Using model-driven engineering to support autonomic computing

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Software complexity has increased dramatically and it still keeps increasing. As a result, the cost of developing and maintaining software systems has also increased significantly. Autonomic computing (AC) is aimed at building systems that have the ability to self-configure, self-manage, self-protect, self-heal, and self-optimize (self-* properties) to reduce human errors, system down time, and improve system efficiency. However, development of autonomic systems is complicated. Model-driven engineering (MDE) applies systematic use of models as primary software artifacts during the development life cycle. High-level models can be transformed into more detailed models and possibly the eventual generation of source code. MDE technologies have the potential to reduce the cost of building and validating autonomic systems. In other words, autonomic systems can be described using various models (e.g., software model, policy model, quality-of-service (QoS) model, and performance model) which can be validated with tools against the requirements and can be utilized at other lifecycle phases. However, it is often difficult to automate or revise the behavioral model. In addition, most existing MDE approaches for AC rely on explicitly known states, which may be impractical for some applications. The main objectives of the proposed research is to (1) extend our existing generative approach to support self-* properties to better understand self-adaptation and its associated cost in performance and QoS; (2) evaluate existing autonomic architectural models and extend architecture analysis methods for autonomic computing; (3) develop decision making strategies to support when, what, and how to adapt in dynamically changing environments; and (4) develop model notations, transformations, and management technologies to facilitate MDE for AC. AC has been advocated to address system complexity which has become a crucial issue in software development and maintenance. The proposed project offer potential solutions to improve MDE technologies for AC which in turn could help to reduce the development and maintenance cost of autonomic systems.