The UK LSCITS Initiative involved around twenty active cloud-computing researchers, spread around the country. Some of whom first worked on issues in remotely-accessed ultra-large-scale data-centres in the mid-1990's, long before the phrase "cloud computing" came into common usage; several of whom were working on cloud-computing research projects that involve collaboration with (and sponsorship from) major multinationals such as BT, HP, and IBM; others worked with small and medium-sized businesses to explore new opportunities that cloud computing opens up. All had very good links with the St Andrews Cloud Computing (STACC) Laboratory, where they had their own cloud hardware and software test-rigs. We published our work in leading international conferences and journals; we held regular internal project meetings; and we organized occasional public events, such as the 2011 LSCITS/SICSA Cloud-Computing Summer School.
Mustafa Aydin started his EngD in October 2010. He is based with BT's Security Futures Practice at Adastral Park, and supervised at the University of York. He previously gained a MSc Computer Science from University College London. The topic of his research is in cloud computing security, looking at cloud federation and brokerage.
Adam Barker is a Lecturer in the School of Computer Science, University of St Andrews. Prior to moving to St Andrews, Adam worked as a postdoctoral researcher at the University of Melbourne, the University of Oxford and the National e-Science Centre (NeSC), University of Edinburgh. He completed his PhD at the School of Informatics, University of Edinburgh.
Adam's broad research interests concentrate on the theoretical foundations and effective engineering of large-scale distributed systems, his current interests are in Cloud computing infrastructures. He is Co-Investigator of the Elastic Virtual Infrastructure for Research Applications (ELVIRA) project, funded via EPSRC and JISC. For a more detailed biography and list of publications please visit: http://www.adambarker.org/
Dr Baxter is interested in security issues in cloud computing, and how they relate to resilience and critical infrastructure considerations,and in how to deploy cloud systems in a way that does not adversely affect user experience.
Radu Calinescu is interested in the automated, model-driven and metadata-driven engineering of cloud-based systems and systems of systems. His recent work on cloud computing uses techniques from the areas of formal specification, modelling and verification to establish how aspects of cloud-based systems such as dependability, resilience and performance are affected by the new benefits and vulnerabilities that arise from deploying these systems in the cloud. This work has been carried out as part of joint research projects with the UK's National Health Service, Fujitsu, the Universities of Bristol, Oxford and St. Andrews, and Politecnico di Milano.
In October 2010, John Cartlidge became a Research Associate in Cloud Computing at the University of Bristol, working on the development of a rigorous simulation framework for modelling next-generation large-scale data centres for delivery and pricing of cloud services. He has a first class BSc in Artificial Intelligence & Mathematics (2000) and a PhD in Computer Science (2004), both from the University of Leeds, UK. After receiving his PhD he spent four years in industry working for Hewlett-Packard Labs European Research Centre, the London Stock Exchange (LSE), and other smaller companies on commercial research projects including: agent-based modelling of the LSE; statistical modelling of bidder behaviour on eBay; and development of a proprietary dark liquidity exchange (patent pending). From 2008-10, John worked as a Research Associate at the University of Central Lancashire, UK, where his research focused on evolutionary computation and finance. He is director and co-founder of Victria.net, a private consultancy company specializing in financial software design and development.
Dave Cliff commenced research on the self-organization and self-management of massive-scale computing systems in the mid-1990's. In 1996, while employed as an academic at the University of Sussex, Cliff was appointed to a seven-month Visiting Academic post at Hewlett-Packard Labs, and there he initiated HP's work on market-based control of ultra-large-scale computer systems. In 1998 Cliff left academia (resigning from the faculty of the MIT AI Lab, where he had moved in 1997) to join HP Labs as a full-time research scientist. By then, HP's plans for what was referred to internally as "planetary-scale computing" were maturing, and soon afterwards HP announced its "Utility Data Centre" program, an initiative launched at the same time as IBM's "Computing On Demand" and Sun's "N-1 Computing". These three initiatives are widely recognized as being the primary foundational forerunners of what is now widely referred to as cloud computing. In 2004, Seth Bullock and Dave Cliff wrote a briefing paper for the UK Government's Department of Trade and Industry, "Complexity and Emergence in ICT Systems", where the coming transition to cloud computing was clearly flagged as the next major shift in commercial provisioning of computing services. In 2005, Cliff was appointed as Director of the UK LSCITS Initiative, and since then has supervised a number of PhD researchers working on issues in cloud computing. Cliff, together with Radu Calinescu and Ian Sommerville co-authored the proposal to EPSRC for funding of the LSCITS Initiative's £1m research programme on cloud computing, which receives additional financial support from HP Labs, and from SICSA. Cliff has given many public lectures and presentations on issues in cloud computing, including: one to the UK government's Cabinet Committee on Science Policy in December 2003; a keynote exploring the likely impact of utility/cloud computing on automation of the global financial markets at TradeTech 2004; and a populist lecture for young people that has now been heard by around 10,000 schoolchildren.
