students of Engineering and Computer Science (CS) in an online learning format, given the context of the COVID-19 pandemic. The laboratory has been used by (n=30) CS students during the second semester of 2020 in the subject of mathematical logic through the methodology of Problem-Based Learning (PBL). To evaluate preliminary the laboratory, it was used a survey with 3 open-ended questions and 12 closed-ended questions on a Likert scale according to the Technology Acceptance Model (TAM). The outcomes show a good reception of the laboratory, an enhancement of the students’ learning regarding the concepts addressed in the course, and an interest of the students for the laboratory to be included in other subjects of the curricula.
... This detection would be straightforward since CRON's vanilla covert data encoding technique (see Section 2) fully replaces the video payload with an apparently random covert data signal that results in a scrambled video image at the receiver's endpoint. Second, by controlling the data transmission, an adversary is able to decrypt SRTP packets' payload and decode the enclosed video frames, e.g., by leveraging WebRTC gateways to record, inspect, or re-encode media on-the-fly [1]. So here, by replaying the video, it is possible to obtain clear evidence of video manipulation. ...
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In many regions of the world, nation-states enforce Inter-net censorship policies that prevent unrestricted access toinformation and services by their citizens. Over the years,many censorship circumvention tools have been proposedwhich, however, require either the deployment of a dedi-cated infrastructure within trusted ISPs, or are vulnerableto state-of-the-art traffic analysis techniques. To fill this gap,we propose to build a practical censorship-circumvention ser-vice that exhibits strong resistance against traffic analysisattacks. By relying on a recent proposal for creating covertchannels through WebRTC streams, we discuss the designof a distributed system named Censorship-Resistant OverlayNetwork (CRON). CRON aims at offering to the users lo-cated in censored regions a set of services that allow themto locate proxies positioned in the free Internet region, andset up secure covert tunnels for accessing arbitrary sites onthe Internet. We present the key challenges and explore thesolutions in making CRON robust against state-level attacks.
... When working on increasing the scalability of an application, one of the main steps always is the evaluation of how a single instance performs. This is a problem we had to face for Janus as well, and that we started to address in two separate occasions in the past [3] [4]. ...
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Nowadays there is a wide range of applications for WebGIS which can add great value to modern economic, and building WebGIS system for specific scenarios is the common requirement of the industry. While currently separate WebGIS systems are deployed at different sites and operated by different owners, each of which has the whole set of functionalities of WebGIS, and thus introduce high cost of development and maintenance, which is a waste of resources as most of the functionalities are the same or similar. An edge computing based WebGIS architecture is proposed in the paper to meet the need of customization by applying the idea of SaaS. In this distributed architecture, the resource load is reasonably balanced between the server and the browser, which improves the overall performance of the system. Also it utilizes edge computing to reduce the pressure on the server by sharing map tiles among WebGIS clients. The proposed WebGIS system can not only be well customized and personalized as it is edge computing base, but also be well usable for large number of visits due to its distributed feature. The experiments show 5 concurrent requests per second, as well as response speed increases by more than 38.6% against traditional deployment.
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This paper deals with the design and implementation of Janus, a general purpose, open source WebRTC gateway. Details will be provided on the architectural choices we took for Janus, as well as on the APIs we made available to extend and make use of it. Examples of how the gateway can be used for complex WebRTC applications are presented, together with some experimental results we collected during the development process.
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Multiparty conferencing has traditionally been a relatively expensive application that was only used in enterprise scenarios. Recently, however, the landscape has started to shift in ways that could change this. Ever-increasing bandwidth and processing capabilities make it possible for mobile endpoints and laptop computers to easily handle multiple incoming media streams (both audio and video). The development of Web Real-Time Communications (WebRTC) has also significantly simplified the development of video conferencing applications and made them mainstream. Both of these changes provide a way of replacing expensive video mixers (that produce composited videos) with light-weight video routers (that selectively forward streams). In this paper, we describe a Multipoint Control Unit (MCU) that identifies and selects the last N dominant speakers and forwards their streams to all the conference participants. We evaluate the performance of this Selective Forwarding Unit (SFU) against a simplistic everyone-to-everyone (full-star) MCU. Our results show that the SFU uses 45% less CPU and 63% less bandwidth when forwarding media for 10 of the endpoints in a 30-participant conference.
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