Research about modelling aspects of networks.

Likelihood based framework for evolving graphs

Invited talk
UCL Statistics

This talk is the latest of my talks about FETA the framework for evolving topology analysis. This uses updated notation. The core of the work is a likelihood based model which can assess how likely it is that observations of the evolution of a graph arise from a particular probabilistic model, for example a model such as the Barabassi-Albert preferential attachment model. Analysis is given to data from Facebook and from Enron as well as from artificial models.

Criticisms of modelling packet traffic using long-range dependence (extended version)

Journal Paper
Journal of Computer and System Sciences, 77(5)

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.

On the predictability of large transfer TCP throughput

Qi He, Constantine Dovrolis and Mostafa Ammar

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.

The power of prediction -- Cloud bandwidth and cost reduction

Eyal Zohar, Israel Cidon and Osnat Mokryn

This paper deals with reducing costs for cloud computing users. Cloud customers use “Traffic Redundancy Elimination” (TRE) to reduce bandwidth costs. Redundant data chunks are detected and removed – cloud providers will not implement middleboxes for this as they have no incentive. The paper gives a TRE solution which does not require a server to maintain client status. The system is known as PACK “Predictive ACKnowledgements” which is receiver driven.

Towards predictable datacenter networks

Hitesh Ballani, Paolo Costa, Thomas Karagiannis and Ant Rowstron

This paper looks at the issue of reducing variability in performance in data centre networks. Variable network performance can lead to unreliable application performance in networked applications – this can be a particular problem for cloud apps. Virtual networks are proposed as a solution to isolate the “tenant” performance from the physical network infrastructure. The system presented is known as Oktopus. The system provides a tradeoff between guarantees to tenants, costs to tenants and profits to providers by mapping a virtual network to the physical network.


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