Conference paper Open Access

Behind the NAT – A Measurement Based Evaluation of Cellular Service Quality

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

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  <identifier identifierType="DOI">10.5281/zenodo.51782</identifier>
      <creatorName>Kaup, Fabian</creatorName>
      <affiliation>Peer-to-Peer Systems Engineering Lab, Technische Universit ̈at Darmstadt</affiliation>
      <creatorName>Michelinakis, Foivos</creatorName>
      <affiliation>MDEA Networks Institute, Universidad Carlos III de Madrid</affiliation>
      <creatorName>Bui, Nicola</creatorName>
      <affiliation>MDEA Networks Institute, Universidad Carlos III de Madrid</affiliation>
      <creatorName>Widmer, Joerg</creatorName>
      <affiliation>MDEA Networks Institute</affiliation>
      <creatorName>Wac, Katarzyna</creatorName>
      <affiliation>Universite de Geneve,  University of Copenhagen</affiliation>
      <creatorName>Hausheer, David</creatorName>
      <affiliation>Peer-to-Peer Systems Engineering Lab, Technische Universit ̈at Darmstadt</affiliation>
    <title>Behind the NAT – A Measurement Based Evaluation of Cellular Service Quality</title>
    <date dateType="Issued">2015-11-13</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <rights rightsURI="">Academic Free License v3.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Abstract&amp;mdash;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 focusing on 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 over four weeks in 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 RTT 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.&lt;/p&gt;</description>
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