Summary The advent of hybrid cloud technologies and public Infrastructures as a Service (IaaS) allows service developers to offer services to their customers with little upfront investment and to adapt services to different workload sizes. The problem of minimizing the costs of the hired public infrastructure while providing the quality of service needed by the final customer arises when using hybrid clouds. Several scheduling strategies have been proposed to solve this problem for services dealing with deadline‐constrained bag‐of‐tasks workloads. Most of these solutions do not consider the variable performance of the clouds, the provisioning delay of virtual machine instances that affects the elasticity, and the impracticality of having good processing time estimations in real systems. We propose a scheduler algorithm that overcomes previous limitations and can minimize the cost of the infrastructure while maximizing the number of deadlines met by the service. Our solution can work autonomously by using sampled observations of the processing times and considers the heterogeneity and the provisioning time of the virtual machine instances. An evaluation was conducted by simulating different scenarios and workload types. Simulation results show that our solution obtains better or similar results than previous techniques in most scenarios in terms of deadlines met.

The ubiquitous computing concept was introduced by Weiser in 1991. In spite of the advances achieved since then, the use of ubiquitous spaces is not yet widespread in home environments, due to the difficulty in using context information and artificial intelligence algorithms. This work combines ontologies and machine learning techniques to infer environmental user preferences automatically. These preferences will allow the development of context-aware applications for the home environment that will adjust it proactively, depending on user preferences. These kinds of applications will contribute to the development of the ambient-assisted living concept and therefore to the improvement of the quality of life.

Nowadays, enterprises providing services through Internet often require online services supplied by other enterprises. This entails the cooperation of enterprise servers using Web services technology. The service exchange between enterprises must be carried out with a determined level of quality, which is usually established in a service level agreement (SLA). However, the fulfilment of SLAs is not an easy task and requires equipping the servers with special control mechanisms which control the quality of the services supplied. The first contribution of this research work is the analysis and definition of the main requirements that these control mechanisms must fulfil. The second contribution is the design of a control mechanism which fulfils these requirements and overcomes numerous deficiencies posed by previous mechanisms. The designed mechanism provides differentiation between distinct categories of service consumers as well as protection against server overloads. Furthermore, it scales in a cluster and does not require any modification to the system software of the host server, or to its application logic.

The exact stochastic analysis of most real-time systems is becoming unaffordable in current practice. On one side, the exact calculation of the response time distribution of the tasks is not possible except for simple periodic and independent task sets. On the other side, in practice, tasks introduce complexities like release jitter, blocking in shared resources, stochastic dependencies, etc, which can not be handled by the periodic and independent task set model. This paper introduces the concept of pessimism in the stochastic analysis of real-time systems in the following sense: the exact probability of missing any deadline is always lower than that derived from the pessimistic analysis. Therefore, if real-time constraints are expressed as probabilities of missing deadlines, the pessimistic stochastic analysis provides safe results. Some applications of the pessimism concept are presented. Firstly, the practical problems that arise in the stochastic analysis of periodic and independent task sets are addressed. Secondly, we extend to the stochastic case some well known techniques of the deterministic analysis, such as the blocking in shared resources, and the task priority assignment.