iObserve is an approach to cloud-based system adaptation and evolution through run-time observation and continuous quality analysis. With iObserve, run-time adaptation and evolution are two mutual, interwoven activities that influence each other. Central to iObserve is (a) the specification of the correspondence between observation results and design models, and (b) their use in both adaptation and evolution. Run-time
observation data is promoted to meaningful values mapped to design models, thereby continuously updating and calibrating those design models during run-time while keeping the models comprehendible by humans. This engineering approach allows for automated adaptation at run-time and simultaneously supports software evolution. Model-driven software engineering is employed for various purposes such as monitoring instrumentation and model transformation.
R. Heinrich, R. Jung, C. Zirkelbach, W. Hasselbring, R. Reussner; https://www.elsevier.com/books/software-architecture-for-big-data-and-the-cloud/mistrik/978-0-12-805467-3 An Architectural Model-Based Approach to Quality-aware DevOps in Cloud Applications. In: Software Architecture for Big Data and the Cloud, Elsevier, 2017. ISBN: 9780128054673.
R. Heinrich; http://dl.acm.org/citation.cfm?doid=2897356.2897359 Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications. ACM SIGMETRICS Performance Evaluation Review, 43(4):13-22, ACM, 2016.
Further publications can be found on the iObserve project web page.
Contact: Please email to Robert Heinrich for installation instructions.