# Reinforcement Learning Based Resource Management for Network Slicing
## 2 Related Work  
The topic of network slicing has received substantial attention among the research community. The end-to-end network slicing allocates slice components spread across the entire network to the tenants to satisfy the diverse requirements with flexibility and scalability [5]. From the current technology's point of view, although network slicing is still a theoretical step, many researchers are looking forward to making a commercial product out of the network slicing concept for 5G systems because of the flexibility and scalability provided by network slicing. In fact, business opportunities of network slicing paradigm can promote an economic advantage for 5G. In [6], [PERSON] and [PERSON] conducted a survey of the economic aspects of network slicing virtualization in terms of network resource and pricing based on various auction mechanisms. They presented the fundamentals of auction theory in designing the business model and simultaneously attempted to optimize resource utilization and profit using a pricing rule. The auction theory is able to model interactions among the provider and multiple tenants. In [7], [PERSON] et al. reviewed resource pricing approaches for efficient resource management to maximize the benefits of the provider and tenants. They emphasized that an efficient mechanism of resource allocation is required for the high resource utilization of the provider and QoS for end-users of tenants. There has been a significant amount of research work on the management and orchestration of network slicing. In [8], [PERSON] et al. comprehensively summarized principal concepts, enabling technologies and solutions as well as the current standardization efforts such as the software-defined networking (SDN) and network function virtualization (NFV). By presenting relevant research, they described how end-to-end network slicing can be achieved by designing, architecting, deploying, and managing network components in 5G networks. An important issue of network slicing is the resource allocation to satisfy a number of heterogeneous service requirements by taking the tenant's point of view. Under such a resource allocation model, every tenant has the ability to allocate network resources to their own slice according to their strategic behavior. In [9; 10], [PERSON] et al. focused on resource allocation based on a game-theoretical model for tenants' strategic behavior and analyzed parameters to reduce cost while maintaining the QoS. The Fisher market mechanism has been used to share the resources of each tenant in a fair manner according to different requirements of end-users who are served by each tenant. The tenants indicate their preferences to the infrastructure and then receive resources of the base station according to their preferences. On the other hand, there is a strategy of the resource allocation and admission control focusing on how the mobile network operator (MNO) adjust slice parameters to maximize the overall revenue. When a tenant issues a service request to create a new slice, the MNO considers the admission of the slice. If MNO determines approval of the request, a slice is created and granted to the tenant. In [11], [PERSON] et al. proposed a mechanism that MNO has the authority of admission control of tenants to maximize the revenue of a network. In [3], [PERSON] et al. proposed a genetic optimization for resource management across the slices using an MNO's binary decision. However, the majority of such studies have not considered dynamically changing resource requirements over time. Thus, most of their business models were quite simple in that a fixed and roughly estimated monthly or annual fee was paid for the resource sharing. The operation in such a framework can often incur unnecessary expenditure due to unused resources or resource scarcity. To address this issue, there are some studies that have considered resource trading in the context of network slicing. In [12], [PERSON] et al.