## 5 Conclusions and Future Works  
Streaming data applications are increasing rapidly every year due to the advent of one-person media (e.g., personal broadcasting) and the emergence of 5G. In addition, user devices are changing from fixed types such as TV or PC to moving types (mobile) such as smartphones and tablet PCs, and most of the users would prefer smartphones. Many unspecified persons can freely move and watch high-quality media through various devices. Therefore, service providers have begun to experience difficulties in providing services to users. Most services are provided from a cloud datacenter. However, existing cloud computing has inevitable latency caused by its geographical location and the huge traffic concentrated in the central cloud. To solve those issues, edge computing is being actively researched. Of course, it would be nice to place many edge nodes near every user, but cost-effectively placing edge nodes is difficult. Therefore, in this paper, we assume that edge nodes have resource constraints. Lightweight, agile caching with a PID controller is proposed as an efficient rate-control algorithm for streaming high-quality data. Through the algorithm, the constrained computing resources of the edge nodes can be used efficiently. Existing caching techniques predominantly predict user patterns or data flows and deploy data in advance to provide data when needed by users. In addition, the pay-as-you-go pricing policy has been less considered, but it is a very important policy that must be considered when adopting cloud technology to edge nodes. For example, if a user uses a sufficient cache capacity, the cache hit ratio will increase, but the transaction will be costly. Therefore, the proposed EDCrammer is needed. The lightweight, agile rate-control algorithm EDCrammer performs minimal operations to prevent overloading the edge nodes and it satisfies the desired cache utilization quickly and reliably, even with those minimal operations. Improving existing algorithms to perform more sensitive caching is also an advantage of EDCrammer. In conclusion, EDCrammer is suitable for dynamic data streaming environments, which we demonstrated through the results of Scenario 4, in which the average convergence time of the proposed algorithm was 39% faster than that of the existing one, and the average convergence deviation of the proposed algorithm was also 4% more stable than the existing one. In addition, EDCrammer supports high cache hit ratios of more 96%, and the 4% miss can be improved through waiting time or preparation time. Finally, EDCrammer can help distribute the streaming data traffic to the edge nodes of 5G environment, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We hope this algorithm can contribute to 5G environment, AR, VR, ITS, IoT, etc. This study has a few limitations. In this study, we calculated \(K_{p}\), \(K_{i}\), and \(K_{d}\) using an engineering approach. When providing services in the cloud, there will be more variables than in our experiments, thus it would be better to use automatic gain tuning such as Ziegler-Nichols or Tyreus-Luyben. In addition, the possibility of this framework and the proposed algorithm was confirmed by comparing with the PID basic algorithm and the existing algorithm. However, it was still difficult to make a decision about optimal design. Therefore, we will compare more various algorithms through future studies. In addition, EDCrammer needs some different functionalities to provide multi-tenancy. For example, two CSCs might watch the same video. In that case, a CSP does not need to provide two different caches. Therefore, a method to save total cache capacity and guarantee QoS is necessary. Those limitations will be considered in our future work. Conceptualization, E.-N.H.; Software, Y.K. ; Supervision, E.-N.H.; Validation, Y.K. ; Visualization, Y.K. ; Writing--original draft, Y.K. ; Writing--review and editing, E.-N.H. This research received no external funding.