Large scale characterisation of YouTube requests in a cellular network

Abdesslem, Fehmi Ben; Lindgren, Anders

doi:10.1109/wowmom.2014.6918954

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Shafiq et al [144] provide further confirmation, as they remark that mobile traffic is dominated by a small fraction of users, 5% of which being responsible for 90% of the total demand. Even when focusing on specific types of traffic, mobile customers can be quite heterogeneous in their access: e.g., Ben Abdesslem et al [170] show that 20% of the users are responsible for 78% of the total number of YouTube requests from mobile devices.…”

Section: Individual Access Network Trafficmentioning

confidence: 99%

“…The authors point out that usage distributions among different video players is limited, as 80% of the relevant traffic load is generated by the five top players only. Ben Abdesslem et al [170] focus on one specific video streaming service, i.e., YouTube, and unveil interesting features of the mobile traffic it generates. Namely, the authors find a significant tendency to replay, with 37% of the users requesting at least 10 different streams over a month who replayed more than 20% of their videos.…”

Section: Marketing Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Large-Scale Mobile Traffic Analysis: A Survey

Naboulsi

Fiore

Ribot

et al. 2016

IEEE Commun. Surv. Tutorials

215

161

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This article surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart from a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-ofthe-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions.

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Section: Individual Access Network Trafficmentioning

confidence: 99%

Section: Marketing Solutionsmentioning

confidence: 99%

Large-Scale Mobile Traffic Analysis: A Survey

Naboulsi

Fiore

Ribot

et al. 2016

IEEE Commun. Surv. Tutorials

215

161

View full text Add to dashboard Cite

show abstract

“…where θ TR ðtÞ represents the power consumption due to transmission processes, θ CACHE ðtÞ is the power consumption contributed by the caching process with its intracommunication, and λðtÞ is the response time function for viral content [22].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

Dlamini

Vilakati

2021

Wireless Communications and Mobile Computing

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As mobile networks (MNs) are advancing towards meeting mobile user requirements, the rural-urban divide still remains a major challenge. While areas within the urban space (metropolitan mobile space) are being developed, i.e., small Base Stations (BSs) empowered with computing capabilities are deployed to improve the delivery of user requirements, rural areas are left behind. Due to challenges of low population density, low income, difficult terrain, nonexistent infrastructure, and lack of power grid, remote areas have low digital penetration. This situation makes remote areas less attractive towards investments and to operate connectivity networks, thus failing to achieve universal access to the Internet. In addressing this issue, this paper proposes a new BS deployment and resource management method for remote and rural areas. Here, two MN operators share their resources towards the procurement and deployment of green energy-powered BSs equipped with computing capabilities. Then, the network infrastructure is shared between the mobile operators, with the main goal of enabling energy-efficient infrastructure sharing, i.e., BS and its colocated computing platform. Using this resource management strategy in rural communication sites guarantees a quality of service (QoS) comparable to that of urban communication sites. The performance evaluation conducted through simulations validates our analysis as the prediction variations observed show greater accuracy between the harvested energy and the traffic load. Also, the energy savings decrease as the number of mobile users (50 users in our case) connected to the remote site increases. Lastly, the proposed algorithm achieves 51% energy savings when compared with the 43% obtained by our benchmark algorithm. The proposed method demonstrates superior performance over the benchmark algorithm as it uses foresighted optimization where the harvested energy and the expected load are predicted over a given short-term horizon.

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“…For example, when a video becomes viral, users watching it share and talk about that video, which will be then requested by other users after a response time. Taking into account the Internet users' response time λðtÞ, this epidemic behavior can be modelled by the self-excited Hawkes condition Poisson process described in [42]:…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

Dlamini

Vilakati

2020

Wireless Communications and Mobile Computing

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The massive deployment of small cell Base Stations (SBSs) empowered with computing capabilities presents one of the most ingenious solutions adopted for 5G cellular networks towards meeting the foreseen data explosion and the ultralow latency demanded by mobile applications. This empowerment of SBSs with Multi-access Edge Computing (MEC) has emerged as a tentative solution to overcome the latency demands and bandwidth consumption required by mobile applications at the network edge. The MEC paradigm offers a limited amount of resources to support computation, thus mandating the use of intelligence mechanisms for resource allocation. The use of green energy for powering the network apparatuses (e.g., Base Stations (BSs), MEC servers) has attracted attention towards minimizing the carbon footprint and network operational costs. However, due to their high intermittency and unpredictability, the adoption of learning methods is a requisite. Towards intelligent edge system management, this paper proposes a Green-based Edge Network Management (GENM) algorithm, which is an online edge system management algorithm for enabling green-based load balancing in BSs and energy savings within the MEC server. The main goal is to minimize the overall energy consumption and guarantee the Quality of Service (QoS) within the network. To achieve this, the GENM algorithm performs dynamic management of BSs, autoscaling and reconfiguration of the computing resources, and on/off switching of the fast tunable laser drivers coupled with location-aware traffic scheduling in the MEC server. The obtained simulation results validate our analysis and demonstrate the superior performance of GENM compared to a benchmark algorithm.

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Large scale characterisation of YouTube requests in a cellular network

Cited by 11 publications

References 14 publications

Large-Scale Mobile Traffic Analysis: A Survey

Large-Scale Mobile Traffic Analysis: A Survey

Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

LSTM-Based Traffic Load Balancing and Resource Allocation for an Edge System

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