Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments.

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data pro-cessing by moving services closer to the edge-end. In this connection, a key issue is to determine when and where services should be deployed. Common service deployment strategies used to be static based on the rules defined at the design time. However, dynamically changing IoT environments bring about unexpected situations such as out -of-range stream fluctuation, where the static service deployment solutions are not efficient. In this paper, we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data. To effec-tively predict upcoming workloads, we combine the online machine learning methods with an online optimiza-tion algorithm for service deployment. A simulation-based evaluation demonstrates that, compared with those state-of-the art approaches, the approach proposed in this paper has a lower latency of stream processing.