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.

Today's buildings are responsible for more than 40% of the world's total energy consumption. Current systems that manage equipment in buildings fail to reduce unnecessary energy consumption while at the same time maintaining the comfort of those using the buildings. This is usually because the existing systems cannot cope with the changes caused by interactions between people and the building environment. Furthermore, people using buildings are not sufficiently aware of how much buildings consume and of what concrete actions could help to reduce this consumption. Moreover, current building management systems do not take into account feedback from building users and their preferences regarding the conditions in their working environment. We designed and implemented a smart energy system to overcome these gaps. Our system took into account the behavior of building users so as to provide automated control of energy consumption and other processes within an actual building. With this system we also provided user dashboards to serve as a means of communication between the building and its users. In addition to reducing energy consumption we also introduced related optimizations such as reduction of water consumption and improvement of waste management, using the same system principles. We installed a prototype of this system in a modern university building, the Bernoulliborg, to show how such a system is realizable in actual working office space. This building served as the evaluation platform for our research. Our results show that the environmental savings achieved by the realized solutions range from 9.5% for PC sleep mode control to 80% for automated lighting systems control. Monetary savings indicate that payback time falls within a range of about 2 years for water consumption reduction to 7.8 years for automated lighting control. Finally, user acceptance evaluations show that the majority of participants find the system both highly acceptable and useful.

The selection of the best fitting process engine for a specific project requires the evaluation of engines according to various requirements. We focus on the non-functional requirement robustness, which is critical in production environments but hard to determine. Thus, we propose an evaluation framework to reveal important robustness criteria of process engines. In this work, we focus on message robustness, i.e., The ability to handle the receipt of invalid messages appropriately. In a case study comprising five open source BPEL engines, we determine message robustness by injecting faults into robustly designed processes as a reply to a previously sent request from an external virtual service and assert their behavior. The results show that the degree of message robustness significantly differs, hence, robustly designed processes do not necessarily lead to robust runtime behavior, the selected engines still play a major role.

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.

Key features: computer-based control of the heating system of buildings that have still manual control of radiators. The solution optimizes the heating of rooms based on fusion of information regarding users presence, external weather conditions, weather forecast, and university/user calendars. The goal is to control the room heating automatically and heat only when users are present, thus, optimizing the temperature given the weather exposition and the actual number of occupants present in the room. Benefits: energy savings, economic saving, improved comfort and well-being for students/university personnel. Savings: 15% of energy for heating. Target building for implementation: Heymansgebouw, RuG Budget: 100.000 euro. Return on investment in 10 years: 165.000 euro. Payback period: 6 years.