Fragmentation and Optimal Deployment for IoT-Aware Business Process

Abstract

Modern Internet of Things (IoT)-aware business processes consist of various geographically dispersed sensor devices. Large amounts of raw data acquired from sensors need to be regularly transmitted to the targeted processes in enterprise data centers, which results in a significant increase in network traffic and latency. It is necessary to execute such processes in a distributed way. A major challenge for distributed business processes is to design an optimal fragmentation and deployment scheme to improve the overall performance of the process. To tackle this challenge, we propose a novel location-based fragmentation algorithm to partition a process, and apply the Kuhn-Munkres algorithm to solve the optimal deployment of process fragments. These distributed fragments can collaborate together to complete a common goal by using a topic-based publish/subscribe infrastructure. This approach can reduce network traffic and save the process execution time. In our experiment, an integrated monitoring process is used to illustrate the effectiveness of the proposed solution. The results show that the performances of distributed execution outperform the centralized one.