Consumer-Centred Validation for Runtime Adaptation in Service-Oriented System

Ensuring that service-oriented systems can adapt quickly and effectively to changes in service quality, business needs and their runtime environment is an increasingly important research problem. However, while considerable research has focused on developing runtime adaptation frameworks for service-oriented systems, there has been markedly less work on assessing the effectiveness of recommended adaptations. One way to address the problem is through validation. Validation not only allows us to assess how well a recommended adaptation addresses the concerns for which the system is reconfigured, it provides us with insights into the nature of problems for which different adaptations are suited. However, the dynamic nature of runtime adaptation and the changeable contexts in which service-oriented systems operate make it difficult to specify appropriate validation mechanisms in advance. This paper describes a novel consumer-centred approach for supporting runtime validation in self-adapting service-oriented systems. Our proposed solution uses machine learning to continuously assess and refine adaptation decisions.