Business-Process-Driven Service Composition in a Hybrid Cloud Environment

Cloud services have been widely used to support tasks in business processes. A variety of services with differing types, brands, and quality of service (QoS) characteristics are available from various vendors. Additionally, companies also build their own private clouds to meet specific business requirements related to performance, privacy, and security. The problem of selecting and assembling appropriate services to support an organization’s multiple related business processes is very challenging. This problem also differs from traditional product/service selection problems because of the presence of business processes with non-sequential tasks and multiple, related business processes. The various QoS characteristics of services, the special requirements of some subtasks in the business processes, compatibility between cloud services, and the coordination of multiple business processes need to be considered when selecting appropriate services. This paper develops a multi-factor cloud service composition optimal selection (CSCOS) model to formalize the constrained combinatorial optimization problem and designs an improved differential evolution algorithm based on a constructive cooperative coevolutionary framework (C3IMDE) for solution. Experiments on synthetic data demonstrate that C3IMDE has better efficiency and stability than benchmark algorithms, especially for large-scale, multi-process collaborative optimization.