Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems.

Home, family and society: women of the Irish landed class, 1860-1914. A Munster case study

The thesis analyses the roles and experiences of female members of the Irish landed class (wives, sisters and daughters of gentry and aristocratic landlords with estates over 1,000 acres) using primary personal material generated by twelve sample families over an important period of decline for the class, and growing rights for women. Notably, it analyses the experiences of relatively unknown married and unmarried women, something previously untried in Irish historiography. It demonstrates that women’s roles were more significant than has been assumed in the existing literature, and leads to a more rounded understanding of the entire class. Four chapters focus on themes which emerge from the sources used and which deal with their roles both inside and outside the home. These chapters argue that: Married and unmarried women were more closely bound to the priorities of their class than their sex, and prioritised male-centred values of family and estate. Male and female duties on the property overlapped, as marriage relationships were more equal than the legislation of the time would suggest. London was the cultural centre for this class. Due to close familial links with Britain (60% of sample daughters married English men) their self-perception was British or English, as well as Irish. With the self-confidence of their class, these women enjoyed cultural and political activities and movements outside the home (sport, travel, fashion, art, writing, philanthropy, (anti-)suffrage, and politics). Far from being pawns in arranged marriages, women were deeply conscious of their marriage decisions and chose socially, financially and personally compatible husbands; they also looked for sexual satisfaction. Childbirth sometimes caused lasting health problems, but pregnancy did not confine wealthy women to an invalid state. In opposition to the stereotypical distant aristocratic mother, these women breastfed their children, and were involved mothers. However, motherhood was not permitted to impinge on the more pressing role of wife

The cloud personal assistant for providing services to mobile clients

This paper introduces the original concept of a cloud personal assistant, a cloud service that manages the access of mobile clients to cloud services. The cloud personal assistant works in the cloud on behalf of its owner: it discovers services, invokes them, stores the results and history, and delivers the results to the mobile user immediately or when the user requests them. Preliminary experimental results that demonstrate the concept are included.

Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user.

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.