Entwicklung eines Hybriden Planers zur verhaltensorientierten Selbstoptimierung - Borkowski, Richard

Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition - Jungmann, Alexander; Kleinjohann, Bernd (Proceedings of the 9th IEEE International Conference on Service Computing (SCC))
A major goal of the On-The-Fly Computing project is the automated composition of individual services based on services that are available in dynamic markets. Dependent on the granularity of a market, different alternatives that satisfy the requested functional requirements may emerge. In order to select the best solution, services are usually selected with respect to their quality in terms of inherent non-functional properties. In this paper, we describe our idea of how to model this service selection process as a Markov Decision Process, which we in turn intend to solve by means of Reinforcement Learning techniques in order to control the underlying service composition process. In addition, some initial issues with respect to our approach are addressed.

Towards an Integrated Service Rating and Ranking Methodology for Quality Based Service Selection in Automatic Service Composition - Jungmann, Alexander; Kleinjohann, Bernd (Proceedings of the 4th International Conferences on Advanced Service Computing (SERVICE COMPUTATION))
The paradigm shift from purchasing monolithic software solutions to a dynamic composition of individual solutions entails many new possibilities yet great challenges, too. In order to satisfy user requirements, complex services have to be automatically composed of elementary services. Multiple possibilities of composing a complex service inevitably emerge. The problem of selecting the most appropriate services has to be solved by comparing the different service candidates with respect to their quality in terms of inherent non-functional properties while simultaneously taking the user requirements into account. We are aiming for an integrated service rating and ranking methodology in order to support the automation of the underlying decision-making process. The main contribution of this paper is a ﬁrst decomposition of the quality-based service selection process, while emphasizing major issues and challenges, which we are addressing in the On-The-Fly Computing project.

Efficient Color-Based Image Segmentation and Feature Classification for Image Processing in Embedded Systems - Jungmann, Alexander; Kleinjohann, Bernd; Kleinjohann, Elisabeth; Bieshaar, Maarten (Proceedings of the Fourth International Conference on Resource Intensive Applications and Services (INTENSIVE))
In this paper, a color based feature extraction and classification approach for image processing in embedded systems in presented. The algorithms and data structures developed for this approach pay particular attention to reduce memory consumption and computation power of the entire image processing, since embedded systems usually impose strong restrictions regarding those resources. The feature extraction is realized in terms of an image segmentation algorithm. The criteria of homogeneity for merging pixels and regions is provided by the color classification mechanism, which incorporates appropriate methods for defining, representing and accessing subspaces in the working color space. By doing so, pixels and regions with color values that belong to the same color class can be merged. Furthermore, pixels with redundant color values that do not belong to any pre-defined color class can be completely discarded in order to minimize computational effort. Subsequently, the extracted regions are converted to a more convenient feature representation in terms of statistical moments up to and including second order. For evaluation, the whole image processing approach is applied to a mobile representative of embedded systems within the scope of a simple real-world scenario.