## 2 Related Works  
Recently, the operation of efficient AI services using edge computing in various applications has been studied. Depending on the nature of AI-based applications, edge computing could be used as a way to improve power consumption, inference accuracy, or inference delay performance, and it is important to consider what specific tasks to execute on which devices to maximize edge computing performance. Offloading strategies are essential for the effective use of edge computing, and various offloading strategies are being introduced in AI services. [PERSON] et al. [27] have studied the edge computing-driven AI service for object detection in low-light conditions. Since low-light conditions could reduce object detection performance, they proposed an end-to-end methodology for image enhancement and object detection. Their proposed structure consists of an image enhancement stage operating on cloud and an edge-based object detection stage performing in edge. In the first stage, the enhancement neural network computes the enhanced illumination portion of a low-light image. Then, edge devices can perform object detection accurately and quickly based on cloud computing information feature maps. By using this structure, the detection performance is significantly improved in low-light conditions. [PERSON] et al. [28] have developed an edge computing-based mobile object tracking method in the internet of things. For IoT devices, it is very difficult to implement advanced mobile object tracking that requires significant computing power due to energy constraints. To ease the energy consumption burden of IoT devices, they proposed an edge computing-based multivariate time series (ET-MTS) framework in IoT systems for accurately tracking mobile objects using edge computing. The EC-MTS framework leverages vector automatic regression to revisit arbitrary historical object trajectory data and fit the best trajectory model for accurate mobile object position prediction. Simulation results demonstrated that EC-MTS showed better object trajectory goodness-of-fit and object location prediction accuracy. [PERSON] et al. [29] have presented an efficient power-aware virtual machine (VM) allocation algorithm in a cloud data center. They considered virtualization to allocate power-aware VMs because the power efficiency of cloud servers in a data center is very important. The evolutionary algorithms were used for VM allocation to choose a suitable VM as a physical host. Simulation results demonstrated that their proposed algorithms improved power consumption. [PERSON] et al. [30] have designed a cooperative task execution mechanism for object detection using an edge computing structure. Cooperation with edge computing is essential to realize object detection algorithms in IoT devices with high performance and low power consumption. In their proposed cooperative task execution mechanism, the edge device collaboratively performs object detection on different images from end devices for optimizing the execution time and the execution accuracy. In particular, they focused on new policy that considers image compressing and the batch sizes of the received images from the end devices. Simulation results demonstrated that the proposed policy based on different splitting decisions and compression values can improve the E2E execution time and accuracy. Most recent studies [27; 28; 29] have focused mainly on offloading specific functions on edge and cloud servers rather than selective offloading [21; 30] according to system environments to improve inference accuracy and latency performance. In addition, offloading policies are derived based on the actual observed inference latency [30] or the inference latency calculated in proportion to the relevant data size [21]. Since these types of inference latency are somewhat different from the actual inference latency, there is a limit to deriving optimal offload policies for edge computing.