Online Learning Using Incomplete Execution Data for Self-Adaptive Service-Oriented Systems

Service composition algorithms support the construction of complex applications by combining various web services to fulfill diverse functional and Quality of Service (QoS) requirements. Moreover, composition algorithms must fulfill diverse user requirements while adhering to constraints such as limited computational resources. Recent research has demonstrated that using online learning to select different algorithms for specific tasks of a problem domain outperforms approaches that use a single algorithm for all tasks, in terms of computational resource usage and solution quality. Problematically, existing work in service composition does not leverage these advances, leading to multiple inefficient compositions. To address these challenges, we propose online composition algorithm selection using contextual multi-armed bandits to select an algorithm for each composition task at runtime. Our evaluations demonstrate the benefits of our approach by reducing time and memory usage by up to 54.2% and 15.5% while fulfilling QoS requirements, compared to using a single composition algorithm for all tasks.