A framework for developing adaptive service compositions

Abstract

This thesis proposes a framework for automatic generation of self-healing service composition which can recover from functional and non-functional failures. To this end, it firstly proposes an automated method for generation of service composition which enables a user to build a service composition by selecting a set of desired features and secondly it proposes a method for adapting the generated service composition to recover autonomously from service failures or non-functional constraint violations. The proposed service composition method uses software product line engineering concepts to build a repository of features and link them to their corresponding services. Using this repository, it uses AI planning to build a work flow of service interactions based on the requirements. It then uses concepts from partial-order-planning to optimize the generated work flow. Eventually, the generated work flow is converted to structured and executable BPEL code. The proposed adaptation method extends the composition software product line to become a dynamic software product line. The proposed dynamic software product line is capable of re-selecting features of a running service composition to continue service with limited features to recover from a service failure or a violation of critical non-functional requirements. A method has been proposed which uses linear regression to determine the effect of features on the non-functional properties of service composition. Knowing how each feature affects non-functional requirements, a method has been proposed which reduces the problem of finding an alternate set of features which recovers service composition from service failure or non-functional requirement violation to a pseudo-boolean optimization problem, which can then be solved. An online tool-suite realizing the proposed framework has been implemented and the usability, effectiveness, and reliability of the proposed framework have been evaluated with extensive experiments.