Auto-configuration System and Algorithms for Big Data-Enabled Internet-of-Things Platforms

Internet of Things (IoT) platforms that handle Big Data might perform poorly or not according to the goals of their operator (in terms of costs, database utilization, data quality, energy-efficiency, throughput) if they are not configured properly. The latter configuration refers mainly to system parameters of the data-collecting gateways, e.g., polling intervals, capture intervals, encryption schemes, used protocols etc. However, re-configuring the platform appropriately upon changes of the system context or the operator targets is currently not taking place. This happens because of the complexity or unawareness of the synergies between system configurations and various aspects of the Big Data-handling IoT platform, but also because of the human resources that an efficient re-configuration would require. This paper presents an auto-configuration solution based on interpretable configuration suggestions, focusing on the algorithms for computing the mentioned suggested configurations. Five such algorithms are contributed, while a thorough evaluation reveals which of these algorithms should be used in different operation scenarios in order to achieve high fulfillment of the operator´s targets.