Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Shafiq, M. Zubair; Ji, Lusheng; Liu, Alex X.; Pang, Jong-Shi; Wang, Jia

doi:10.1109/tmc.2014.2350981

Cited by 34 publications

(18 citation statements)

References 22 publications

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“…Two of these reports show that different types of data demand tend to be spatially correlated [45], [46]. These studies were concerned with web services and smartphone apps only; however, similar conclusions were drawn when more general mobile demand was considered, as in [34], where the authors operated on flow-level information sent to and from cellular devices. Their study reveals that various data applications are not equally popular across all cells, and that "the popularity of some applications is more skewed than others across cells".…”

Section: Related Workmentioning

confidence: 92%

“…We turn to the measurement work [34], which identifies four traffic patterns, i.e., sets of content types, that users in each location were found to conform to. We assume then that a particular content type can be associated with an OTT.…”

Section: Reference Scenariomentioning

confidence: 99%

“…Finally, our work is related to such real-world measurements of data demand as [34], [45], [46]. Two of these reports show that different types of data demand tend to be spatially correlated [45], [46].…”

Section: Related Workmentioning

confidence: 99%

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Sensitivity Analysis on Service-Driven Network Planning

Francesco

Kibiłda

Malandrino

et al. 2017

IEEE/ACM Trans. Networking

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Abstract-Service providers are expected to play an increasingly central role in the mobile market and their relationship with the traditional mobile network operators (MNOs) is starting to change. The dilemma faced by over-the-top service-providers (OTT) is now whether to enter into a service level agreement with the MNOs (in the same spirit of mobile virtual network operator agreements) or to invest in deploying their own network infrastructure to serve their demand. The purpose of this paper is to study the factors shaping the agreements between OTTs and MNOs and how these factors impact network planning decisions. To this end, we build a synthetic model of cellular network deployment that explores how traditional mobile operators and OTTs compete in deploying new infrastructure. Using our model in conjunction with real-world data, we find that service-driven networks are heavily influenced by regulatory decisions, and that cost structures and demand characteristics play non-marginal roles in the definition of service-driven networks.

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Section: Related Workmentioning

confidence: 92%

Section: Reference Scenariomentioning

confidence: 99%

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Sensitivity Analysis on Service-Driven Network Planning

Francesco

Kibiłda

Malandrino

et al. 2017

IEEE/ACM Trans. Networking

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“…Finally, several papers have appeared on the analysis of cellular traffic data traces, tackling different aspects. As an example, mobile traffic patterns of cellular towers are modeled through an empirical study in [14], while the geospatial and temporal dynamics of mobile traffic are studied in [15], [16]. User mobility and temporal activity patterns, as well as the usage of radio resources by different applications, are studied in [17], [18] for 3G networks.…”

Section: Related Workmentioning

confidence: 99%

Understanding the present and future of cellular networks through crowdsourced traces

Malandrino

Chiasserini

Kirkpatrick

2017

2017 IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Abstract-We focus on today's LTE systems and use realworld, crowdsourced traces to understand (i) how present-day LTE networks are deployed and to which extent they are suited to the current traffic load; (ii) how well they will withstand the traffic demand forecasted within 2020; (iii) which techniques to improve them should be pursued and how aggressively. To this end, we use two datasets, coming from WeFi and OpenSignal and available under commercial terms. We find that today's networks are composed of tangled, medium to large-sized cells, characterized by fairly high interference. Also, current networks are typically overprovisioned, but the future traffic load will pose a significant strain on them. To accommodate the forecasted mobile traffic, our study highlights the efficacy of: (i) traffic offloading for pedestrian and stationary users, (ii) increasing the available bandwidth through, e.g., spectrum refarming, (iii) mitigating interference and improving link quality for edge users through coordinated downlink transmissions. By putting in place these actions, only a negligible amount of additional cellular infrastructure will be required. Our results come from the combination of real-world traces, experimental measurements, and ITU-recommended propagation models. Each step we take is backed by real-world facts and data.

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“…Interestingly, their results indicate that aggregate network usage exhibits periodic behavior, but individual sectors do not exhibit such properties (however, in [29], Jiang et al do find such correlations in KPIs). The usage of individual applications in cellular data networks has been studied by Shafiq et al [30]. Finally, simulation has been also used to quantify hot spots in wireless cellular networks [31].…”

Section: Related Workmentioning

confidence: 99%

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

Serrà¹,

Leontiadis²,

Karatzoglou³

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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Abstract-To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network and its overall trend, forecasting the performance of each sector over time is a challenging task, as it is affected by both regular and non-regular events, triggered by human behavior and hardware failures. In this paper, we study the spatio-temporal patterns of the hot spot score and uncover its regularities. Based on our observations, we then explore the possibility to use recent measurements' history to predict future hot spots. To this end, we consider tree-based machine learning models, and study their performance as a function of time, amount of past data, and prediction horizon. Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots. The latter brings strong evidence that, for moderate horizons, forecasts can be made even for sectors exhibiting isolated, non-regular behavior. Overall, our work provides insight into the dynamics of cellular sectors and their predictability. It also paves the way for more proactive network operations with greater forecasting horizons.

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Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks

Cited by 34 publications

References 22 publications

Sensitivity Analysis on Service-Driven Network Planning

Sensitivity Analysis on Service-Driven Network Planning

Understanding the present and future of cellular networks through crowdsourced traces

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

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