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.
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.