Iobserve windows
We present the Kieker framework for monitoring software runtime behavior, e.g., internal performance or (distributed) trace data. Furthermore, instrumentation should be non-intrusive to the business logic, as far as possible. In contrast to profiling for construction activities, monitoring of operational services should only impose a small performance overhead. A requirement for its robust operation are means for effective monitoring of software runtime behavior. In addition to studying the construction and evolution of software services, the software engineering discipline needs to address the operation of continuously running software services. The benefit of our approach will be to automatically keep the PM up-to-date throughout the development process which enables the proactive identification of upcoming performance problems and provides a foundation for evaluating design alternatives at low costs.
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Our work will combine static code analysis with adaptive, automatic, dynamic analysis covering updated parts of code to update the PM with parameters, like resource demands and branching probabilities. To address these problems, this paper envisions an approach for efficient continuous integration of a parametrised performance model in an agile development process.
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Moreover, keeping potential manual changes of the PM is another challenge as long the PM is regenerated from scratch every time. Thus, they are too costly to be applied frequently, up to after each code change.
However, these approaches require to monitor and analyse the whole application. Existing approaches automate the extraction of a PM based on reverse engineering and/or measurements techniques. Creating such a model manually is an expensive process that is unsuitable for agile software development aiming to produce rapid releases in short cycles.
We come up with a roadmap to include planning and execution activities in iObserve.Īpplying model-based performance prediction requires that an up-to-date Performance Model (PM) is available throughout the development process. Currently, iObserve covers the monitoring and analysis phases of the MAPE control loop. As an umbrella a megamodel integrates design-time models, code generation, monitoring, and run-time model update.
A correspondence model maintains the semantic relationships between monitoring outcomes and architecture models. The run-time model builds upon a technology-independent monitoring approach. Central to this perception is an architectural run-time model that is usable for automatized adaptation and is simultaneously comprehensible for humans during evolution. In this vision paper, we present the iObserve approach to target aforementioned challenges while considering operationlevel adaptation and development-level evolution as two mutual interwoven processes. However, typical run-time models are close to an implementation level of abstraction which impedes understandability for humans. Run-time models are kept insync with the underlying system. During operation the systems often drifts away from its design-time models. Models are useful for involving humans and conducting analysis, e.g. While previous research focused on automated adaptation, increased complexity and heterogeneity of cloud services as well as their limited observability, makes evident that we need to allow operators (humans) to engage in the adaptation process. Yet at the same time, it leads to major challenges like limited control of third party infrastructures and runtime changes which mostly cannot be foreseen during development. Building software systems by composing third-party cloud services promises many benefits such as flexibility and scalability.