Systems Research Group

This is the website for the Systems Research Group (SRG) at the University of St Andrews. Systems is the largest research area in the School of Computer Science, covering the broad areas of distributed systems, networked systems, sensor systems and data-intensive systems.

The Systems Research Group (SRG) consists of twelve interdisciplinary faculty members, and a large cohort of Research Fellows and PhD students. Our interdisciplinary nature means that we can work on projects which cross multiple areas of systems research, many in collaboration with industry partners. Members of the group have expertise in research areas spanning: data centres, cloud computing, many-core systems, networking, middleware, sensor networking, machine learning, Internet of Things (IoT), autonomic computing and software architectures.

We take a very practical approach to research, by building and evaluating real systems, whilst publishing in many of the top-tiered systems research conferences and journals. SRG research is currently funded through the following organisations:

SRG runs a bi-weekly seminar series every other Thursday at 1pm in JC 1.33B during semester time. There are talks from faculty, research fellows, PhD students and visitors. Please check our exciting schedule.

News and Events

The latest Systems Research Group posts from the School of Computer Science blog.

SRG Seminar: “Simulating a pulmonary tuberculosis infection using a network-based metapopulation model” by Michael Pitcher and “A Fake City of People: Modeling the Co-evolution of City and Citizens” by Xue Guo

Event details
When: 28th September 2017 13:00 – 14:00
Where: Cole 1.33b
Series: Systems Seminars Series
Format: Seminar

Michael Pitcher’s abstract

Tuberculosis (TB) is one of the world’s most deadly infectious diseases, claiming over 1.4 million lives every year. TB infections typically affect the lungs and treatment regimens are long and arduous, requiring at least 6 months of daily chemotherapy. Previous investigations have shown TB to have unique localisations within the lung at varying stages of infection. The initial implant and the primary lesion which arises from it can occur anywhere in the lungs, with a greater probability of occurrence in the lower to middle regions of the lung. However, reactivation of a previously latent form of disease always involves cavitation of the tissue at the apical regions. This difference in spatial location of TB infections suggests two important factors: i) bacteria are able to disseminate across the lung in some manner, and ii) the environment at the top of the lung has some properties that make it preferential for TB replication.

In this project, we aim to build a whole-organ model of the lung and surrounding lymphatics which incorporates both bacterial dissemination possibilities and lung tissue spatial heterogeneity in order to understand their impact on TB. We develop ComMeN (Compartmentalised Metapopulation Network), a Python framework designed to allow the easy creation of complex network-based metapopulations with spatial heterogeneity upon which interaction dynamics can be applied, with discrete event modelling using the Gillespie Algorithm. We then extend this framework to create a TB-specific model, PTBComMeN, which models a TB infection occurring over lung tissue which is divided into patches, each of which contains spatial attributes appropriate to its position in the lung, such as ventilation, perfusion and oxygen tension. Events dictate the interactions between cells and bacteria and their interaction with the environment, with dissemination occurring between edges joining patches on the lung network. This model allows experimentation into studying the effects spatial heterogeneities and bacterial dissemination may have on the progression of disease and the model is designed to provide insight into the factors that result in long treatment times for TB.

Xue Guo’s abstract

By the year 2050, the global urban population will reach 2.5 billion. While the fast pace of urbanisation brings improved quality of life initially, the surging population will inevitably lead to unique urban issues. Emerging research fields, with the aim of creating smarter cities, plan to counteract these problems. To facilitate this research, we need solid models to generate ’fake cities’, which cannot be easily produced by existing random graph algorithms due to spatial constraints. Therefore, we propose a new model for the co-evolution of city and population, which can show how street network forms, how population spreads and how settlements emerge and diminish. The new model will be a random city generator, which could be used to backtrack the history and predict the future of a city, or act as test cases for the validation and evaluation of urban optimisation algorithms.

Containers for HPC environments

Rethinking High performance computing Platforms: Challenges, Opportunities and Recommendations, co-authored by Adam Barker and a team (Ole Weidner, Malcolm Atkinson, Rosa Filgueira Vicente) in the School of Informatics, University of Edinburgh was recently featured in the Communications of the ACM and HPC Wire.

The paper focuses on container technology and argues that a number of “second generation” high-performance computing applications with heterogeneous, dynamic and data-intensive properties have an extended set of requirements, which are not met by the current production HPC platform models and policies. These applications (and users) require a new approach to supporting infrastructure, which draws on container-like technology and services. The paper then goes on to describe cHPC: an early prototype of an implementation based on Linux Containers (LXC).

Ali Khajeh-Hosseini, Co-founder of AbarCloud and former co-founder of ShopForCloud (acquired by RightScale as PlanForCloud) said of this research, “Containers have helped speed-up the development and deployment of applications in heterogeneous environments found in larger enterprises. It’s interesting to investigate their applications in similar types of environments in newer HPC applications.

PhD viva success: Long Thai

Congratulations to Long Thai, who successfully defended his thesis today. He is pictured with supervisor Dr Adam Barker, Internal examiner Dr John Thomson and external examiner Dr Rami Bahsoon, from the University of Birmingham. Long is joining Amazon as a Software Engineer.