# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 2 Related Works
### Caching in a MEC System
RL-Cache uses the size, frequency, and request recency of an object as an input feature for a feed-forward neural network that determines the admission probability of an object. However, these DRL-based methods do not exploit the spatio-temporal relation among the cached services in each MECS. In addition, they are reactive in that they make a caching decision in an on-demand manner. In [24], the authors analyze the road side unit (RSU) historical content request data and calculate the spatial-temporal correlation among the RSUs in terms of the request number, which is used to predict the service popularity in each RSU. They also employ multi-agent RL (MARL) to cope with the diversity of the popularity. However, unlike the method in [24] that uses a traditional statistical tool to directly calculate the correlation, we adopt a deep learning model to use the hidden features in the spatio-temporal relationships. To make a caching decision in a proactive manner, the dynamics of service popularity is often considered as a time series, and a deep learning model that can handle sequential data is adopted to predict the future service popularity. An LSTM encoder and decoder model is used in [25] to map a sequence of objects requested so far to the top-\(k\) objects most likely to be requested in the future. In [26], a two-step model is proposed. In the first step, the popularity of a video genre in a future time slot is predicted. Then, the popularity of a video belonging to the prevalent genre in the same time slot is further predicted in the second step. An LSTM model is also used in [27]. The authors predict the number of content requests by the seq2 seq LSTM model. Using the priority of the content and predicted popularity, they solve the cache placement problem with the binary particle swarm optimization (BPSO) technique. However, since BPSO is an iterative process, it takes time to find a solution. Attention-based methods have been proposed for a caching problem in a MEC system. In [15], the authors propose a multi-head attention-based popularity prediction model (MAPP). After proposing an architecture that integrates MEC in social content-centric network, they use MAPP to predict content popularity by considering the history of content popularity, social relationships, and geographic location. However, they mainly use temporal correlations in the data when predicting the popularity. A transformer-based edge (TEDGE) caching scheme based on the attention-based vision transformer (ViT) is proposed in [28]. At the end of each time window, they aim to determine the top-\(k\) popular contents in the future by directly mapping the request patterns of all contents during the current time window to their future request patterns. In [29], authors improve the TEDGE by proposing a self-supervised caching scheme called CoPo. CoPo distinguishes the input samples by utilizing the contrastive learning paradigm. Since CoPo does not need all the input request patterns of all the contents at the same time, it can reduce the algorithm complexity, and even the number of contents increases. In addition, CoPo does not require manual labeling for model training. In [30], a parallel ViT with cross attention (ViT-CAT) fusion is proposed, which is composed of two ViT networks. The first ViT network collects the temporal correlation of a content, while the second ViT network captures the spatial correlation between different contents. In [31], the authors focus on the popularity prediction problem while preserving the privacy of users. They propose an efficient content popularity prediction of a privacy-preserving (CPPP) scheme based on federated learning and the Wasserstein generative adversarial network (WGAN). The goal of CPPPP is to predict future content popularity by generating fake samples that can represent the overall trend of content popularity. However, since they use federated learning, it is inevitable to exchange model parameters between users and MECSs multiple times.