Model-Driven Architectural Monitoring and Adaptation for Autonomic Systems

TL;DR

This paper presents a model-driven approach for architectural monitoring and adaptation in autonomic systems, enabling more efficient and concurrent self-management by synchronizing models with running systems.

Contribution

It introduces a novel use of meta models and model transformation techniques to facilitate incremental and concurrent synchronization between system models and actual systems.

Findings

Enables incremental synchronization of models and systems.

Supports concurrent self-management activities.

Reduces complexity and cost of architectural monitoring.

Abstract

Architectural monitoring and adaptation allows self-management capabilities of autonomic systems to realize more powerful adaptation steps, which observe and adjust not only parameters but also the software architecture. However, monitoring as well as adaptation of the architecture of a running system in addition to the parameters are considerably more complex and only rather limited and costly solutions are available today. In this paper we propose a model-driven approach to ease the development of architectural monitoring and adaptation for autonomic systems. Using meta models and model transformation techniques, we were able to realize an incremental synchronization between the run-time system and models for different self-management activities. The synchronization might be triggered when needed and therefore the activities can operate concurrently.