Hadron Therapy (HT) exploits properties of ion radiation to gain therapeutic advantages over existing photon-based forms of external radiation therapy. However, its relative superiority and cost-effectiveness have not been proven for all clinical situations. Establishing a robust evidence base for the development of best treatment practices is one of the major challenges for the field. This thesis investigates two research infrastructures for building this essential evidence. First, the thesis develops main components of a metadata-driven software architecture for the collection of clinical information and its analysis. This architecture acknowledges the diversity in the domain and supports data interoperability by sharing information models. Their compliance to common metamodels guarantees that primary data and analysis results can be interpreted outside of the immediate production context. This is a fundamental necessity for all aspects of the evidence creation process. A metamodel of data capture forms is developed with unique properties to support data collection and documentation in this architecture. The architecture's potential to support complex analysis processes is demonstrated with the help of a novel metamodel for Markov model based simulations, as used for the synthesis of evidence in health-economic assessments. The application of both metamodels is illustrated on the example of HT. Since the biological effect of particle radiation is a major source of uncertainty in HT, in its second part, this thesis undertakes first investigations towards a new research facility for bio-medical experiments with ion beams. It examines the feasibility of upgrading LEIR, an existing accelerator at the European Organisation for Nuclear Research (CERN), with a new slow extraction and investigates transport of the extracted beam to future experiments. Possible configurations for the slow-resonant extraction process are identified, and designs for horizontal and vertical beam transport lines developed. The results of these studies indicate future research directions towards a new ion beam facility for biomedical research.

This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.