DYNAMIC TRANSMISSION CONTROL FOR IMPROVING MOBILE DATA USAGE

This paper describes techniques for dynamically calculating values of one or more Transmission Control Protocol (TCP) memory buffer size variables (e.g., “tcp_mem,” “tcp_rmem,” and/or “tcp_wmem” variables), based on consideration of real-time network conditions, to achieve improved data usage of a mobile computing device. By dynamically determining and/or adjusting the values of TCP memory buffer size variables, the described techniques enable a mobile computing device (e.g., a mobile phone, tablet computer, wearable and/or headset device) to avoid sending too many in-flight packets that exceed network capacity, thereby reducing packet loss and the need for data retransmission from the mobile computing device. In some cases, the described techniques introduce and utilize a machine-learning model to predict suitable values of the dynamically determined TCP memory buffer size variables. The machine-learning model accepts a number of different features as inputs in order to produce a predicted output value of a memory buffer size variable. These features may include, for example, a specified time frame, real-time network allocated bandwidth, a geographic region (e.g., cell tower identifier or Global Positioning Satellite (GPS) location), and/or a packet loss rate, to name only a few examples

Mathematical and Statistical Opportunities in Cyber Security

The role of mathematics in a complex system such as the Internet has yet to be deeply explored. In this paper, we summarize some of the important and pressing problems in cyber security from the viewpoint of open science environments. We start by posing the question "What fundamental problems exist within cyber security research that can be helped by advanced mathematics and statistics?" Our first and most important assumption is that access to real-world data is necessary to understand large and complex systems like the Internet. Our second assumption is that many proposed cyber security solutions could critically damage both the openness and the productivity of scientific research. After examining a range of cyber security problems, we come to the conclusion that the field of cyber security poses a rich set of new and exciting research opportunities for the mathematical and statistical sciences

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Throughput Prediction in Cellular Networks: Experiments and Preliminary Results

International audienceThroughput has a strong impact on user experience in cellular networks. The ability to predict the throughput of a connection, before it starts, will bring new possibilities, particularly to the Internet service providers. They could adapt contents to the quality of service really reachable by users, in order to enhance their experience. First this study highlights the prediction capabilities thanks to different algorithms and data gathered at different network levels. Then we propose a simple approach based on machine learning to predict the throughput using a few data related to the context of use.Le débit d'une connexion possède un impact significatif sur la qualité d'expérience via les réseaux cellulaires. Savoir prédire le débit d'une connexion à venir permettrait d'offrir d'immenses possibilités, notamment aux fournisseurs de services Internet. Ces derniers pourraient ainsi adapter leurs contenus à la qualité de service accessible par l'utilisateur, dans le but de maximiser sa qualité d'expérience. En premier lieu, cette étude illustre les capacités de prédiction atteignables grâce à des données collectées à différents niveaux du réseau. Dans un second temps, nous proposons une approche simple fondée sur des méthodes d'apprentissage pour prédire le débit d'une connexion à partir d'information minimale sur le contexte d'utilisation