Online Learning for Network Resource Allocation

Salem, Tareq Si

doi:10.1145/3579342.3579348

Cited by 1 publication

(1 citation statement)

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“…Traditional web caching and similarity-caching [10], [11], [38] can significantly impact network-level QoS. Indeed, research on similarity caching bodes well with the idea that the interplay between network-level QoS, application-level QoS and QoE can benefit the network and its users.…”

Section: A Qos and Qoe Parametersmentioning

confidence: 94%

When Should Recommenders Account for Low QoS?

Nogueira,

Pinheiro,

Assis

et al. 2023

IEEE Access

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Content recommendation systems, also known as recommenders, are pervasive and have significant impact on user demands over the Internet. Platforms such as YouTube and Netflix constantly seek to improve their recommendation systems, to provide better quality of experience (QoE) for their users. QoE, in turn, depends on a multitude of factors, including the quality of recommendation (QoR), e.g., based on users histories and content categories, and the quality of service (QoS), e.g., measured by network delay and throughput. Even though QoS is key in a best-effort Internet, existing recommendation systems overlook it, resulting in recommendations that are suboptimal in terms of QoE. In this study, our goal is to devise a QoS-aware, QoE-friendly, content recommendation system and indicate its feasibility in the wild. For this purpose, we conducted an experiment with real users driven by the following question: When should recommenders account for low QoS? Each user is requested to evaluate pairs of videos, that vary in their contents and QoS levels. We experimentally determined category-dependent thresholds that determine the sensitivity of users with respect to QoS and QoR. Given the collected insights on QoS-aware recommendations, we considered our second research question: Can recommenders compensate for low QoS? We conducted experiments over the Internet, relying on YouTube API and network measurements tools, and report our findings on (i) the characterization of QoS and (ii) the compensation for low QoS. Our measurements suggest that content far from the trends tends to be far from the user. We quantified the extent to which unpopular content tends to be served with a lower QoS and established a methodology to determine the relationship between content popularity and its physical proximity to users. Then, we verified that making requests a bit trendier can hit much closer content.

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Section: A Qos and Qoe Parametersmentioning

confidence: 94%