An optimal service selection approach for service-oriented business collaboration using crowd-based cooperative computing

Crowd-based cooperative computing (CBCC) emerges as a new computing paradigm, the core issue of which is the effective management and the coordinated use of crowd resources, including Internet users, application services, and smart devices. The service-oriented architecture (SOA) provides interoperability among crowd resources to support service-oriented business collaboration (SOBC). To address such a common issue of the coordinated use of crowd resources for SOBC, this paper studies a collaborative service computing model by considering the competition and cooperation among crowd resources. Then, a multi-objective optimization mathematical model is established for optimal service selection (OSS). Specifically, the methodology is resorted to an improved particle swarm optimization (IPSO) algorithm to find suitable collaborative services that optimally balance the quality of service (QoS) and synergy effect (SE). Furthermore, a flexible rescheduling strategy is presented for faulty services. The experimental results show that the proposed methodology is effective and feasible to obtain better-quality solutions for fulfilling the SOBC. •A crowd-cooperative approach for service-oriented business collaboration.•Collaborative service computing model based on crowd behaviors and relations.•An evaluation approach for synergy effect to support service composition.•Flexible rescheduling strategy for faulty services.•An improved particle swarm optimization algorithm for optimal service selection.