Seamless Integration of Cloud and Edge with a Service-Based Approach

Edge computing may improve the processing quality of big IoT stream data and reduce network operational cost by moving computation onto the edge. However, there are two challenges in integrating cloud and edge computing for big stream data. Firstly, edge equipment usually has very limited computing power as well as storage ability, and apparently cannot support all the processing of big and real-time stream data. A flexible division of such services between edge and cloud is needed. Secondly, edge-end collaboration continuously changes due to some intrinsic interaction of data stream. In this paper, we propose a service-based approach to seamlessly integrating cloud and edge equipment. Based on our service model, we split a cloud service into two parts running on cloud and edge respectively. Also, we propose a dynamic service scheduling mechanism based on the improved bipartite graphs. We can deploy a cloud service to the edge at the right time when a key node emerges. 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.