Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems

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The increased adoption of service-oriented technologies and cloud computing creates new challenges for the adaptation and evolution of long-living software systems. Software services and cloud platforms are owned and maintained by independent parties. Software engineers and system operators of long-living software systems only have limited visibility and control over those third-party elements. Traditional monitoring provides software engineers and system operators with execution observation data which are used as basis to detect anomalies. If the services and the cloud platform are not owned and controlled by the engineers of the software systems, monitoring the execution of the software system is not straightforward.The aim of the iObserve project is to develop and validate advanced techniques which empower the system engineers to observe and detect anomalies of the execution of software systems they do not fully own and control. It will extend and integrate previous work on adaptive monitoring, online testing and benchmarking and will use models@runtime as means to adjust the observation and anomaly detection techniques during system operation. To demonstrate the feasibility and potential benefits gained and for providing feedback to guide the research, the results will be continuously evaluated using an established research benchmark (CoCoME) as well as an industry-driven open-source application (Eclipse Skalli) that runs on a cloud platform.