# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
###### Abstract
By leveraging these identified patterns, they make estimates about the future popularity of each service. However, these methods primarily rely on the temporal relationships among services within individual MECS to forecast service popularity. Since users change locations over time, not only the users currently residing in the service area of a MECS but also the users served by the neighboring MECSs affect the future service popularity in the MECS. Consequently, integrating the spatial-temporal correlations between MECS enhances the accuracy of service popularity predictions. From an implementation standpoint, the number of predictors required in these popularity prediction methods escalates with the expansion of services and MECSs. Thus, concerns about scalability may surface as the MEC system grows. In response to the identified challenges, we propose a proactive service caching methodology inspired by the Convolutional Long Short-Term Memory (ConvLSTM) model, recognized for its efficacy in video frame prediction [17; 18]. Our approach seeks to exploit the inherent spatio-temporal relationships among service popularity within a MEC system. To operationalize the ConvLSTM model for this purpose, we discretize time into time slots and construct a heatmap that collectively depicts the distribution of service popularity in each MECS during a given time slot. Treating each heatmap in a time slot as a frame, we conceptualize the sequence of heatmaps as a video. Subsequently, we train a ConvLSTM model to predict the upcoming heatmap based on a few recent heatmaps. At the beginning of each time slot, we leverage the trained model to identify the most popular services from the predicted heatmap. These services are then selected to be cached on each MECS for the ensuing time slot. Utilizing a heatmap containing the popularity of each service across MECS as input enables us to comprehensively predict the popularity of all services in all MECS simultaneously. This holistic approach contributes to an enhanced service cache hit rate in each MECS. In addition, our methodology stands out by requiring only a single predictor model, as opposed to conventional methods necessitating separate prediction models for each service and MECS. This consolidation not only streamlines the predictive process but also significantly improves the scalability of a MEC system. Our main contributions can be summarized as follows. * We propose a framework for proactive service caching by taking a deep learning approach. Inspired by the ConvLSTM model that successfully predicts video frames by exploiting the hidden spatio-temporal relationships in the frames, we incorporate the ConvLSTM model as a fundamental element in our proactive service caching methodology. * We propose a procedure that utilizes ConvLSTM model for accurately predicting service popularity for each MECS over time. We construct heatmaps collectively representing the distribution of service popularity in a MEC system during each time slot. Treating each heatmap in a time slot as a frame, we conceptualize the sequence of heatmaps as a video. We predict the next heatmap by using the ConvLSTM model and identify the most popular services for the upcoming time slot from the predicted heatmap. * Through simulation studies using real-world datasets, we verify that our method outperforms conventional LSTM-based method in terms of the cache hit rate and the amount of model parameters required to predict service popularity in a MEC system. The rest of the paper is organized as follows. In Section 2, we discuss the related works. We explain the system model and formalize the problem in Section 3. In Section 4, we present our proactive caching strategy. In Section 5, we verify the proposed method by comparing its performance with that of the conventional method through simulation studies using real-world datasets. We conclude the paper with future research directions in Section 6. Before we proceed, in Table 1, we present the notations used in this paper.