# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 4 Performance Evaluation  
To assess the improvement of the proposed CoHEC architecture, we created a series of simulations where the number of mobile users that transmitted a request message to the server was varied. To do this, we simulated the cloud and MEC environments using the CloudSim and EdgeCloudeSim simulation tools, respectively [21; 22; 23]. First, we constructed the hierarchical MEC network topology with three edge level layers, where each MECS in hierarchy level 1 was attached to one wireless router. The link bandwidth was assumed to be 100 Mbps. We set the number of users to be from 100 to 400. We assumed that each MECS could handle migration requests of up to 200 mobile users. Also, to express the complexity of a computing operation, we set the number of cycles of the Central Processing Unit (CPU). For example, if a computing task with 1 gigacyclic is offloaded to the server, its  
Figure 8: Message flow of the proposed context-aware hierarchical mobile edge computing (CoHEC) architecture. Figure 7: Computing type migration request message format and example. execution time would be 1/2.5 s. We compared the efficiency of various existing network architectures with the efficient use of server resources and the provision of low latency content. Figure 9 shows the average latency of the response when a user migrates a low complexity computing operation to a server based on the number of users. The average latency is the response time excluding the computation time after computing a migration request. As the number of users who delegate the computing operation to the server increases, existing mobile cloud data centers have significantly higher latency than the flat MEC architecture and the proposed CoHEC architecture. As the existing mobile cloud data centers are located at relatively long distance from the user, they have a high average latency time. Unlike the existing mobile cloud architecture, it is possible for a flat MEC to provide a low latency service to a user because it performs computing on an MECS close to the user. However, the average latency time of a flat MEC increases when the user's requests for computing migration operations increase. On the other hand, since the proposed architecture operates by migrating computation operations to upper hierarchy level MECS, it can provide low latency services even when the number of user requests increases. In addition, if the number of hierarchy layers increases, the MECS with high computing performance will be placed in the upper layer, so that the service can be provided to the user with lower latency. That is, if the hierarchical MEC architecture can be migrated to the upper layer MECS when its computing capability is exceeded, it will be possible to provide a service with a lower latency than the flat MEC architecture even if the number of users increases. Figure 10 shows the content download completion time according to the number of users requesting content. For this purpose, a high volume of multimedia video was chosen to evaluate the possibility of providing fast content as the user content migration requests increase. Existing mobile cloud architecture can provide multimedia content to the user if the user capacity of the mobile cloud data center is not exceeded. However, if the content request increases and the mobile cloud data center cannot process all of the users' requests, the mobile cloud data center requires a long downloading time, because it performs the content providing operations according to the order of the requests. The flat MEC architecture enables faster content download than mobile cloud architecture because the adjacent MECS of the user provides a caching function. On the other hand, the proposed CoHEC architecture provides a content caching function by hierarchically structuring MECS, so that it is possible to perform a download quickly if the neighboring MECS has the content requested by the user.