TY - GEN

T1 - Behind the NAT??? A measurement based evaluation of cellular service quality

AU - Kaup, F.

AU - Michelinakis, F.

AU - Bui, N.

AU - Widmer, J.

AU - Wac, Katarzyna

AU - Hausheer, D.

N1 - Conference code: 11

PY - 2015

Y1 - 2015

N2 - 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'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.

AB - 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'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.

KW - 4G mobile communication

KW - Markov processes

KW - cellular radio

KW - learning (artificial intelligence)

KW - quality of experience

KW - quality of service

KW - telecommunication network management

KW - 4G cellular network

KW - Germany

KW - Markov Chain

KW - NAT

KW - QoS

KW - VoIP

KW - cellular service quality

KW - live gaming

KW - machine learning

KW - measurement based evaluation

KW - mobile devices

KW - network operator

KW - point of presence

KW - video streaming

KW - Delays

KW - Mobile communication

KW - Mobile computing

KW - Performance evaluation

KW - Quality of service

KW - Servers

KW - Throughput

U2 - 10.1109/CNSM.2015.7367363

DO - 10.1109/CNSM.2015.7367363

M3 - Article in proceedings

SN - 978-3-9018-8278-4

SP - 228

EP - 236

BT - Network and Service Management (CNSM), 2015 11th International Conference on

PB - IEEE

Y2 - 9 November 2015 through 13 November 2015

ER -