Service Chain-driven Communication and Computing Integration Networking: A Case Study of Levee Piping Hazard Inspection via Remote Sensing

The deployment of artificial intelligence technology in various emerging network applications has spawned a large number of computing tasks, such as on-the-fly image rendering, which requires dynamic collaboration of multi-dimensional resources from the perspective of communication and computing to meet service requirements. In this paper, we introduce a communication and computation integrated network architecture, referred to as (Com)2INet, which has multi-dimensional, multi-layer and heterogeneous characteristics. The architecture integrates software defined network (SDN) and network function virtualization (NFV) technologies to ensure quality of service (QoS) and quality of experience (QoE). Customized advanced computing services can be implemented as service chains (SCs), which consist of ordered virtual network functions and can be scheduled across domains of end-edge-cloud, space-air-ground, and multiple data centers to facilitate ubiquitous network connectivity and collaborative computation. Moreover, we outline key technologies such as measurement and modeling of multi-dimensional heterogeneous resources, multi-path transmission, heterogeneous resource scheduling, and fault tolerance. Furthermore, an intelligent scheduling scheme with dynamic coordination of multi-dimensional resources is proposed for massive remote sensing images, in which an SC-based stepwise adaptive clustering method is utilize to make the optimal joint decision for levee piping hazard identification. Finally, we explore open issues that are underlie efficient collaborative computation in (Com)2INet.