R-CASS: Using Algorithm Selection for Self-Adaptive Service Oriented Systems

In service composition, complex applications are built by combining web services to fulfill user Quality of Service (QoS) and business requirements. To meet these requirements, applications are composed by evaluating all possible web service combinations using search algorithms. These algorithms need to be accurate and inexpensive to evaluate a large number of possible service combinations and services' fluctuating QoS attributes while meeting the constraints of limited computational resources. Recent research has shown that different search algorithms can outperform others on specific instances of a problem domain, in terms of solution quality and computational resource usage. Problematically, current service composition approaches ignore this property, leading to inefficient compositions. To address these limitations, we propose a composition algorithm selection framework which selects an algorithm per composition task at runtime, R-CASS. Our evaluations demonstrate that R-CASS leads to more efficient compositions, reducing composition time by 55.1% and memory by 37.5%.