Journal article Open Access

# Assessing the Implications of Cellular Network Performance on Mobile Content Access

Kaup, Fabian; Michelinakis,, Foivos; Bui, Nicola; Widmer, Joerg; Wac, Katarzyna; Hausheer, David

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<identifier identifierType="URL">https://zenodo.org/record/51783</identifier>
<creators>
<creator>
<creatorName>Kaup, Fabian</creatorName>
<givenName>Fabian</givenName>
<familyName>Kaup</familyName>
<affiliation>Peer-to-Peer Systems Engineering Lab, Technische Universit ̈ at Darmstadt</affiliation>
</creator>
<creator>
<creatorName>Michelinakis,, Foivos</creatorName>
<affiliation>IMDEA Networks Institute, Universidad Carlos III de Madrid</affiliation>
</creator>
<creator>
<creatorName>Bui, Nicola</creatorName>
<givenName>Nicola</givenName>
<familyName>Bui</familyName>
<affiliation>IMDEA Networks Institute, Universidad Carlos III de Madrid</affiliation>
</creator>
<creator>
<creatorName>Widmer, Joerg</creatorName>
<givenName>Joerg</givenName>
<familyName>Widmer</familyName>
<affiliation>IMDEA Networks Institute</affiliation>
</creator>
<creator>
<creatorName>Wac, Katarzyna</creatorName>
<givenName>Katarzyna</givenName>
<familyName>Wac</familyName>
<affiliation>Universite de Geneve, University of Copenhagen</affiliation>
</creator>
<creator>
<creatorName>Hausheer, David</creatorName>
<givenName>David</givenName>
<familyName>Hausheer</familyName>
<affiliation>Peer-to-Peer Systems Engineering Lab, Technische Universit ̈ at Darmstadt</affiliation>
</creator>
</creators>
<titles>
<title>Assessing the Implications of Cellular Network Performance on Mobile Content Access</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2016</publicationYear>
<subjects>
<subject>Cellular networks</subject>
<subject>Performance analysis</subject>
<subject>4G mobile communication</subject>
<subject>Network measurement</subject>
</subjects>
<dates>
<date dateType="Issued">2016-03-02</date>
</dates>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/51783</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TNSM.2016.2544402</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_monroe</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="https://opensource.org/licenses/afl-3.0">Academic Free License v3.0</rights>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted on a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a Point of Presence (PoP) within the operator&amp;rsquo;s network can influence the end-to-end performance by a large extent. Given the collected data, a model predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements. Measurements of the response and page load times of popular websites lead to similar results, namely a median increase of 40% between the worst and the best performing PoP.&lt;/p&gt;</description>
</descriptions>
</resource>

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