Making the Connection: How Provider Dialogue and Network Clusters Can Spur Successful Collaboration

In 2005, the Forbes Funds commissioned a report called Service Clustering: Building Cohesive Public Service Capacity that described collaboration as a way to achieve greater efficiency through shared back-office or non-mission critical functions without reducing consumer choice. The researchers argued that collaboration could best be induced by focusing on providers that are geographically close together and that provide an overlapping set of services. According to the report, "It is easier to share, communicate, and collaborate with your neighbor than with an organization separated by distance." Though this idea is compelling, it has become clear in the years since the 2005 report that the identification of geographic clusters is not sufficient to inspire a host of new collaborations. Formal collaboration, the kind suggested in the past report and the focus of this work, remains a relatively rare phenomenon. Convinced that collaboration continues to promise greater efficiency and effectiveness when successfully implemented, The Forbes Funds revisited the topic this year, hoping to gain further insight into the factors that make collaboration successful and to identify additional clusters of providers that could provide the greatest potential for collaboration

Transmission parameters of the 2001 foot and mouth epidemic in Great Britain.

Despite intensive ongoing research, key aspects of the spatial-temporal evolution of the 2001 foot and mouth disease (FMD) epidemic in Great Britain (GB) remain unexplained. Here we develop a Markov Chain Monte Carlo (MCMC) method for estimating epidemiological parameters of the 2001 outbreak for a range of simple transmission models. We make the simplifying assumption that infectious farms were completely observed in 2001, equivalent to assuming that farms that were proactively culled but not diagnosed with FMD were not infectious, even if some were infected. We estimate how transmission parameters varied through time, highlighting the impact of the control measures on the progression of the epidemic. We demonstrate statistically significant evidence for assortative contact patterns between animals of the same species. Predictive risk maps of the transmission potential in different geographic areas of GB are presented for the fitted models

Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues

In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal characteristics. By adapting the batch training of standard HMMs to incremental learning, online HMMs act as benchmarks on workloads obtained from live systems (i.e. storage systems and financial markets) and reduce time complexity of the Baum-Welch algorithm. Similarly, by extending HMM capabilities to train on multiple traces simultaneously it follows that workloads of different types are modelled in parallel by a multi-input HMM. Typically, the HMM-generated traces verify the throughput and burstiness of the real data. Applications of adaptive HMMs include predicting user behaviour in social networks and performance-energy measurements in smartphone applications. Equally important is measuring system delay through response times. For example, workloads such as Internet traffic arriving at routers are affected by queueing delays. To meet quality of service needs, queueing delays must be minimised and, hence, it is important to model and predict such queueing delays in an efficient and cost-effective manner. Therefore, we propose a class of discrete, processor-sharing queues for approximating queueing delay as response time distributions, which represent service level agreements at specific spatiotemporal levels. We adapt discrete queues to model job arrivals with distributions given by a Markov-modulated Poisson process (MMPP) and served under discriminatory processor-sharing scheduling. Further, we propose a dynamic strategy of service allocation to minimise delays in UDP traffic flows whilst maximising a utility function.Open Acces