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Details of Grant 

EPSRC Reference: EP/C520246/1
Title: Topology and congestion Invariants in Global Internet-scale Networks
Principal Investigator: Mondragon, Dr RJ
Other Investigators:
Pitts, Professor JM
Researcher Co-Investigators:
Project Partners:
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary University of London
Scheme: Standard Research (Pre-FEC)
Starts: 01 November 2005 Ends: 31 October 2008 Value (£): 195,421
EPSRC Research Topic Classifications:
Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
The present use of telecommunication networks simulators as a test-bed to explore the behaviour of new Internet protocols has significant limitations. State of the art simulators can only accurately simulate relatively small networks, around 0.001% of the size of the Internet. Although the actual simulators have been used for exploring the behaviour of new Internet protocols, there is a correspondingly vast scale of magnitude discrepancy which undermines the testing potential of the simulation. The challenge that a researcher confronts when developing or testing new Internet protocols is to test a concept designed to work on networks of tens of millions of elements using simulations of only a few thousand elements.The research proposed here is to investigate how to model these very large networks by using smaller networks which conserve the behaviour of interest, e.g. traffic congestion. Firstly, to search for topology-traffic scale invariants, that is, properties of the network whose behaviour can be predicted as the network grows. Secondly to exploit these properties such that the behaviour of a large network can be modeled using a smaller network by taking into account the scale invariants. For example, starting from a small network, to be able to model events like traffic congestion or catastrophic failure due to a security attack and, easily and confidently extrapolate the results to a larger network.
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