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Although several techniques have been proposed towards monitoring and adaptation of Service-Based Applications (SBAs), few of them deal with cross-layer issues. This paper proposes a framework, able to monitor and adapt SBAs across all functional layers. This is achieved by using techniques, such as event monitoring and logging, event-pattern detection, and mapping between event patterns and appropriate adaptation strategies. In addition, a taxonomy of adaptation-related events and a meta-model describing the dependencies among the SBA layers are introduced in order to "capture" the cross-layer dimension of the framework. Finally, a specific case study is used to illustrate its functionality.

The adoption of Cloud computing in the Service Oriented Architecture (SOA) world is continuously increasing. However, as developers try to optimize their application deployment cost and performance, they may also deploy application parts redundantly on different VMs. In such heterogeneous and distributed environments, it is important to have a clear view of the system's state and its components' interrelationships. This paper aims at proposing a novel monitoring and adaptation framework for Service-based Applications (SBAs) deployed on multiple Clouds. The main functionality of this framework is the discovery of critical event patterns within monitoring event streams, leading to specific Service Level Objective (SLO) violations. Furthermore, two main meta-models are proposed for describing the SBA's components and their dependencies, and the supported adaptation actions in a specific context respectively. The proposed approach is empirically evaluated based on a real-world traffic management application.