# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 2 Related Work  
**(1) Orchestration of MEC Servers:** With the development of MEC, numerous studies have investigated the orchestration of MEC servers. The authors of [11] studied the joint problem of MEC server placement and service placement. They employed a clustering algorithm and nonlinear programming to solve server placement and service placement sequentially. On the downside, this work used a fixed number of servers, which may incur unnecessary maintenance costs. The numbers of servers in [10; 13] were dynamically adjusted according to service demand. The authors of [10] proposed a three-layer algorithm to determine server locations, deployed computation units and subscribed computational resource, aiming to maximize the revenue of MEC operators. The authors of [13] used reinforcement learning to place MEC servers with the goal of minimizing the delay and the number of servers. However, the server deployment schemes in [10; 13] were \"one-shot\" decisions. In this case, when the geographic distribution of users shifts, servers need to be migrated to other locations in order to improve resource utilization, which may incur service interruptions, transportation costs, etc. Accordingly, the server activation problem was raised in [8; 14]. They both assumed that the locations of the MEC servers were fixed, but decisions need to be made on which servers to be activated. Inspired by [8; 14], we study the activation of MEC servers in this paper. _(2) Deployment of Network Slice:_ Except for the server activation issue, refs. [8; 14] also addressed the VNF chain deployment on MEC servers. Ref. [8] studied the deployment of VNF chains and their replicas on MEC servers with the goal of minimizing the cost. Ref. [14] investigated deploying primary and backup VNF chains on servers and presented a multi-objective optimization problem to minimize the number of VNF chains and servers, as well as the response delay. Differently, ref. [14] ignored the propagation delay, which is part of the delay and is related to the distance between the MEC server and the base station, whereas ref. [8] took this into account. However, neither of them took into account the geographic distribution of users and the deployment of access functions. The authors in [18] proposed a method for deploying VNF chains at the network edge, effectively improving resource utilization and latency utility. In [30], a real-time end-to-end VNF deployment and migration method was proposed, successfully increasing the throughput of network slices and reducing the migration cost of VNFs. Although refs. [18; 30] considered the physical connections between MEC servers, they did not optimize the placement of the MEC servers. In [19], although the authors considered the selection of remote radio heads (RRHs) to reduce the cost of RRHs, they also did not take into account the geographical distribution of users. In [17], access function deployment and VNF deployment were considered sequentially. However, the authors did not jointly optimize these two issues. In fact, the deployment scheme of access functions and the deployment scheme of VNF chains jointly determine the users' delay and the tenants' cost, and they mutually influence each other. Therefore, optimizing both schemes simultaneously can achieve better system performance. In our previous work [20], we designed a three-stage algorithm to address the above issues. Specifically, DRL was used to determine the number of activated servers, the k-means clustering algorithm was used to determine the server activation scheme and convex optimization was applied to solve the network slice deployment and resource allocation problems. However, the k-means clustering algorithm in [20] only considered the distance between servers and did not take into account factors such as user distribution and available resources, which did not guarantee that the solution is effective.