360 research outputs found
Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices
Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth
Generation (5G) mobile networks. MEC facilitates distributed cloud computing
capabilities and information technology service environment for applications
and services at the edges of mobile networks. This architectural modification
serves to reduce congestion, latency, and improve the performance of such edge
colocated applications and devices. In this paper, we demonstrate how reactive
service migration can be orchestrated for low-power MEC-enabled Internet of
Things (IoT) devices. Here, we use open-source Kubernetes as container
orchestration system. Our demo is based on traditional client-server system
from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As
the use case scenario, we post-process live video received over web real-time
communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1
handovers, demonstrating MEC-based software defined network (SDN). Now, edge
applications may reactively follow the UE within the radio access network
(RAN), expediting low-latency. The collected data is used to analyze the
benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end
(E2E) latency and power requirements of the UE are improved. We further discuss
the challenges of implementing such schemes and future research directions
therein
Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe
With the rapidly growing market for smartphones and user’s confidence for immediate
access to high-quality multimedia content, the delivery of video over wireless networks has
become a big challenge. It makes it challenging to accommodate end-users with flawless
quality of service. The growth of the smartphone market goes hand in hand with the
development of the Internet, in which current transport protocols are being re-evaluated to
deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter
is a unique and evolving standard explicitly developed to meet this demand and enable
a high-quality experience for mobile users of real-time communication services. QUIC
has been designed to reduce Web latency, integrate security features, and allow a highquality
experience for mobile users. Thus, the need to evaluate the performance of these
rising protocols in a non-systematic environment is essential to understand the behavior
of the network and provide the end user with a better multimedia delivery service. Since
most of the work in the research community is conducted in a controlled environment, we
leverage the MONROE platform to investigate the performance of QUIC and WebRTC
in real cellular networks using static and mobile nodes. During this Thesis, we conduct
measurements ofWebRTC and QUIC while making their data-sets public to the interested
experimenter. Building such data-sets is very welcomed with the research community,
opening doors to applying data science to network data-sets. The development part of the
experiments involves building Docker containers that act as QUIC and WebRTC clients.
These containers are publicly available to be used candidly or within the MONROE
platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part
II of the Thesis.
We exploit data collection from MONROE to apply data science over network
data-sets, which will help identify networking problems shifting the Thesis focus from
performance evaluation to a data science problem.
Indeed, the second part of the Thesis focuses on interpretable data science. Identifying
network problems leveraging Machine Learning (ML) has gained much visibility in the
past few years, resulting in dramatically improved cellular network services. However,
critical tasks like troubleshooting cellular networks are still performed manually by experts
who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable
ML algorithms, moving away from the current trend of high-accuracy ML algorithms
(e.g., deep learning) that do not allow interpretation (and hence understanding) of their
outcome. We prefer having lower accuracy since we consider it interesting (anomalous)
the scenarios misclassified by the ML algorithms, and we do not want to miss them by
overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in
Networks), a practical and interpretable ML methodology, which we implement in the
form of a software tool named TTrees (from Troubleshooting Trees) and compare it to
a supervised counterpart, named STress (from Supervised Trees). Both methodologies
require small volumes of data and are quick at training. Our experiments using real
data from operational commercial mobile networks e.g., sampled with MONROE probes,
show that STrees and CIAN can automatically identify and accurately classify network
anomalies—e.g., cases for which a low network performance is not justified by operational
conditions—training with just a few hundreds of data samples, hence enabling precise
troubleshooting actions. Most importantly, our experiments show that a fully automated
unsupervised approach is viable and efficient. In Part III of the Thesis which includes
Chapter 6 and 7.
In conclusion, in this Thesis, we go through a data-driven networking roller coaster,
from performance evaluating upcoming network protocols in real mobile networks to
building methodologies that help identify and classify the root cause of networking
problems, emphasizing the fact that these methodologies are easy to implement and can
be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren
Performance Evaluation of WebRTC-Based Video Conferencing: A Comprehensive Analysis
In an ever-evolving technological landscape, addressing the performance challenges of real-time communication protocols is crucial. Real-time communication, facilitated by streaming media protocols, utilizes peer-to-peer or client-server models to enhance Quality of Service (QoS). WebRTC (Web Real-Time Communication) stands as a widely adopted, browser-based, open-source, peer-to-peer protocol, offering real-time media transmission through JavaScript APIs without third-party plugins. This paper presents an in-depth performance evaluation of a WebRTC-based video conferencing system using Socket.io services on a Node.js server. Our research expands on recent studies by introducing a comprehensive set of performance parameters, including Processing delay, CPU Utilization, Latency, Jitter, and Packet Loss, and packet delay. Our findings indicate that WebRTC performs exceptionally well within specific latency thresholds. However, scalability concerns emerge when a large number of clients are introduced, especially in bandwidth-constrained environments
Can Video Conferencing Be as Easy as Telephoning?-A Home Healthcare Case Study
Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).In comparison with almost universal adoption of telephony and mobile technologies in modern day healthcare, video conferencing has yet to become a ubiquitous clinical tool. Currently telehealth services are faced with a bewildering range of video conferencing software and hardware choices. This paper provides a case study in the selection of video conferencing services by the Flinders University Telehealth in the Home trial (FTH Trial) to support healthcare in the home. Using pragmatic methods, video conferencing solutions available on the market were assessed for usability, reliability, cost, compatibility, interoperability, performance and privacy considerations. The process of elimination through which the eventual solution was chosen, the selection criteria used for each requirement and the corresponding results are described. The resulting product set, although functional, had restricted ability to directly connect with systems used by healthcare providers elsewhere in the system. This outcome illustrates the impact on one small telehealth provider of the broader struggles between competing video conferencing vendors. At stake is the ability to communicate between healthcare organizations and provide public access to healthcare. Comparison of the current state of the video conferencing market place with the evolution of the telephony system reveals that video conferencing still has a long way to go before it can be considered as easy to use as the telephone. Health organizations that are concerned to improve access and quality of care should seek to influence greater standardization and interoperability though cooperation with one another, the private sector, international organizations and by encouraging governments to play a more active role in this sphere
Performance Analysis of WebRTC-based Video Streaming over Power Constrained Platforms
This work analyses the use of the WebRTC framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of size, weight and power requirements, such as the use case of unmanned aerial vehicles delivering real-time video streams overWebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality
Machine learning for Quality of Experience in real-time applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
U-DiVE: Design and evaluation of a distributed photorealistic virtual reality environment
This dissertation presents a framework that allows low-cost devices to visualize and
interact with photorealistic scenes. To accomplish this task, the framework makes use of
Unity’s high-definition rendering pipeline, which has a proprietary Ray Tracing algorithm,
and Unity’s streaming package, which allows an application to be streamed within its
editor. The framework allows the composition of a realistic scene using a Ray Tracing
algorithm, and a virtual reality camera with barrel shaders, to correct the lens distortion
needed for the use on an inexpensive cardboard. It also includes a method to collect
the mobile device’s spatial orientation through a web browser to control the user’s view,
delivered via WebRTC. The proposed framework can produce low-latency, realistic and
immersive environments to be accessed through low-cost HMDs and mobile devices. To
evaluate the structure, this work includes the verification of the frame rate achieved by the
server and mobile device, which should be higher than 30 FPS for a smooth experience. In
addition, it discusses whether the overall quality of experience is acceptable by evaluating
the delay of image delivery from the server up to the mobile device, in face of user’s
movement. Our tests showed that the framework reaches a mean latency around 177 (ms)
with household Wi-Fi equipment and a maximum latency variation of 77.9 (ms), among
the 8 scenes tested.Esta dissertação apresenta um framework que permite que dispositivos de baixo
custo visualizem e interajam com cenas fotorrealÃsticas. Para realizar essa tarefa, o
framework faz uso do pipeline de renderização de alta definição do Unity, que tem um
algoritmo de rastreamento de raio proprietário, e o pacote de streaming do Unity, que
permite o streaming de um aplicativo em seu editor. O framework permite a composição
de uma cena realista usando um algoritmo de Ray Tracing, e uma câmera de realidade
virtual com shaders de barril, para corrigir a distorção da lente necessária para usar um
cardboard de baixo custo. Inclui também um método para coletar a orientação espacial
do dispositivo móvel por meio de um navegador Web para controlar a visão do usuário,
entregue via WebRTC. O framework proposto pode produzir ambientes de baixa latência,
realistas e imersivos para serem acessados por meio de HMDs e dispositivos móveis de
baixo custo. Para avaliar a estrutura, este trabalho considera a verificação da taxa de
quadros alcançada pelo servidor e pelo dispositivo móvel, que deve ser superior a 30 FPS
para uma experiência fluida. Além disso, discute se a qualidade geral da experiência é
aceitável, ao avaliar o atraso da entrega das imagens desde o servidor até o dispositivo
móvel, em face da movimentação do usuário. Nossos testes mostraram que o framework
atinge uma latência média em torno dos 177 (ms) com equipamentos wi-fi de uso doméstico
e uma variação máxima das latências igual a 77.9 (ms), entre as 8 cenas testadas
Architecture and Protocol to Optimize Videoconference in Wireless Networks
[EN] In the past years, videoconferencing (VC) has become an essential means of communications. VC allows people to communicate face to face regardless of their location, and it can be used for different purposes such as business meetings, medical assistance, commercial meetings, and military operations. There are a lot of factors in real-time video transmission that can affect to the quality of service (QoS) and the quality of experience (QoE). The application that is used (Adobe Connect, Cisco Webex, and Skype), the internet connection, or the network used for the communication can affect to the QoE. Users want communication to be as good as possible in terms of QoE. In this paper, we propose an architecture for videoconferencing that provides better quality of experience than other existing applications such as Adobe Connect, Cisco Webex, and Skype. We will test how these three applications work in terms of bandwidth, packets per second, and delay using WiFi and 3G/4G connections. Finally, these applications are compared to our prototype in the same scenarios as they were tested, and also in an SDN, in order to improve the advantages of the prototype.This work has been supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Jimenez, JM.; GarcÃa-Navas, JL.; Lloret, J.; Romero MartÃnez, JO. (2020). Architecture and Protocol to Optimize Videoconference in Wireless Networks. Wireless Communications and Mobile Computing. 2020:1-22. https://doi.org/10.1155/2020/4903420S122202
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