Measurement

On the Distribution of Traffic Volumes in the Internet and its Implications

Conference paper
Proc IEEE Infocom

This paper updates previous work on fitting traffic profiles. We use more modern statistical techniques to question (and refute) previous assumptions about heavy tails in statistics. In this case we believe that the best fit for traffic volume per unit time is the log-normal distribution. Tail distributions an have big impacts for capacity planning and for prediction of pricing (say 95th percentile).

Mobile Sensor Data Anonymization

Workshop paper
ACM/IEEE International Conference on Internet-of-Things Design and Implementation

This paper looks the problem of releasing time-series data when privacy is a concern. It uses information theory to look at what extra information could "leak" if our device sends motion data. For example, can users be reidentified or can features such as height and weight be determined. A machine learning framework is given that can produce a tradeoff between allowing useful data to pass through while distorting the signal minimally to disguise information we wish to be private.

On rate limitation mechanisms for TCP throughput: a longitudinal analysis

Journal Paper
Computer Networks

This paper is a considerably expanded version of the INFOCOM paper.

Again it argues that TCP is no longer mainly controlled by loss and congestion but instead by algorithms and settings under the control of the sender or receiver deliberately or accidentally designed to restrict throughput for a variety of reasons (for example limiting video sending to the rate at which the viewer is watching).

It contains extended discussion of the methodology and in particular how flight and RTT data was extracted from passive traces.

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