# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 3 System Model  
In this section, we propose the overall design of the ICN-based telco CDN architecture. Then, we present the framework that consists of an LDP module, PARM module, and SM modules. Finally, we illustrate the functions and relationship between the listed modules.  
Figure 1 shows the ICN-based telco CDN service scenario we consider in this paper in comparison with the traditional CDN service scenario. Traditional CDNs, e.g., Alibaba CDN and Tencent CDN, always deploy their sites in core networks or connect their sites with telcos on Point-of-Presence (PoP) [22] and serve traditional IP access networks. In comparison, telcos in the 5G era can deploy amounts of sites in their cloudified hierarchical access networks. We propose to use these sites to provide a caching service for both ICN-based and IP-based underlay network regions. In IP network regions, content requests are directed to CDN sites after DNS look-up and data flows are forwarded based on IP address. In ICN network regions, content requests are looked up based on content name at  
every router; if the requested content is cached locally, then the content chunks are directly sent back. Once a cache miss occurs, the request is forwarded to the border router, which is employed to direct the unsatisfied requests to the CDN sites. Take ICN-based region 1 as an example, if a cache miss for content A occurs at R1, border router R2 will direct the request to CDN site 1 or site 2 according to the forwarding information table (FIB) rules (as shown by R2's FIB in Figure 1). In this paper, we assume there are \(M\) telco CDN sites, serving \(L\) user access regions. The regions may be partitioned in base station level or aggregation-level. Let \(C_{m}\) denote the bandwidth capacity of site \(m\), and \(d_{m}\) denote the historical traffic usage of site \(m\) (\(m\in M\)). Let \(t_{lm}\) denote the latency between region \(l\) (\(l\in L\)) and site \(m\) (\(m\in M\)). Assume that \(N\) applications adopt the telco CDN service, paying their bandwidth and cache expenses to the telco. Let \(\lambda_{nl}\) denote the request arrival rate of application \(n\) (\(n\in N\)) in region \(l\) (\(l\in L\)). Let \(t_{n}\) denote the expected response time of application \(n\) (\(n\in N\)).  
Given the input parameters above, the telco should set the bandwidth price and calculate the rules of user request mapping. Let \(p_{m}\) denote the unit bandwidth price of site \(m\) (\(m\in M\)). Let \(\alpha_{nlm}\) denote the portion of application \(n\) requests from region \(l\) directed to site \(m\). For clarity, we summarize the notations mentioned above in Table 1.  
In our design, the bandwidth prices and the request mapping rules are periodically calculated by the LDP module and PARM module, respectively. As shown in Figure 2, the SM module measures the bandwidth usage, the application access statistics at different sites, and updates the latest state to the LDP module and the PARM module. The LDP module calculates bandwidth prices according to the congestion level and its variation. Then, on the basis of the prices and other system parameters as listed in Table 1, the PARM module runs an optimization to obtain the latest request mapping rules and install the rules on the relevant sites. Given \(T_{LDP}\), \(T_{PARM}\), \(T_{SM}\) as the update period of LDP, PARM, SM, respectively, then they should be set according to the following inequality:  
\[T_{LDP}>T_{PRAM}\geq T_{SM}. \tag{1}\]  
Figure 1: Telco content delivery network (CDN) service scenario in comparison with traditional CDN scenario.