While Cloud computing is now found everywhere, and has the potential to be the next generation utility computing model, it carries at least two major risks. The first is its potential will not be fully realised. The second is the solution will cover only a small range of business opportunities and be superseded quickly. While progress has been made, there remains a wide range of concerns. Some are technical, such as architecture, security and applications. Others are enterprise-related - the need for business concerns to be addressed by Cloud technology solutions.
The Cloud Computing Research Group examines new trends and concepts for the management of hybrid cloud systems, to provide intelligent solutions for high throughput and computational problems utilising large-scale data in the cloud environment. We also contribute to standards and governance strategy nationally and internationally, and coordinate and develop cloud research activities. This combination of research strengths, covering a range of technology and business concerns, is a distinctive feature of the Cloud Computing Research Group.
Our research is currently organised around four themes:
Security & Governance: Identity Security and Management in the Cloud; License Management Services; Data Security in the Cloud; Governance – Standards, Models and Architectures
Services & Architectures: Design & Modelling of Cloud based solutions; Runtime Architectures – Monitoring and Adaptation; Parallelisation; Autonomic Scaling and Elasticity in the Cloud; Network Architectures and Performance
Applications: High Performance Computing in the Cloud; Microarray analysis; E-learning in the Cloud
Cloud Adoption and Business Models: Cloud Migration Patterns and Processes; Data Quality in the Cloud
Dr Geoff Hamilton
Dr Markus Helfert
Dr Mark Humphrys
Ms Jane Kernan
Dr John McKenna
Dr Rory O'Connor
Dr Claus Pahl
Prof. Heather Ruskin
Dr David Sinclair
Mr Ray Walshe
IC4 (Irish Centre for Cloud Computing and Commerce)
Lero (The Irish Software Engineering Centre)
CNGL (Centre for Global Intelligent Content)
INSIGHT (Centre for Data Analytics)
Example Research Projects
Autonomous Cloud Auto-scaling and Auto-Management: Resource sharing in the cloud allows a higher utlisation rate that in turn reduces cost. In order to achieve this, autonomous solutions to manage changing and diverse, often even uncertain requirements are addressed through auto-scaling techniques based on fuzzy logic. Another aim is to build exascale cloud ecosystems, including the development of autonomous management and workload-specific cloud infrastructure. Specific concerns include scalability, resilience, performance and energy efficiency.
PaaS Migration and Interoperability: cloud on-boarding and in-cloud migration are still key concerns. The first aim is to investigate techniques to optimise software for deployment in the cloud and to support the migration process through processes and patterns. While all layers have been investigated, our key target is platform-as-a-service. Software re-engineering and performance engineering are additional focus areas. Modernised and re-engineered software can fully utilise cloud technology benefits such as elasticity and cost-effective deployment. A key concern for migration into, but also within the cloud is interoperability that is addressed through standardisation and interoperable workload management.
Data Security and Management: this project aims to develop a cloud-based infrastructure that will enable users to securely store data in a cloud and control how the data is accessed by users and services. Users and services will store and access data through Trusted Access Services. Each data object stored in the cloud will be encrypted by the Trusted Access Service and only exist in the cloud in its encrypted form. Each data object will have an associated Usage Policy that is signed by the Trusted Access Service. This Usage Policy will define who can access the data and how the data object will be managed by the Trusted Access Services. The Usage Policy will also record the activity on the data object to enable an audit trail to be maintained on each data object. The internal protocols used in the Trusted Cloud will be modelled and verified to ensure that the data stored within the Cloud is not leaked from the Trusted Cloud.