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Staff Profile

Dr Nigel Thomas

Reader in Performance Engineering

  • Email: nigel.thomas@ncl.ac.uk
  • Telephone: +44 191 208 8182
  • Address: Room 3.038,
    School of Computing,
    Urban Sciences Building,
    1 Science Square,
    University of Newcastle upon Tyne,
    Newcastle upon Tyne, NE1 7RU
    United Kingdom
Background

Introduction

I am Associate Dean for Postgraduate Research in the SAgE Faculty.

I joined the School of Computing Science in January 2004 from the University of Durham, where I had been a lecturer since 1998. I was promoted to reader in 2009. My research interests lie in scalable performance analysis for networks, distributed algorithms, Cloud and IoT using queueing theory, stochastic process algebra and simulation. See my Google Scholar Citations page.

I was awarded a PhD in 1997 and an MSc in 1991, both from the University of Newcastle upon Tyne. I have previously held a number of positions of responsibility within the School, including Deputy Head of School, Director of Teaching and Director of Postgraduate Studies. 

I currently supervise 5 research students and I am always interested in further self-financed international students for the Integrated PhD in Computer Science. Research topics of interest are listed below, please contact me for further information.

I am interested in the application of performance modelling to a variety of different problem areas. My preferred approach is to use stochastic process algebra, but I have also used queueing theory, stochastic Petri nets, discrete event simulation and trace-driven simulation. Different applications require different techniques for model specification and analysis. The challenge is forming the right models for a given problem and applying and developing appropriate analysis techniques. Some areas of current interest are listed here (in no particular order), but this is by no means exhaustive. Each area will have a number of possible projects.

1. Performance modelling of secure systems.

Security measures impose a performance overhead, as more work needs to be done in order to perform operations such as authentication, encryption and recording transactions. Understanding this overhead is vital in order to make intelligent choices in the design of secure systems. Of particular novel interest is the performance effect of attacks against the system. Choosing the right management policy (e.g. encryption key management) and a good protocol enables the system to maintain an acceptable performance even when under attack. 

2. Energy management in large scale systems.

Energy is a significant cost in the provision of large scale computing platforms, such as cloud computing. This cost is both environmental (CO2 output) and financial (£'s, €'s, $'s). Managing the level of resource provision to handle the service demand can help to limit the amount of energy used and hence decrease costs. However, it is not a simple matter to predict what demand will be in future. One approach is to form adaptive policies which react to changing demand. Different policies will work in different situations and are highly sensitive to the tuning of particular parameters.

3. Wireless network performance analysis.

Wireless networks are used for a wide range of applications in a wide range of operating scenarios. Clearly it is infeasible to expect that a given network protocol will perform well in all situations, as protocol are typically designed to perform well in respect to a limited set of performance metrics under a limited set of operating conditions. It is therefore necessary to understand the limitations of such protocols in terms of factors such as fairness and energy, when the network topology is sub-optimal or where nodes are misbehaving, for example when compromised due to infection by a virus or a fault, or subject to a denial of service attack.

4. Electric vehicle performance and planning.

Electric vehicles are rapidly developing to become the a significant part of a low carbon transport provision. However, at present, infrastructure and technology is still evolving, leading to issues such as range anxiety and recharging planning. Modelling these systems can provide insight into battery state of charge prediction and efficient journey planning. 


Publications