A GP approach to QoS-aware web service composition including conditional constraints

Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e. fully executable), (2) respect conditional constraints (e.g. user can specify logical branching), and (3) are optimised according to nonfunctional Quality of Service (QoS) measurements. This paper presents a genetic programming approach that addresses these three dimensions simultaneously through the fitness function, as well as through the enforcement of constraints to candidate trees during initialisation, mutation, and crossover. The approach is tested using an extended version of the WSC2008 datasets, and results show that fully functional and quality-optimised solutions can be created for all associated tasks, with an execution time that is roughly equivalent to that of a non-conditional approach.