Abstract:

Edge computing can improve the processing quality of big IoT stream data and reduce network operating cost by moving computation onto edge devices. However, there are two challenges in integrating cloud and edge computing for big stream data. Firstly, edge devices usually have very limited computing and storage capabilities, and apparently cannot support real-time processing of big stream data. Secondly, the unpredictability of stream data leads to constant changes in edge-side collaboration. Therefore, it is necessary to achieve a flexible division between edge services and cloud services. We propose a servicebased approach to seamlessly integrating cloud and edge devices to realize the collaboration of large-scale stream data cloud computing and edge computing. This approach divides the cloud service into two parts running on cloud and edge respectively. At the same time, we propose a dynamic service scheduling mechanism based on the improved bipartite graphs. During event generation, we can deploy cloud service on the edge node at appropriate time. The effectiveness of the proposed approach is demonstrated by examining real cases of China's State Power Grid. Experimental results verify the effectiveness and efficiency of our approach.

Key words: edge computing, cloud computing, seamless integration, proactive data service, dynamic scheduling