Throughout Europe, the introduction of Solvency II is forcing companies in the life assurance and pensions provision markets to change how they estimate their liabilities. Historically, each solvency assessment required that the estimation of liabilities was performed once, using actuaries' views of economic and demographic trends. Solvency II requires that each assessment of solvency implies a 1-in-200 chance of not being able to meet the liabilities. The underlying stochastic nature of these requirements has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. Currently, practitioners within UK pension provision companies consider the calculations required to meet new regulations to be outside the realms of anything which is achievable. This project brings the calculations within reach: this thesis shows that it is possible to perform the required calculations in manageable time scales, using entirely reasonable quantities of hardware. This is achieved through the use of several techniques: firstly, a new algorithm has been developed which reduces the computational complexity of the reserving algorithm from O(T2) to O(T) for T projection steps, and is sufficiently general to be applicable to a wide range of non unit-linked policies; secondly, efficient ab-initio code, which may be tuned to optimise its performance on many current architectures, has been written; thirdly, approximations which do not change the result by a significant amount have been introduced; and, finally, high performance computers have been used to run the code. This project demonstrates that the calculations can be completed in under three minutes when using 12,000 cores of a supercomputer, or in under eight hours when using 80 cores of a moderately sized cluster.

Mobile devices are often expected to perform computational tasks that may be beyond their processing or battery capability. Cloud computing techniques have been proposed as a means to offload a mobile device's computation to more powerful resources. In this thesis, we consider the case where powerful computing resources are made available by utilizing vehicles. These vehicles can be repositioned in real time to receive computational tasks from user-carried devices. They can be either equipped with rugged high-performance computers to provide both computation and communication service, or they can be simple message ferries that facilitate communication with a more powerful computing resource. These scenarios find application in challenged environments and may be used in a military or disaster relief settings. It is further enabled by increasing feasibility of (i) constructing a Mobile High Performance Computer (MHPC) using rugged computer hardware with form factors that can be deployed in vehicles and (ii) Message Ferries (MF) that provide communication service in disruption tolerant networks. By analogy to prior work on message ferries and data mules, one can refer to the use of our first schema, MHPCs, as computational ferrying. After illustrating and motivating the computational ferrying concept, we turn our attention into the challenges facing such a deployment. These include the well known challenges of operating an opportunistic and intermittently connected network using message ferries -- such as devising an efficient mobility plan for MHPCs and developing techniques for proximity awareness. In this thesis, first we propose an architecture for the system components to be deployed on the mobile devices and the MHPCs. We then focus on defining and solving the MHPC movement scheduling problem with sufficient generality to describe a number of plausible deployment scenarios. After thorough examination of the MHPC concepts, we propose a scheme in which MHPCs are downgraded to be simple MFs that instead provide communication to a stationary HPC with powerful computing resources. Similar to the MPHCs, we provide a framework for this problem and then describe heuristics to solve it. We conduct a number of experiments that provide an understanding of how the performance of the system using MHPCs or MFs is affected by various parameters. We also provide a thorough comparison of the system in the dimensions of Computation on the Move and Controlling the Mobility.