Lu Feng is a third year D.Phil student at the Oxford University Computing Laboratory, working on the Predictable Software Systems research theme of the LSCITS Initiative. She holds a M.Phil in Computer Speech, Text and Internet Technology from the University of Cambridge and a B.Eng in Information Engineering from the Beijing University of Posts and Telecommunications (China). Her current research focuses on applying computational learning techniques to tackle the inherent challenge of scalability, for the formal modelling and analysis of systems that exhibit stochastic behaviours. Her supervisor is Prof. Marta Kwiatkowska.
Julian Friedman has worked as a developer and architect for IBM's Cloud Labs / High Performance On-Demand Solutions team for the past five years, on projects focused on dynamic and self-service provisioning and most recently as part of the architecture and development team for the cloud management subsystem that forms part of many IBM cloud products. Other projects have included helping to architect and build the IBM clouds used to support the Google/IBM Cloud Computing Academic Initiative in 2007, a joint project by the two companies to provide education and resources to train the next generation of engineers in this new computational paradigm. Research is focused on Cloud Computing and particularly on lowering the barrier to the creation of scalable enterprise-class cloud systems for developers by attempting to identify cloud 'primitives' which can be composed to develop cloud applications.
David Greenwood is interested in the socio-political issues that affect the engineering and adoption of large scale complex IT systems (e.g. Cloud computing, intra/inter-organisational IT systems, ...). Research has found that it is socio-political issues rather than technical issues that most significantly impede IT project performance when measured in terms of cost, scope, adherence to schedule and end-user satisfaction. His recent work on cloud computing uses techniques to elicit and evaluate the socio-technical benefits and risks of migrating application from in-house data centres to a cloud based infrastructure-as-a-service. The analysis approaches that he has developed through-out his research enable practitioners to model the impact a project and its deliverable (e.g. an IT system with its associated organisational change) may have on stakeholders and thus enable informed decisions to be made about the feasibility of a particular undertaking.
Kenneth Johnson is a postdoctoral research fellow at Aston University. He holds a PhD from Swansea University. Before joining the LSCITS project he held positions at INRIA in Rennes, France and at the University of Sheffield. His main research interests lie in formal methods applied to both software systems and data types. His work on the LSCITS project aims to provide a theoretical foundation to specify properties such as service level agreements, analyse and verify non-functional properties of cloud-based systems, and apply these results to real-world scenarios.
Ali Khajeh-Hosseini is investigating the challenges of migrating enterprise IT systems to infrastructure-as-a-service clouds. He is developing tools to enable decision makers to make informed trade-offs between the costs, benefits and risks of using the cloud. One of the tools he has developed, called Cost Modelling, enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. This tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios.
Carrying out research in the area of online machine learning for self-star, cloud-based systems. Initially my research work will target service-based systems deployed in the cloud, as their component services are often characterised by variable non-functional parameters such as service rates and failure rates. The machine learning algorithms and techniques developed as part of this research will ensure that the operational models that guide self-adaptation in cloud-based systems are brought and maintained in sync with the actual behavior of these systems, based on observations of this behavior. These techniques will enable self-star, cloud-based systems to attain improved adaptiveness when the initial models they base their decisions upon are approximate, and when the system behavior changes over time.
I am broadly researching the challenges facing cloud computing as scale and complexity increase. My current research is focused on the economic changes which occur as a result of this disruptive technology, in particular, the buying and selling of utility computing resource as a commodity in competitive options and futures markets.
