Approach and Challenges for a Knowledge-optimized Cloud-oriented IoT Service Execution Platform

With the progressing Internet of Things connected devices trend, ever more live data streams are potentially used by cloud-based service instances. Yet also, cloud service provider customers demand ever more types and instances of such stream-intensive services to be deployed fast and flexibly. In this paper we propose knowledge-based service decomposition as a way to make large service collections optimally executable in a new architectural approach to world and system model-aware service platforms. We discuss the related research questions, design challenges and potential technology choices, to conclude on an experiments-based design validation outlook.