2013-Present. The goal of the Broker@Cloud project is to develop a framework that will equip cloud service intermediaries with advanced methods and mechanisms for continuous quality assurance and optimization of software-based cloud services. The framework will allow enterprise cloud service brokers to monitor the obligations of providers towards consumers, as well as to detect opportunities for optimising service consumption. Broker@Cloud is funded by the European Community's Seventh Framework Programme FP7/2007-2013 under Objective 1.2 'Cloud Computing, Internet of Services and Advanced Software Engineering'.
OPTIMIS - Optimized Infrastructure Services, 2011-2013. The high-level objective of OPTIMIS is to enable an open and dependable Cloud Service Ecosystem that delivers IT services that are adaptable, reliable, auditable and sustainable (ecological and economical). The key goal of the project was to allow organizations to automatically and seamlessly externalize services and applications, to trustworthy and auditable cloud providers. This ecosystem will give rise to a strengthened European ICT industry able to meet key societal and economical needs. The project is funded under the 'Software and Service Architectures and Infrastructures' track of the EU's FP7 framework program and started from June 2010. Dr. Kiran's work focused on risk assessment on services and SLAs on the cloud infrastructures. She was also involved with various activities in the project to do with building trust, eco-efficiency and cost (TREC factors) on the Cloud and Cloud Brokerage use case.
EURACE - Modelling of the European Economy using Agents, EU FP6 Project, 2007-2010. Model and do policy analysis of the economic flow in the European Union. The study and the development of multi-agent models that reproduce, at the aggregate economic level, the emergence of global features as a self-organized process from the complex pattern of interactions among heterogeneous individuals. Involved writing economic models of the labour and credit markets, collecting simulation data for dissemination and analysis later.
SPICE - Simulation and Prediction in Crowded Environment, 2009-2010. Working with the Graphics Research Group. Using the FLAME-GPU, a version of FLAME which run on a graphical processing unit is funded by the competition of Ideas Grant by the Defense Science and Technology (DSTL) division of MOD. Designing and implementing agent based models which could be ported directly on the Nvidia cards using FLAME. Also designed models for traffic modelling which later earned funding as a separate research project. Focused on social behaviour modelling for predicting dangerous elements within the crowd, working with University of Birmingham.
Working with the Sociological Studies Department. Modelling social networks based on economic factors, 2009-2010, ESRC-Funded Project. Studying the effects of social networks made on the basis of economic factors. Social capital and small worlds. Designing and implementing computational models, merged tools from social analysis background (UCINET) to work with FLAME agent based modelling framework and investigating algorithmic issues using MATLAB.
FLAME - Flexible Large Agent based Modelling Environment, 2007-2010, been EU-funded and RC-UK funded project. FLAME is an EU-funded and RC-UK funded, developed into a professional tool for biologists and economists to use. Contributions include: Development of the agent-based modelling framework, providing analysis, testing and deployment tools of the framework, liaising with Rutherford Appleton Laboratories for parallel batch run testing of agents over parallel machines. Dr. Kiran also taught FLAME tutorials to MSc and PhD Students and supervised MSc dissertations.
Automated Discovery of Emergent Misbehaviour (Misbehaviour): involved in contributing to developing methods for testing of agent-based models and model contribution.
Modelling epithelial cells, 2007-2010, EPSRC funded project. Development of software tools for data analysis and measurement with agent-based models for cell structures. Implementing the model in parallel batch runs on the mainframe computer Iceberg on the White Rose consortium and documentation of the software code.