Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration

Abstract

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. To this end, we propose SemCloud, a semantics-enhanced cloud system, for tackling the challenges of data volume and more users. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results. This poster paper accompanies our full paper and focuses on Datalog rules with external machine learning functions for automated resource configuration, and provides additional discussion on formalism and implementation techniques.