Characterizing the Relationship Between Application QoE and Network QoS for Real-Time Services

Carofiglio, Giovanna; Grassi, Giulio; Loparco, Enrico; Muscariello, Luca; Papalini, Michele; Samain, Jacques

doi:10.1145/3472727.3472800

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…Indeed, QoE degradation may cause critical quality assurance problems and may lead to the most sensitive network impairment. A good interplay is usually needed for real-time services between network and application dynamics so as to deliver an acceptable user experience [36]. The traditional monitoring and optimization of QoS parameters in the network lacks of knowledge of user's QoE and, as a result, of efficiency in improving user experience.…”

Section: Qoe/qos Aware Sfc Orchestrationmentioning

confidence: 99%

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

Escheikh,

Yakoub

2023

Preprint

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The proliferation of the ever-increasing number of highly heterogeneous smart devices and the emerging of a wide range of diverse applications in 5G mobile network ecosystems impose to tackle new set of raising challenges related to agile and automated service orchestration and management. Fully leveraging key enablers technologies such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) capabilities in such environment is of paramount importance to address Service Function Chaining (SFC) orchestration issues according to user requirements and network constraints. To meet these challenges, we propose in this paper a Deep Reinforcement Learning (DRL) approach to investigate online Quality of Experience (QoE)/Quality of Service (QoS) aware SFC orchestration problem. The objective of this work is to fulfill intelligent, elastic and automated Virtual Network Functions (VNF)s/Container Network Function (CNF)s deployment optimizing end-to-end user experience while respecting QoS constraints. We implement the DRL approach through using a variant of Deep-Q-Network (DQN) algorithm referred to as Double DQN. We show how DRL agent behaves along the learning process for different PSN scales. We highlight also the impact of a set of hyper-parameters such as batch size and learning rate on solving the sequential decision problem related to SFC orchestration. The evaluation of the learning process is achieved based on the quality of learning with respect to the number of runs. In this regard, we use QoE metric to define a score quantifying the quality of learning.

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Section: Qoe/qos Aware Sfc Orchestrationmentioning

confidence: 99%

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

Escheikh,

Yakoub

2023

Preprint

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“…In both experiments, we quantify the impact on user QoE by using packet loss as a metric, since it has been shown to be closely related to QoE [14]. The results are summarized in Table I.…”

Section: Introductionmentioning

confidence: 99%

“…Besides classifying streams to the content they carry (close to the source), network equipments report QoE-related metrics (close to the destination). The latter can be done in different ways, using well-known industrial standards [17], [18] or with other ML models [14]. A controller (or orchestrator) implements RTCaware traffic management and can, like in our example, select new paths for RTC streams if it detects degradation in the measured QoE.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Classification of Real-Time Communications

Perna

Markudova

Trevisan

et al. 2022

IEEE Trans. Netw. Serv. Manage.

Self Cite

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Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry.In this paper, we propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet. We achieve an overall accuracy of 96% for Webex and 95% for Jitsi, using a lightweight decision tree model that makes decisions based solely on 1 second of real-time traffic. Our results show that models trained for a particular meeting software have difficulty when used with another one, although domain adaptation techniques facilitate the transfer of pretrained models.

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“…We recruited additional research assistants to help with group calls. For group calls, we kept the group size consistent at 6 participants for each of the calls as per previous research [8].…”

Section: Table 1: Call Configurations Tested During the Measurement E...mentioning

confidence: 99%

Investigating the Usability and Quality of Experience of Mobile Video-Conferencing Apps Among Bandwidth-Constrained Users in South Africa

Oosthuizen¹,

Sharma²,

Chavula³

et al.

EPiC Series in Computing

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During the COVID-19 pandemic and mandated global lockdowns, people and busi- nesses started the extensive use of video-conferencing applications for staying connected. This surge in demand and the usability of video-conferencing services has been severely overlooked in developing countries like South Africa, where one-third of adults rely on mo- bile devices to access the internet, and the per-gigabyte data cost is among the highest in Africa. Considering these numbers, we conduct a two-pronged study where 1) we measure data consumption of different Android apps through data measurement experiments and 2) we conduct interviews and usability assessments with bandwidth-constrained users to bet- ter understand the usability and Quality of Experience (QoE) of mobile video-conferencing apps. Usability is the degree to which specified users can use a product to achieve specified goals. In contrast, QoE measures the subjective perception of the quality of an application and the level of delight or annoyance with a service. The key benefit of this study will be to inform organisations that seek to be inclusive about these tools’ relative usability by letting them know about the factors influencing users’ QoE.

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Characterizing the Relationship Between Application QoE and Network QoS for Real-Time Services

Cited by 13 publications

References 11 publications

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

Online QoS/QoE-driven SFC Orchestration Leveraging a DRL Approach in SDN/NFV Enabled Networks

Real-Time Classification of Real-Time Communications

Investigating the Usability and Quality of Experience of Mobile Video-Conferencing Apps Among Bandwidth-Constrained Users in South Africa

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