Motivation

We are witnessing the emergence of new classes of application that are highly complex, inevitably distributed, and operate in heterogeneous and rapidly changing environments. Examples of such applications include those from pervasive and Grid computing domains. These systems are required to be adaptable, flexible, reconfigurable and, increasingly, self-managing. 

Such characteristics make systems more prone to failure when executing and Thus the development and study of appropriate mechanisms for runtime Validation and monitoring is needed. In the model-driven software development area, research effort has focused primarily on using models at design, implementation, and deployment stages of development. This work has been highly productive with several techniques now entering the commercialisation phase. The use of model-driven techniques for validating and monitoring run-time behaviour can also yield significant benefits. A key benefit is that models can be used to provide a richer semantic base for run-time decision-making related to system adaptation and other run-time concerns. For example, one can use models to help determine when a system should move from a consistent architecture to another consistent architecture.

Model-based monitoring and management of executing systems can play a significant role as we move towards implementing the key self-* properties associated with autonomic computing.

Goal

The goal of this workshop is to look at issues related to developing appropriate model-driven approaches to managing and monitoring the execution of systems. We build on the previous events where we have succeeded in building a community and bringing about an initial exploration of the core ideas of Models@run.time and now seek:

- experiences with actual implementations of the concept

- rationalisation of the various concepts into overall architectural perspectives

- to make explicit the specific roles that models play at runtime

- impact on software engineering methodologies

- to continue to assemble a network of researchers in this emerging area, building on the results of the earlier editions.