A Reference Architecture and Modelling Principles for Architectural Stability based on Self-Awareness: Case of Cloud Architectures

TL;DR

This paper presents a reference architecture and modeling principles based on self-awareness to enhance the stability of cloud architectures during runtime, supporting continuous operation and quality maintenance.

Contribution

It introduces a novel self-awareness-based reference architecture and modeling approach specifically designed to improve architectural stability in cloud systems.

Findings

Increases efficiency of maintaining stable architecture behavior during runtime.

Supports continuous adaptation without architecture drifting.

Applicable to cloud architectures for long-lived software systems.

Abstract

With the increased dependence on software, there is a pressing need for engineering long-lived software. As architectures have a profound effect on the life-span of the software and the provisioned quality of service, stable architectures are significant assets. Architectural stability tends to reflect the success of the system in supporting continuous changes without phasing-out. The \textit{behavioural} aspect of stability is essential for seamless operation, to continuously keep the provision of quality requirements stable and prevent architecture's drifting and phasing-out. In this paper, we introduce a reference architecture and model for stability. Specifically, we leverage on the self-awareness principles and runtime goals modelling to explicitly support architectural stability. To illustrate the applicability and evaluate the proposed approach, we consider the case of cloud architectures. The experimental results show that our approach increases the efficiency of the architecture in keeping the expected behaviour stable during runtime operation.