# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework
### Optimisation Using Cuckoo Search
In reactive caching, there is a very small amount of cache to store and process the data; in case of increasing cache size, we have to buy the cache. Figure 6: Cache hit ratio with different cache sizes. Figure 8 shows that increasing cache sizes data delivery costs will be minimized because requested data will be pre-cached in the server and not need to fetch content from the upper layer. When the cache size is 150 (GB), the caching cost in COOP (ACO) is 28%, while in COOP (CS) it is 25%. In proactive, when the cache size is less than the delivery, the cost is almost 25% less, but it fluctuates after some time, in case of not finding the content in its local cache. While in the reactive cache, it always downloads content from the back-haul server. Hence, the delivery cost will increase more. Figure 9 shows that by increasing cache sizes more files will be stored in a cache, so a greater number of users can access the data from the nearest servers then the cache hit ratio will definitely increase. The results show that there is a 1% difference in delay when the cache size is 20 (GB). While increasing cache size delay is almost 10% less in our proposed technique. In reactive caching, the delay is 20% more than our proposed technique, as there is no cache and cooperation. The reason is that in COOP (CS) due to clustering and optimal route finding, there will be a minimum delay to send and receive requests. ### Delay and Caching Cost While Increasing Number of Files

The given results show that caching more files on the server increases the availability of data on demand. There will be a minimum delay because files will already be cached on corresponding servers and there is no need to download files from the back-haul server. As the number of files increases, the availability of content will be high so the hit ratio will also increase. Figure 10 shows that in reactive caching when the number of files will increase, the hit ratio will not be good due to non-caching devices, and each time data is downloaded from the back-haul server and remains the same in indifferent cases. In proactive caching, the hit ratio is 45% with an increasing number of files, while COOP

Figure 8: Delivery cost with different cache sizes. Figure 7: Caching cost with different cache sizes. (ACO) performs much better due to its cooperative nature. However, in COOP (CS), the hit ratio is almost 90% due to joint cooperation among RSU and BS, clustering, and CS optimization algorithm. If more files are available in the local cache, the time to deliver that content is less. Figure 11 shows if there are 20 files in the cache then the delay will be 10% more as compared to COOP (CS). Similarly, when there are 300 files then the delay of COOP (ACO) will be 20% more than COOP (CS). In proactive caching, the delay is double due to its non-cooperative nature. It fluctuates more in case of non-availability of content, it will fetch from the back-haul server. While reactive cannot store more files due to the unavailability of the cache. Figure 12 shows that by increasing the number of files in the cache, caching cost will also consume in terms of the price of BW consumption. In COOP (CS), data are

Figure 11: Delay with increasing number of files. Figure 10: Cache hit ratio with increasing number of files. Figure 9: Delay with different cache sizes. collaboratively shared among servers, so there is no need to download files again. As a result, the cost will be minimized. When there are only 20 files, the caching cost is almost the same at 12%, but when there are 300 files, the cost of COOP (ACO) is 87%, while the cost of COOP (CS) is 75%, the same as proactive caching, which is 12% costly than our proposed technique. Data are stored in clusters that are more organized and easier to maintain. ### Comparison of Number of Users with Delay

In Figure 13, caching with the cuckoo search algorithm and ant colony is compared in terms of increasing the number of users with delay in finding the optimal path to reach the requesting node, where required content is cached.