Interactive and Reliable Graph Processing via the Edge-Cloud Collaboration Framework
Jun Zhou, Masaaki Kondo · 2022
Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide the capacity constrained portable terminals with more processing capacity to improve the performance and quality of service (QoS) in several typical domains of the human activity in smart society, such as social networks, medical diagnosis, transportation, Internet of Things (IoT), etc. These domains often handle a vast collection of entities with various relationships which can be naturally represented by graph data structures. Graph processing is a powerful tool to model and optimize the complex problems where the graph-based data is involved. In view of the relatively insufficient resource provision of the edge devices, for the first time to our knowledge, we propose a reliable edge-cloud collaboration framework facilitating the shortest path search (SPS) operations based on a lightweight interactive graph processing library. One practical case is presented as well to show the typical application scenarios of our graph processing strategy. Experimental evaluation is conducted to indicate that the proposed strategy is able to efficiently implement the relevant application tasks with considering user-friendliness, low-latency response, collaborations between edge and cloud, interactions among edge devices, and privacy protection at an acceptable overhead.