Not All Apps Are Created Equal

Márquez, Cristina; Gramaglia, Marco; Fiore, Marco; Banchs, Albert; Ziemlicki, Cezary; Smoreda, Zbigniew

doi:10.1145/3143361.3143369

Cited by 43 publications

(9 citation statements)

References 22 publications

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“…This is consistent for all MNOs we measure. Furthermore, when checking the 3G data paths, we find that the set of IP addresses we see in 3G is a subset of the set of IP addresses we see in 4G, suggesting that the two functions are co-located in the same PGW [20]. We also check the time when the first IP address was used.…”

Section: Roaming Configurationmentioning

confidence: 87%

Experience

Mandalari

Lutu

Custura

et al. 2018

Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

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Roam like Home" is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people are able to use data services more freely across Europe. However, the implications roaming solutions have on performance have not been carefully examined. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 3G and 4G networks, including mobile network configuration, performance characteristics, and content discrimination. We find that operators adopt common approaches to implementing roaming, resulting in additional latency penalties of ∼60 ms or more, depending on geographical distance. Considering content accessibility, roaming poses additional constraints that leads to only minimal deviations when accessing content in the original country. However, geographical restrictions in the visited country make the picture more complicated and less intuitive.

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Section: Roaming Configurationmentioning

confidence: 87%

Experience

Mandalari

Lutu

Custura

et al. 2018

Proceedings of the 24th Annual International Conference on Mobile Computing and Networking

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“…a) Dataset trafficApp: This dataset contains realworld mobile data traffic demand generated by twelve popular service providers in a large metropolitan area during several consecutive months [68]. The data was collected and aggregated by the network operator using passive measurement probes, resulting in traffic levels (in bytes) every five minutes, for more than 22,000 samples for each of the services.…”

Section: B Detailed Implementationmentioning

confidence: 99%

Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network Management

Collet,

Bazco-Nogueras,

Banchs

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Zero-touch network management is one of the most ambitious yet strongly required paradigms for beyond 5G and 6G mobile communication systems. Achieving full automation requires a closed loop that combines (i) network status data collection and processing, (ii) predictive capabilities based on such data to anticipate upcoming needs, and (iii) effective decision making that best addresses such future needs through proper network control and orchestration. Recent seminal works have proposed approaches to jointly implement the last two phases above via a single deep learning model trained on past network status to directly optimize future decisions. This is achieved by designing custom loss functions that directly embed the management task objective. Experiments with real-world measurement data have demonstrated that this strategy leads to substantial performance gains across diverse network management tasks. In this paper, we go one step beyond the loss tailoring schemes above, and introduce a loss meta-learning paradigm that (i) reduces the need for human intervention at model design stage, (ii) eases explainability and transferability of trained deep learning models for network management, and (iii) outperforms custom losses across a range of controlled experiments and practical use cases.

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“…We believe that analysing per-app usages can give valuable details about the nature of events, and thus can help nely characterise them. In the literature, attention is paid to the app usage for other purposes than special events detection [16,26,35,36]. In [26], authors provide an analysis of spatiotemporal heterogeneity in nationwide app usage -they notice a large bias between apps (even within the same category) that makes the time series clustering inconclusive, and some heterogeneity even when looking to activity peaks of individual apps.…”

Section: Per-app Mobile Trac Analysismentioning

confidence: 99%

“…In the literature, attention is paid to the app usage for other purposes than special events detection [16,26,35,36]. In [26], authors provide an analysis of spatiotemporal heterogeneity in nationwide app usage -they notice a large bias between apps (even within the same category) that makes the time series clustering inconclusive, and some heterogeneity even when looking to activity peaks of individual apps. Authors in [36] investigate the similarities and dierences across dierent apps; as a result, they identify several welldierentiated clusters for each category of apps.…”

Section: Per-app Mobile Trac Analysismentioning

confidence: 99%