# ICN-Based Enhanced Content Delivery for CDN
## 5 Simulation Study
### Methodology
#### 5.1.2 Parameter Configuration  
As stated in Section 4, our problem can be solved using an ILP (Integer Linear Programming) model. Hereafter, we list the parameters we used in our numerical analysis, which can be categorized as follows:  
1. Content and request parameters: Since working with objects of a large cardinality is practically unfeasible in an ILP model, we generated a workload extracted from a catalog of \(1\times 10^{4}\) content objects following the Zipf distribution, and to study the impact of different content popularity on our model, we varied the parameter \(\alpha\) of the Zipf distribution to within the range of [0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2], referring to [51]. The requests of this workload were designed up to \(2\times 10^{4}\), with each request mapped to a content delivery demand in our model. We generated different demand profiles at the numbers in the range of [10; 12; 14; 16; 18; 20] to model different user demand frequency. Within an analysis, the destination of each request/demand was randomly assigned to an unrepeated user; the CDN edge servers servicing these requests were scheduled in a Round Robin manner. To stay faithful to the real CDN routing scenario, routing for each requested content was carried out in two stages if necessary as mentioned in Section 4.1. Furthermore, we analyzed two possible user demand rates of 0.5 and 1.0 Gbps. Finally, we computed the mean total bandwidth consumption and delay as the outcome.
2. Network parameters: We classified links into two main categories, the core links and the edge links.
* Core links refer to links connecting routers. Bandwidth limits on core links in both topologies are set to 10 Gbps. Delays on core links in the Abilene topology are set to the measurement results of [52].
* Edge links connect routers to user. For both topologies, bandwidth limits on edge links are set to the highest user demand rate of 1.0 Gbps, and delays are set to random values between 1 and 5 ms, following a uniform distribution.  
In particular, according to our previous assumption, for the link between the origin server and EG, the bandwidth is set to infinite, and the delay is set to a large number, for which we deem 100 ms to be reasonable, considering the dataset we employed from [52]. Meanwhile, we relaxed the bandwidth constraints on links connected to IG since it is the meeting point of all the influx when delivering content from the source to the PoP. Their limits are elevated to the value of \(max\) (content demand frequency) \(\times\)\(max\) (contend demand rate) to allow the concurrency of all the flows.
3. Cache parameters: In the context of our study, caching is involved in both CDN and ICN. As we assumed in Section 4.2, content is always preferably cached on CDN servers in our model. Regarding ICN, we implemented the LCE policy, which we deemed as the counterpart. We set the cache capacity for CDN nodes to be within the range of [10; 50; 100; 200; 300; 500] (objects). From a practical point of view, the cache capacity for CDN servers is assuredly larger than that of ICN routers. Therefore, we set the cache capacity of all ICN routers equally as one-tenth of the CDN server capacity, conforming to the homogeneous cache allocation strategy. When reaching the maximum cache capacity, the eviction policy of LRU is adopted for both ICN and CDN. The fundamental parameters we tested in our numerical analysis are shown in Table 4.