The aim of this paper is to provide a summary and a critique of power law modelling in the internet. Long-range dependence and self-similarity are considered as well as scale-free topology analysis.
The investigation of network traffic using statistical analysis to gain insight into performance and behaviour.
This paper looks at a mechanism related to Explicit Congestion Notification. It uses a single bit in the IP header to communicate the congestion at each hop in the path. Statistical estimators are used to work out the accuracy of the congestion estimation.
This paper looks at the phenomenon of long-range dependence. It shows that certain long-range dependent models give answers which contain infinities and also that this behaviour will not be detected by a naive modelling approach. The work is an extension of an earlier published PMECT paper.
This paper looks at P2P traffic over bittorrent from a large database of torrents. The paper considers the effects of localising bittorrent traffic on performance and ISP cost saving.
Data: The data set is one of the impressive things about this paper. 100K torrents of which 40K active. Demographics from 3.9M concurrent users and 21M total users over a day from 11K ISPs. Speed test results from ookla and iplane.
This paper looks at ways of predicting the TCP throughput of a connection. The assumption is that some information is available about the connection. A comparison is made between “formula based” (FB) prediction, that is using round-trip time and loss versus time series analysis prediction (referred to here as history based (HB)), that is using previous measurements on the same connection. Both approaches require some measurements from the connection already.
This paper looks at CDN networks and, in particular, suggests Provider-aided Distance Information System (PaDIS), which is a mechanism to rank client-host pairs based upon information such as RTT, bandwidth or number of hops. Headline figure, 70% of http traffic from a major european ISP can be accessed via multiple different locations. “Hyper giants” are defined as the large content providers such as google, yahoo and CDN providers which effectively build their own network and have content in multiple places.