Centralized business process execution engines are not adequate to guarantee smooth process execution in the presence of multiple, concurrent, long-running process instances exchanging voluminous data. In the centralized architecture of most BPEL engine solutions, the execution of BPEL processes is performed in a closed runtime environment where process instances are isolated from each other, as well as from any other potential sources of information. This prevents processes from finding relative data at runtime to adapt their behavior in a dynamic manner. The goal of this chapter is to present a solution for the performance improvement of BPEL engines by using a distributed architecture that enables the scalable execution of service-oriented processes, while also supporting their data-driven adaptation. The authors propose a decentralized BPEL engine architecture using a hypercube peer-to-peer topology with data-driven adaptation capabilities that incorporates Artificial Intelligence (AI) planning and context-aware computing techniques to support the discovery of process execution paths at deployment time and improve the overall throughput of the execution infrastructure. The proposed solution is part of the runtime infrastructure that was developed for the environmental science industry to support the efficient execution and monitoring of service-oriented environmental science models.
