ABSTRACT: Pervasive coverage and continuous connectivity of Mobile Broadband (MBB) networks are common goals for regulators and operators. Given the increasing heterogeneity of technologies in the last mile of MBB networks, further support for seamless connectivity across multiple network types relies on understanding the prevalent network coverage profiles that capture different available technologies in an area. Correlating these coverage profiles with network performance metrics is of great importance in order to forestall disturbances for applica- tions running on top of MBB networks. In this paper, we aim to profile MBB coverage and its performance implications from the end-user’s perspective along critical transport infrastructure (i.e., railways in Norway). For this, we deploy custom measurement nodes on-board five Norwegian inter-city trains and we collect a unique geo-tagged dataset along the train routes. We then build a coverage mosaic, where we divide the routes into segments and analyze the coverage of individual operators in each segment. We propose and evaluate the use of hierarchical clustering to describe prevalent coverage profiles of MBB networks along the train routes and classify each segment accordingly. We further analyze the areas we classify with each profile and assess the packet-loss performance of the networks in those areas.
ABSTRACT: Mobile broadband (MBB) connections are often exposed to varying network conditions under mobility scenarios, which can result in packet loss and higher end-to-end delays. Such performance degradation in turn can adversely impact the user experience. In this paper, we study packet loss characteristics of MBB networks under mobility using six measurement nodes that are placed on regional and inter-city trains in Norway for a period of seven months. Our findings show that packet loss is significantly higher for mobility scenarios compared to the stationary. In order to understand the cause of packet loss, we investigate Radio Access Technology (RAT) changes, temporary loss of service, and changes in cells and location area codes (LAC). We surprisingly find that almost all periods with RAT changes involve packet loss. We also observe that 70% of the overall loss happens in periods with RAT changes or temporary loss of service. Further, one third of RAT changes involve connection termination. Our findings highlight the importance of radio access network (RAN) planning and configuration, and provide guidelines to alleviate packet loss in MBB networks.
IRTF & ISOC Workshop on Research and Applications of Internet Measurements (RAIM) in Cooperation with ACM SIGCOMM
ABSTRACT: There is a strong need for objective data about stability and performance of Mobile Broadband (MBB) networks, and for tools to rigorously and scientifically assess their performance. In particular, it is important to measure and understand the quality as experienced by the end user. Such information is very valuable for many parties including operators, regulators and policy makers, consumers and society at large, businesses whose services depend on MBB networks, researchers and innovators. In this paper, we introduce the MONROE measurement platform aimed to address this need. MONROE is an open, European-scale, and flexible platform with multi-homing capabilities to run experiments on operational 3G/4G Mobile Broadband networks. The MONROE platform enables accurate, realistic and meaningful monitoring and assessment of the performance of MBB networks. MONROE also provides WiFi connectivity mimicking multi-homing in smartphones with both MBB and WiFi interfaces, to allow experimenting on different access technologies as well as to explore new ways of combining them to increase performance and robustness.
ABSTRACT: Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.
ABSTRACT:In this paper we study a surveillance system forpublic transport vehicles, which is based on the collection ofon-board videos, and the upload via wireless transmission to acentral security system of video segments corresponding to thosecameras and time intervals involved in an accident. We assumethat vehicles are connected to several wireless interfaces, providedby different Mobile Network Operators (MNOs), each charging adifferent cost. Both the cost and the upload rate for each networkinterface change over time, according to the network load andthe position of the vehicle. When a video must be uploadedto the central security, the system has to complete the uploadwithin a deadline, deciding i) which interface(s) to use, ii) whento upload from that interface(s) and iii) at which rate to upload.The goal is to minimize the total cost of the upload, which weassume to be proportional to the data volume being transmittedand to the cost of using a given interface. We formalize theoptimization problem and propose greedy heuristics. Results aregenerated, using real wireless bandwidth traces, showing that oneof the proposed greedy heuristics comes very close to the optimalsolution.
ABSTRACT:We consider a surveillance system for public transport vehicles, which is based on the collection of on-board videos, and the upload via mobile network to a central security system of video segments corresponding to those cameras and time intervals involved in an accident. We assume that vehicles are connected to several wireless interfaces, provided by different Mobile Network Operators (MNOs), each charging a different cost. Both the cost and the upload rate for each network interface change over time, according to the network load and the position of the vehicle. When a video must be uploaded to the central security, the system has to complete the upload within a deadline, deciding i) which interface(s) to use, ii) when to upload from that interface(s) and iii) at which rate to upload. The goal is to minimize the total cost of the upload, which we assume to be proportional to the data volume being transmitted and to the cost of using a given interface. We formalize the optimization problem and discuss greedy heuristics to solve it. Then, we discuss scientific and technical challenges to solve the system.
ABSTRACT: 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’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.
ABSTRACT: Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity.
ABSTRACT: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.
ABSTRACT:There Consumption of multimedia content is moving from a residential environment to mobile phones. Optimiz- ing Quality of Experience—smooth, quick, and high quality playback—is more difficult in this setting, due to the highly dynamic nature of wireless links. A key requirement for achieving this goal is estimating the available bandwidth of mobile devices. Ideally, this should be done quickly and with low overhead. One challenge is that the majority of connections on mobiles are short-lived TCP connections, where a significant portion of data exchange is within the slow start phase. In this paper, we propose a novel method that passively estimates the currently available bandwidth by monitoring the minimal traffic generated by such connections. To the best of our knowledge, no other solution can operate with such constrained input. Our estimation method is able to achieve good precision despite artifacts introduced by the slow start behavior of TCP, mobile scheduler and phone hardware. We evaluate our solution against traces collected in 4 European countries. Furthermore, the small footprint of our algorithm allows its deployment on resource limited devices.
The 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
ABSTRACT: The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the fu- ture wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users’ expected Quality- of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user’s demand and expected channel gain. In this paper, we use this information to for- mulate an optimal admission control scheme that maximizes the number of accepted users into the system with the con- straint that not only the current but also the expected de- mand of all users must be satisfied. Together with the opti- mal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuris- tic. Numerical results show the performance of the proposed scheme with respect to the state of the art.