# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 2 Related Work  
As one of the key algorithmic functionalities in CDNs, request mapping/load balancing has always been a hot topic for its direct impact on the end users' QoE and the CDN providers' costs [37]. Specifically, different types of CDNs have different design goals in terms of request mapping.  
In the context of traditional CDNs, most CDN sites are deployed in the data centers that are owned by CDN companies and the data centers are always in core networks. Therefore, a key principle in request mapping design is to take users' location into consideration. Researchers from Bell Labs propose to use user location and content type as match fields of the routing table in the request mapping server [38]. Stability of the designed mechanism is guaranteed by the Lyapunov optimization technique while the transmission cost is minimized. Different from current request mapping mechanisms that are usually based on DNS/HTTP redirection, the proposed mechanism provides more flexibility and finer mapping granularity, which help reduce average delivery time and promote cache hit ratio. Similarly, researchers from Akamai also propose to take user location into account and realize their design in commercial deployment [39].They make it possible to specify the client's IP prefix by using an EDNS0 extension of the existing DNS protocol. Specifically, the IP prefix of a user is included when a DNS look-up request is forwarded from a local DNS (LDNS) to an up-level authoritative DNS server. The EDNS0-based request mapping design significantly reduces round-trip-time (RTT), the time to first byte, and content downloading time.  
In the context of cloud CDNs [40], CDN sites are deployed in cloud data centers which are highly distributed and always have to transit a number of Internet Service Providers (ISPs). Therefore, a key principle of request mapping is to optimize the cost efficiency. For example, the authors of [41] argue that geographical diversity of bandwidth cost brings a highly challenging problem of how to minimize total cost. In this context, bandwidth cost means the bandwidth prices that are paid by a cloud CDN provider to different transit ISPs. They formulate a problem that minimizes the summation of bandwidth cost and energy cost. In addition, they propose a hybrid particle swarm algorithm (HPSO) to solve the problem. Simulations show that the proposed design can minimize the total cost and promote throughput of the system. The authors of [42] also take into account the bandwidth prices charged by transit ISPs when solving request mapping and routing problems. In order to reduce complexity and promote robustness in the implementation, they design a distributed tide algorithm and evaluate the effectiveness. Simulations show that the proposed design can guarantee minimized average latency and average cost.  
In the context of telco CDN (ISP-operated CDN), request mapping is often designed in collaboration with congestion management of the underlying network infrastructure. The reason is that a telco CDN has to optimize both the service performance that its overlay (CDN) provides and the efficiency of its underlying network infrastructure. The authors of [25] argue that in Mobile CDNs that enhance Base Stations with storage capability, blind redirection of user requests upon content placement can cause traffic congestion. They investigate a joint optimization problem of content placement and request mapping to achieve both congestion avoidance and load balancing. By using the stochastic optimization model and Lyapunov optimization technique, they design an algorithm that efficiently achieves a low transmission cost. An evaluation confirms that the design goals were achieved. The authors of [43] propose a flexible content-based and network-status aware request mapping mechanism, taking the content prefix and traffic statistics as input parameters. Simulations show that average traffic volume and average access latency are both reduced.