## 2 Related Work
### Network Slicing and Resource Allocation  
Because of the novelty of NS, many challenges in aspects, e.g., wireless slicing, slice resource allocation with guaranteed extreme QoS and isolation in multi-domain networks remain to be addressed. In the context of wireless networks, resource allocation has captured much attention due to significant challenges in energy efficiency (EE) [31], bandwidth allocation, power control and so on. There are significant efforts in the network EE optimization under the QoS constraints and a transmit power budget [32; 33]. [PERSON] [34] tackles the resource allocation for the critical EE in 5G wireless networks, which considers the EE in small cells, massive multiple-input multiple-output (MIMO) [35; 36] and cell-free networks [37]. In order to dynamically allocate wireless resources [19]such as cell slices and virtual base stations, [PERSON] et al. [38] study bandwidth slicing and resource allocation problems for supporting the mixture of IoT and video streaming services. However, most of them do not consider the ultra-low latency services especially.  
On the other hand, lots of research introduces realization models for the management and orchestration of network functions and mapping of services. Since NS allows operators to customize networks according to various service demands, both industry and academia introduce many realization models of NS. [PERSON] et al. [14] illustrate the slice as an E2E traffic flow set, outline a model for Network Slice Design Problem (NSDP), and present two model extensions for traffic robustness and survivability requirements. However, the problem statement for NS has to encompass the characterization of the NS itself (i.e., including required VNUs and their interconnections). To this end, [PERSON] et al. [39] describe the slice deployment model between the slice request and substrate network and formulate the failure recovery problem. Although these two models give helpful studies in slice resource allocation, solution schemes still lack the ability to provide very high data rates (typically of Gbps order) and extremely low latency simultaneously. In multi-domain wireless communications networks, [PERSON] et al. [40] introduce a slice orchestration system and [PERSON] et al. [41] propose an isolated way for service-oriented deployment policy of E2E NS based on a coordinated virtual network embedding method. However, the algorithm is more suitable for evaluating the adaptability of substrate nodes according to service requirements, rather than directly determining mapping relationships. Furthermore, targeting to balance three typical slices with the ultimate goal (i.e., taking advantage of infrastructure resources efficiently), the works do not consider the transmission latency and propagation latency with real transmission distance, and ignore the higher priority of latency-sensitive slices for occupying edge DC resources. In the backhaul transport network, [PERSON] et al. [3] analyze the joint path selection and backhaul link scheduling problem and formulate the latency by capturing time-slots required for a link to satisfy its target rate. However, the allocation is limited to millimeter wave backhaul between macro cells and small cells, more structural characteristics of the backhaul transport network and service requirements have not been considered.  
It is worth noting that the aforementioned studies have not provided optimal-latency virtual resource allocation to support concurrent diverse NSIs (network slice instances) in the backhaul transport network, although it is necessary for the slice realization. Meanwhile, less research considers the structure features of transport networks and slice virtual networks and their mapping relationships, so the extreme QoS and user experience cannot improve significantly. In order to address the specific problems, more details about our approach will be provided in the next section.