John Rooksby is a senior research fellow at the University of St Andrews. He holds a PhD in Computer Science from the University of Manchester and has previously held research positions at Lancaster University and The University of Salford. His research interests are in sociotechnical systems engineering, particularly in organisational and social issues in the development and deployment of technology. In the context of cloud computing, he is particularly interested in sociotechnical approaches to the design and management of services and to increasing their dependability. He is currently focusing on work practice issues in the management and operation of data centres, and on the changing nature of technical support. He is also interested in cloud computing and the consolidation of computing and information infrastructure in the context of government and healthcare.
James Smith is a doctoral research student at the University of St Andrews in Scotland. He graduated with a B.Sc in Computer Science from St Andrews in 2009 then returned to purse a Ph.D with an LSCITS initiative funded studentship. His supervisor is Prof. Ian Sommerville.
Since starting his research course in September 2009 he has begun to look at Socio- Technical Issues in Cloud Computing. In particular he is interested in the potential energy efficiency impacts of Cloud systems and how they effect organisations.
Generally interested in problems of dependability and healthcare systems, with a focus on socio-technical factors that influence system dependability. Also interested in links with cloud computing and, in particular, the issues around migrating healthcare system to an NHS cloud.
Cloud computing research interests Generally, research interests are around migrating infrastructure and application systems to the cloud including cost and risk modelling, data migration, modelling application portfolios and high-value services. Also interested in using clouds in an energy efficient way.
Ilango Sriram is a PhD student at the University of Bristol, funded by Hewlett-Packard Labs. Ilango has a Master's degree in Computer Science from the Technical University of Munich (Germany). He spent a year as a research associate at the Hewlett-Packard Research Labs in Bristol, UK, where he worked on automated mapping of business processes to application and infrastructure configuration, and explored ways of monitoring and managing virtualised infrastructures in data centres. He started his PhD, fully sponsored by HP Labs, in Oct. 2007 and is looking into ways of understanding and modelling increasing complexity and dynamics in future generations of data centres.
Charlotte Szostek is a PhD student at the University of Bristol She is a member of the Bristol Centre for Complexity Science, having completed a Master of Research in Complexity Science in 2010. She completed a project with Biochemistry on The Integration of Receptor Trafficking and Signalling in Angiogenesis and a Second with Dave Cliff on Automated Algorithmic Trading. Charlotte holds a MSc in Earth Systems Science; funded by the NERC; and a BEng in Civil Engineering; during which she was a Queen's Jubilee Scholar of the Institute of Civil Engineering, receiving funding from the Institute and representing it. She started her PhD in February 2011 and is investigating automated Algorithmic Trading from a Complex System perspective. She is looking at ways to use automated trading algorithm to better understand financial markets as complex adaptive systems; their dynamics and emergent features.
Essentially, in our view, cloud computing reflects a radical movementtowards efficient provision, utilisation, and re-use of resources, capabilities, and processes, such as IT infrastructure, platform, software, business, and human capacities. It aims to realise such a vision by taking a centralized approach to offer efficiency, elasticity, abstraction, and flexibility, through mass provision and reuse.
Despite its great potential, such a radical change is very challenging. Current research on cloud computing in computer science and information systems mainly concentrates on tackling well-understood technical and organizational challenges, unfortunately, in an isolated fashion. That is, there is a lack of holistic view that acknowledges a large ecosystem of cloud systems, which comprise both social and technical elements in an inter-connecting way. Due to the increased scale of cloud systems and their involved service value chains, the cloud ecosystem comprises more complex internal elements, for example: legacy IT systems, service provision systems, service usage, supporting infrastructures, business processes and innovations, standards, regulations and legislation, and various stakeholders at individual, organisational, and social level. As a result, in our view, it is critical to obtain good understanding of these inter-connected elements, and to synthesise them as an organic whole, in order to effectively derive and operate cloud systems that create added value in an efficient way.
Therefore, my research interest, in a broad sense, is to understand critical socio-technical factors involved in the processes of cloud adoption, diffusion, and sustainability, and to facilitate these processes by bridging the gaps between social and technical aspects, preferably, though pragmatic methods and tools. More specifically, this research focuses on understanding and modelling of organisational and social drivers and obstacles of cloud adoption and diffusion. For example, what are the benefits and risks for organisations to adopt cloud computing, form the perspective of individual, enterprises, or countries, and moreover, how to model, monitor, and manage them? Based on the obtained understanding and experience, the research aims to create a practical supporting environment to enable and foster business adaptation and innovations based on technical infrastructures of cloud computing.
HEALTH & SOCIAL CARE