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With the increased dependence on software, there is a pressing need forengineering long-lived software. As architectures have a profound effect on thelife-span of the software and the provisioned quality of service, stablearchitectures are significant assets. Architectural stability tends to reflectthe success of the system in supporting continuous changes without phasing-out.The \textit{behavioural} aspect of stability is essential for seamlessoperation, to continuously keep the provision of quality requirements stableand prevent architecture's drifting and phasing-out. In this paper, we presenta framework for reasoning about stability during runtime, leveraging onself-awareness principles. Specifically, we employ runtime goals for managingstability goals, online learning for reasoning about stability on the long-run,and stochastic games for managing associated trade-offs. We evaluate theproposed work using the case of cloud architectures for its highly dynamicsduring runtime. The experimental results have shown the efficiency ofself-awareness techniques in realising the expected behaviour stable duringruntime operation.

Architectural Stability Reasoning using Self-Awareness Principles: Case of Self-Adaptive Cloud Architectures