Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks

We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NP-completeness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomial-time approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution

Wi-Fi Offload: Tragedy of the Commons or Land of Milk and Honey?

Fueled by its recent success in provisioning on-site wireless Internet access, Wi-Fi is currently perceived as the best positioned technology for pervasive mobile macro network offloading. However, the broad transitions of multiple collocated operators towards this new paradigm may result in fierce competition for the common unlicensed spectrum at hand. In this light, our paper game-theoretically dissects market convergence scenarios by assessing the competition between providers in terms of network performance, capacity constraints, cost reductions, and revenue prospects. We will closely compare the prospects and strategic positioning of fixed line operators offering Wi-Fi services with respect to competing mobile network operators utilizing unlicensed spectrum. Our results highlight important dependencies upon inter-operator collaboration models, and more importantly, upon the ratio between backhaul and Wi-Fi access bit-rates. Furthermore, our investigation of medium- to long-term convergence scenarios indicates that a rethinking of control measures targeting the large-scale monetization of unlicensed spectrum may be required, as otherwise the used free bands may become subject to tragedy-of-commons type of problems.Comment: Workshop on Spectrum Sharing Strategies for Wireless Broadband Services, IEEE PIMRC'13, to appear 201

Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

On Factors Affecting the Usage and Adoption of a Nation-wide TV Streaming Service

Using nine months of access logs comprising 1.9 Billion sessions to BBC iPlayer, we survey the UK ISP ecosystem to understand the factors affecting adoption and usage of a high bandwidth TV streaming application across different providers. We find evidence that connection speeds are important and that external events can have a huge impact for live TV usage. Then, through a temporal analysis of the access logs, we demonstrate that data usage caps imposed by mobile ISPs significantly affect usage patterns, and look for solutions. We show that product bundle discounts with a related fixed-line ISP, a strategy already employed by some mobile providers, can better support user needs and capture a bigger share of accesses. We observe that users regularly split their sessions between mobile and fixed-line connections, suggesting a straightforward strategy for offloading by speculatively pre-fetching content from a fixed-line ISP before access on mobile devices.Comment: In Proceedings of IEEE INFOCOM 201

Inference of User-Facing Packets from Network Traffic

International audienceThis thesis presents a technique to determine whether a user is actively interacting with a device from inside the network. By the observation of user's traffic, network operators can use the detection method to improve traffic management by prioritizing user facing flows. Understanding whether a user is using a device from inside the network requires separating user generated traffic from the one that is automatically generated by a machine in the background. To accomplish this, we apply three filtering methods, looking at request URLs and at the inter events distance and periodicity. Using real world data from eight devices in the UK collected with a tool capable of recording not only network traffic, but also user input, we show how our method separates the traffic generated by the user

Adaption algorithms for mobile traffic offloading

Der durch Smartphones generierte Datenverkehr in Mobilfunknetzen steigt seit Jahren unablässig an. Dazu trägt sowohl die steigende Verbreitung der Smartphones, als auch die immer größer werdenden Bandbreitenanforderungen z.B. durch Audio- oder Videostreaming bei. Um den Zuwachs an nötiger Bandbreite zu reduzieren, soll entstehender Traffic, von dem Mobilfunknetz auf lokale WLAN Kommunikation verschoben werden. Eine besondere Art dieser Auslagerung bezeichnet man als "Opportunistic Traffic Offloading". Dabei tauschen benachbarte Smartphones beispielsweise per WLAN Daten aus, wenn sie sich begegnen. Im Laufe dieser Arbeit werden Algorithmen vorgestellt, die anhand der Positionsdaten von Smartphones besonders günstige Ziele, das heißt Smartphones mit einer hohen Anzahl an Begegnungen, bestimmen. Diesen Zielen kann per Mobilfunk die entsprechende Nachricht übermittelt werden, woraufhin sie selbstständig die Nachricht per WLAN verbreiten. Eine hohe Zahl an Begegnungen fördert dabei die schnelle Verbreitung von Nachrichten, da nach jeder Begegnung ein weiteres Smartphone in der Lage ist die Nachricht weiter zu verteilen. Weiterhin wird gezeigt, wie mit dem periodischen Versenden einer Nachricht per Mobilfunk und dem Anwenden der beschriebenen Algorithmen, im Vergleich zum reinen Versenden per Mobilfunk, Traffic-Einsparungen von durchschnittlich 40 bis 50% möglich sind

Spatio-Temporal Predictability of Cellular Data Traffic

The knowledge of the upper bounds of mobile data traffic predictors provides not only valuable insights on human behavior but also new opportunities to reshape mobile network management and services as well as provides researchers with insights into the design of effective prediction algorithms. In this paper, we leverage two large-scale real-world datasets collected by a major mobile carrier in a Latin American country to investigate the limits of predictability of cellular data traffic demands generated by individual users. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of mobile traffic consumption patterns in isolation. Our results show that it is theoretically possible to anticipate the individual demand with a typical accuracy of 85% and reveal that this percentage is consistent across all user types. Despite the heterogeneity of users, we also find no significant variability in predictability when considering demographic factors or different mobility or mobile service usage. Then, we analyze the joint predictability of the traffic demands and mobility patterns. We find that the two dimensions are correlated, which improves the predictability upper bound to 90% on average