## 1 Introduction  
The computing paradigm is headed toward the edge of the network to address the limitations of cloud computing, such as latency between a central datacenter and a user device and load concentrated on a central datacenter. Gartner, the world's leading research and advisory company, mentioned \"Cloud to the Edge\" as one of its Top 10 Strategic Technology Trends for 2018 and included \"Empowered edge\" in its 2019 list. The emergence of the edge computing paradigm has generated active movements to redesign the network, increase coverage, boost network capacity, and cost-effectively bring content closer to users. Cloud and network experts forecast that this paradigm will contribute significantly to the emerging 5G environment. Within those active movements, caching techniques could be a key factor since the advantages of caching can fulfill requirements of edge computing. For example, caching can reduce unnecessary data transfers, thereby reducing the cost of network charges; reduce network bottlenecks, thus allowing pages to load quickly without increasing bandwidth; reduce requests to the origin server, thus reducing the load on the server and allowing it to respond faster; and reduce delayscaused by distance. Therefore, caching as a service should be considered so that caching techniques for edge nodes can be used more easily by service developers, end-users, and even between cloud service providers (CSPs). From a multi-cloud perspective, CSPs can collaborate and provide cloud services through caching. To do that, CSPs demand clear metering and billing methods for caching as a service. Edge nodes has its own limitations, and the most important of those is that edge nodes have relatively lower computing resources than a cloud datacenter. The reason is that it is difficult to allocate a lot of resources to the edge of the network due to cost effectiveness which has been an important since the introduction of cloud computing. That is why edge nodes deployed in multiple locations need to be miniaturized, energy efficient, and cost-effective. From the viewpoint of improving cost efficiency by taking advantage of existing resources, base stations or home WiFi routers could be improved and thereby become edge nodes. However, current research has not adequately considered the performance of edge nodes. Instead, it has studied applying relatively heavy tasks to edge nodes, such as machine learning [1; 2; 3; 4; 5; 6], high-quality video streaming [2; 3; 4; 5; 7; 8; 9; 10; 11; 12; 13; 14], and AR/VR [1; 3; 7]. Furthermore, existing caching research has focused on pre-deploying data to an edge node without fully considering the dynamic scale-up and -down of the cache [3; 15; 16; 17; 18; 19], which are related to the pay-as-you-go pricing policy of cloud computing [20; 21]. As the computational limitation of edge nodes, the lightweight, agile, and optimized data caching algorithm for edge nodes is highly necessary to meet the needs of cloud service developers, end-users, and CSPs. We here propose an efficient caching rate-control algorithm, EDCrammer (Efficient Data Crammer), that considers the performance of edge nodes and the optimal cost of the cache used. EDCrammer performs agile operations by using the enhanced PID (Proportional-Integral-Differential) controller, which includes three techniques, namely overshooting prevention, min-max regulation of PID output, and variation control of PID output, to tune PID controller for data caching. Accordingly, EDCrammer can control the caching rate for streaming data on resource-limited edge nodes by transferring appropriate amounts of data. The main contribution of this algorithm is that EDCrammer requires less computing resources than machine learning based caching, and reaches the desired cache utilization quickly. Therefore, this efficient data caching algorithm is suitable to apply for resource-limited edge nodes.