CROSS-LAYER ADAPTATION OF SERVICE-BASED SYSTEMS

One of the key features of service-based systems (SBS) is the capability to adapt in order to react to various changes in the business requirements and the application context. Given the complex layered structure, and the heterogeneous and dynamic execution context of such systems, adaptation is not at all a trivial task. The importance of the problem of adaptation has been widely recognized in the community of software services and systems. There exist several adaptation approaches which aim at identifying and solving problems that occur in one of the SBS layers. A fundamental problem with most of these works is their fragmentation and isolation. While these solutions are quite effective when the specific problem they try to solve is considered, they may be incompatible or even harmful when the whole system is taken into account. Enacting an adaptation in the system might result in triggering new problems. When building adaptive SBSs precautions must be taken to consider the impacts of the adaptations on the entire system. This can be achieved by properly coordinating adaptation actions provided by the different analysis and decision mechanisms through holistic and multi-layer adaptation strategies. In this dissertation, we address this problem. We present a novel framework for Crosslayer Adaptation Management (CLAM) that enables a comprehensive impact analysis by coordinating the adaptation and analysis tools available in the SBS. We define a new system modeling methodology for adaptation coordination. The SBS model and the accompanying adaptation model that we propose in this thesis overcome the limitations of the existing cross-layer adaptation approaches: (i) genericness for accommodating diverse SBS domains with different system elements and layers (ii) flexibility for allowing new system artifacts and adaptation tools (iii) capability for dealing with the complexity of the SBS considering the possibility of a huge number of problems and adaptations that might take place in the system. Based on this model we present a tree-based coordination algorithm. On the one hand it exploits the local adaptation and analysis facilities provided by the system, and on the other hand it harmonizes the different layers and system elements by properly coordinating the local solutions. The outcome of the algorithm is a set of alternative cross-layer adaptation strategies which are consistent with the overall system. Moreover, we propose novel selection criteria to rank the alternative strategies and select the best one. Differently from the traditional approaches, we consider as selection criteria not only the overall quality of the SBS, but also the total efforts required to enact an adaptation strategy. Based on these criteria we present two possible ranking methods, one relying on simple additive weighting - multiple criteria decision making, the other relying on fuzzy logic. The framework is implemented and integrated in a toolkit that allows for constructing and selecting the cross-layer adaptation strategies, and is evaluated on a set of case studies.