# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 2 Related Work
The tenant demand model and tenant provider model are used to represent the tenant demand, provider demand, and their different attributes and behaviors. A resource deficiency model is designed in [47], and a resource allocation algorithm for resource rejection rate, average resource waiting period, and resource utilization is proposed. The relationship between the resources consumed by task requests and the resources occupied by the system in a discrete computing network is analogized to the relationship between healthy cells and infected cells in epidemiology, and a task-resource model in a discrete computing network based on the classical virus dynamics model is built [48]. [PERSON] et al. used genetic algorithms and ant colony algorithms to solve the problem of mapping a set of software components to the available computational units in a heterogeneous computing system, but the authors only considered the single-objective problem of component allocation [49]. A model-driven approach was proposed to specify the allocation problem and automatically compute feasible allocations in [50]. The author formulated the allocation problem as a 0-1 integer linear programming; however, the approach can only find feasible allocations (not optimal allocation) and cannot be extended to large-scale scenarios. A platform for implementing architecture optimization algorithms was provided in [51; 52; 53]. The component deployment problem was modeled as a dual objective optimization problem, but its optimization process stalled after a certain number of iterations. A dynamic allocation algorithm for docker container resources that adapts to application requirements considering node resources, node network consumption, and the energy consumption was proposed in [54]. The resource allocation algorithms when different services are available in docker containers are described in [55; 56; 57; 58]. A genetic algorithm-based resource allocation and task scheduling for minimum completion time and maximum customer satisfaction was proposed in [59] in multi-cloud computing architecture. A hybrid genetic algorithm based on the case base for resource allocation was proposed in [60]. This algorithm finds Pareto solutions in a two-stage algorithm structure by computing crossover operators. Virtual network mapping is the mapping of a virtual network into a physical network. The deployment of network functions in the virtual network is performed in physical hardware devices through software, which is essentially the allocation of physical resources. The solution to the virtual network mapping problem is mainly to map virtual nodes and virtual links by considering the availability of hardware resources such as computing resources and memory resources of physical nodes in the physical network and the bandwidth resources of physical links, which is the optimal allocation of physical networks for virtual network deployment. Early virtual network mapping algorithms perform one-to-one mapping for simple networks in a small area, that is, a virtual node in a virtual network request can be mapped to only one physical node. In solving such irreducible mapping problems, the problem is usually modeled as a linear programming problem or a mixed integer programming problem for solution [61; 62; 63; 61]. Although this precise algorithm can traverse each virtual node request, it is only suitable for small virtual network mapping scenarios, and with the expansion of the network scale, the solution time and complexity of the exact algorithm grow exponentially. In addition, some of the early virtual network mapping algorithms [64; 65; 66; 67; 68] only consider part of the network requirements, consider the requirements of virtual network nodes in a simple normalized way, or only consider computational resources without considering the impact of storage resources on network mapping. The fidelity and practicality of this type of algorithm are lacking.