This paper is a presentation of the FETA framework and new work with Naomi Arnold on time varying models.
Research about modelling aspects of networks.
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).
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.
This paper describes the Raphtory system which is used to analysis large-scale time-varying graph systems. It can ingest streaming graph information and store the complete graph history. It enables queries to be made over the graphs at different points in that graph's history.
This paper is a simulation based study of cloud assisted multi-user video streaming. It is based upon two use cases (one related to video poker the other related to MOOCs). The paper looks at strategies for placing cloud locations to facilitate streaming using Amazon EC2 cloud locations. The paper compares a strategy that dynamically picks new locations for cloud hosts as time goes on. Interestingly this seems to provide little benefit compared with simply having a good initial choice of sites even when users may drop into and out of a cloud chat session over the course of many hours.
This talk describes FLICK a system for the application-specific middlebox. It consists of three parts:
1) A domain specific language for the middlebox that allows easy development of typical middlebox functions.
2) An abstraction, the task graph, that allows the breaking of middlebox functions into easily parallelisable work units.
3) The system -- this implements the compiled language, handles TCP connections and memory management.
The whole system is comparable in speed to a specialist implementation.
This paper used a likelihood based framework to create a rigorous way to assess models of networks. Network evolution is broken down into an operation model (it decides the 'type' of change to be made to the network, e.g. "add node" "add link" "remove node" "remove link") and an object model (that decides the exact change -- which node/link to add).
The system is shown to be able to recover known parameters on artificial models and to be useful in analysis of real data.
This work can generate graphs from a very large family with the aim of fitting those graph to parameters of real data sets.
This is an extended unpublished 14 page version with all proofs of the short paper (2 pages) accepted for SIGMETRICS 2014 as a poster/short paper.
This paper is a development of the earlier ideas in PREFLEX -- http://www.richardclegg.org/node/18
In this case the focus is resilience within a data centre. In particular resilience at the network layer. If several paths are available to a destination the system known as INFLEX can support fail over between paths seamlessly using OpenFlow. In this case the system is tested using Openvswitch.
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.