Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud, and then offered as a service. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. To alleviate such a process, we introduce an approach that uses planning to automatically and dynamically compose applications ready for Cloud deployment. The industry may benefit from using automated planning in terms of support for product variability, sophisticated search in large spaces, fault tolerance, near-optimal deployment plans, etc. Our approach is based on Hierarchical Task Network (HTN) planning as it supports rich domain knowledge, component modularity, hierarchical representation of causality, and speed of computation. We describe a deployment using a formal component model for the Cloud, and we propose a way to define and solve an HTN planning problem from the deployment one. We employ an existing HTN planner to experimentally evaluate the feasibility of our approach.

It has been shown that up to 64 percent of personal computers in office buildings are left running during after-hours. Enabling power management options such as sleep mode is a straightforward method to reduce the energy consumption of computers. However, choosing the right timeout can be challenging. A sleep timeout which is too low leads to discomfort, whereas a timeout which is too high results in poor energy saving efficiency. Having the users choose their own sleep timeout is not viable as research shows that most users disable the sleep timeout completely, or choose a suboptimal timeout. Unlike existing context based power management systems which use predefined rules, we propose a solution which can determine a personalized sleep timeout for any point in time solely based on the users behaviour. We propose multiple models which have the goal of maximizing the energy savings while minimizing discomfort. The models are tested on the computers of employees of the University of Groningen over several weeks. We analyse the results of the experiments and determine which model performs best. We can potentially save between 4.02 and 17.17 kWh per computer per year, depending on the model that used.

. Since its emergence, one of the most advertised opportunities offered by service oriented computing has been the possibility of composing loosely coupled services on a per need basis. Services, like Lego pieces, act as modular building blocks which are assembled when a given articulated user request comes and are ready to be reused for other requests. Over the years, the promise has been of reducing recoding and refactoring efforts while achieving scalability, run-time adaptability, and in fi nite reuse. After reviewing 12 years of personal experiences and research in dynamic service composition, going from initial work on composing trips based on a number of independent travel service operations to the more recent research in home and building automation where services often represent interconnected things in a de fi ned physical space, I will introduce our current efforts in building dynamic service composition frameworks. In particular, I will present the RuG-planner which is able to defer composition decisions to run-time and to seamlessly make revisions in response to a constantly evolving execution environments.

Sustainability and energy-efficiency are receiving increasing attention. Existing buildings are responsible for more than 40% of the worlds total primary energy consumption. Current building management systems fail to reduce unnecessary energy consumption and preserve to user comfort at the same time mainly because they are unable to cope with dynamical changes caused by user’s interaction with the environment. To cope with this dynamicity, we propose a software architecture for energy smart buildings that includes a set of concrete software solutions that tackle energy consumption sub-systems; i.e., heating, ventilation and air conditioning (HVAC), lighting, workstations and other appliances subsystems, in order to save significant amount of energy whilst preserving user comfort. Experimental results carried out in a 12.000 square meter building of the University of Groningen show that our proposed solutions are able to save up to 56% of electricity used for lighting, at least 20% of electricity used for heating while the savings from controlling workstation as well as other appliances are 33% and 10%, respectively.