Towards big services composition

Abstract

Recently, cloud computing has been combined with big data processing leading to a new model of services called big services. This model addresses the customers' complex requirements by reusing and aggregating existing services from various domains and delivery models, and from multiple cloud availability zones. Existing web/cloud service composition approaches are not adequate for the big service context due to many reasons, including the large volume of data, the cross-domain and cross-cloud interoperability issues, etc. Considering the aforementioned facts, we provide a solution to the big service composition issue, by taking advantage of relational concept analysis (RCA), as a clustering method, and composite particle swarm optimisation (CPSO), as an optimisation technique. RCA is used to model the big service environment, whereas CPSO helps continuously optimising the quality of big service composition. The implementation and experimental studies on our approach have proven its feasibility and efficiency.