## 2 Related Works
### Edge Caching and Edge Data Management Techniques Analysis  
Caching data to the edge of the network has been researched extensively to address network traffic management. Edge caching techniques need to consider requirements for lightweightness because an edge node has relatively low computing resources. Low-power devices, such as embedded systems, have begun to have multiple cores, and on-chip caches are under study as on-chip caches can consume half of a system's total energy, which is very expensive. Improving the PID controller, [PERSON] et al. [45] could create optimal banks for each thread and thus reduce the energy use of the  
Figure 1: The coverage of key features among conventional cloud computing and edge-related technologies [3; 5; 8; 9; 31; 32; 37].  
shared cache by 39.7%. In a low-power device, caching methods using lightweight algorithms are important because the resources or energy consumed by the cache can be a large part of the total consumption. Caching to edge nodes must consider different factors from hardware level caching. For example: What should be cached? How should the limited capacity be used? What is the network status? How should data be delivered to a moving user? Therefore, a new design is sometimes needed for 5G [17]. A relationship analysis and context awareness of Social Network Service (SNS) are used to control the traffic load by applying proactive caching [19]. In addition, cloud data are decomposed into a set of files and services, and replicated to MEC nodes by frequency-based workflow-net [46], or to fog storage sites by predictive fog selection based on trained user's past history [47]. However, the above preactive and proactive methods for data caching would be a relatively computing intensive work to apply to edge nodes, and occupy the storage resource related to the pay-as-you-go pricing policy in resource-limited edge nodes. Thus, it is necessary for a data caching method to perform lightweight computing and to transfer data to edge nodes on demand.  
Meanwhile, the normalized delivery time (NDT) is theoretically derived to prove the usefulness of a cache [18], although it is difficult to derive the various metrics into an optimal metric. In addition, the data encoding often uses maximum-distance separable (MDS) codes as an alternative to file fragmentation for energy-efficient edge caching. By using MDS codes, it is possible to obtain the optimal caching location and reduce the backhaul rate [48]. To efficiently manage caches in a 5G Cloud Radio Access Networks (C-RAN) environment, exponential decay and the Analytical Hierarchy Process (AHP) are applied to achieve multi-level mobility and the smallest amount of feedback [15]. Although it is possible to derive a more optimal caching method by considering various factors and a new design, it is more important for an algorithm to be lightweight and agile as edge nodes have limited resources. Accordingly, we propose an efficient caching rate-control algorithm and framework of streaming data to resource-limited edge nodes. We hope the proposed algorithm not only supports high-quality cloud service by reducing network latency and congestion, but also contributes real-time services, such as AR and VR, autonomous vehicles, and ITS.