A Scalable Method of E-Service Workflow Emergence Based on the Bio-Network

An emergent modeling approach is proposed to the study of E-service workflow, which is inspired by the characteristics of emergence and self-evolution in biological neuroendocrine-immune network. E-services are represented by autonomous bio-entities (mobile agents with biological operation), The E-service workflow composition is achieved by the evolutionary community of the bio-entities. By understanding properties of messages, the bio-entities use projection-join closure to capture such message situations. Based on the negotiation of the bio-entities, the optimization of the E-service workflow composition is translated into dynamic services selection with QoS global optimization. We propose an improved algorithm based on quick sort and NSGA-II to resolve it. The simulation results show that the approach can significantly improve the E-service workflow performance. It adapts well to the changes of dynamic environments.