# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 2 Methodology and Foundations
### Resource Management in Network Slicing

Table 4: Resource orchestration based on DRL. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Rel** & **Algorithm** & **Frame** & **Optimization Objective** & **RegionRegionRegion** & **NS Resource** & **Number** & **Number** & **Number** & **Number** \\ \hline
**Rel** & **complete** & **complete** & **complete** & **complete** & **complete** & **complete** & **complete** & **complete** \\ \hline
Network slices support the provisioning of 5G use cases defined by the International Telecommunications Union, each with specific performance requirements [97]. The 5G use cases are known as eMBB, mMTC, and uRLLC [98, 99]. The eMBB refers to services demanding high data traffic and a bit rate of 20 Gbps and 100 Mbps for user experiences in urban zones. The mMTC covers services requiring the connectivity of a wide gamma of devices and simplifies operational processes for providing a long battery lifetime. The uRLLC comprises services needing ultrahigh reliability and extremely low latency [100]. Although 5G networks have been widely deployed since 2020 [101], 6G networks appear on the horizon. This appearance is to address the exponential growth of emerging telecommunications services demanding more ambitious performance requirements. Furthermore, 5G features cannot entirely support extremely demanding services such as remote surgery and immersive media. The 6G networks propose highly heterogeneous environments that are expected to provide global coverage, enhanced spectral/energy/cost efficiency, higher data rate (Tbps), 10 times lower latency, 100 times higher connection density, and full automation compared with 5G networks [102]. Technologies such as novel air interface and transmission techniques and architectures based on the IoT-Edge-Cloud continuum are fundamental for accomplishing 6G [1]. Potential 6G use cases include: sEMBB embraces EMBB services demanding high QoE; umMTC comprises services needing a much more massive number of simultaneous connections per space than mMTC; mURLLC covers mission-critical services requiring high reliability, low latency, and high availability; MBRLLC includes classical URLLC and eMBB services; ERLLC comprises services that merge URLLC and mMTC demands. These use cases are essential to accomplish intelligent home systems, smart cities, mission-critical applications, self-driving cars, and remote surgeries [36, 103]; notably, they require approaches capable of performing dynamic network slicing resource management. Figure 1 shows different 5G/6G end-to-end slices built on a shared network infrastructure. NSL follows an architecture formed by the infrastructure layer, the network function layer, and the service layer [104]. The infrastructure layer represents all physical elements (involving RAN, CN, and the edge network) needed by slices and the functions for controlling, operating, maintaining, and managing them. The network function layer encapsulates all configurations and life cycle management functions of the service function chains (SFCs) needed to realize end-to-end services that fulfill use cases' performance requirements. NFV and SDN are fundamental technologies in this layer [105]. The service layer comprises  
Figure 1: 5G/6G network slices. the vertical applications, business models, XLAs/SLAs, and performance requirements of network slices. Intelligent and efficient resource management is fundamental for accomplishing the layers mentioned. Resource management involves four phases (see Figure 2): admission control, resource allocation, resource scheduling, and resource orchestration. It is noteworthy that a phase can provide feedback to another one, and, as a result, they should operate coordinately. Admission control decides which slice requests coming from tenants (or a single one) can be accepted or not according to one or various network policies related to avoiding idle resources, increasing network providers' revenue, and prioritizing services [10], for instance.