# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
###### Abstract  
Optimizing cache hit rate in a multi-access edge computing (MEC) system is essential in increasing the utility of a system. A pivotal challenge within this context lies in predicting the popularity of a service. However, accurately predicting popular services for each MEC server (MECS) is hindered by the dynamic nature of user preferences in both time and space, coupled with the necessity for real-time adaptability. In this paper, we address this challenge by employing the Convolutional Long Short-Term Memory (ConvLSTM) model, which can capture both temporal and spatial correlations inherent in service request patterns. Our proposed methodology leverages ConvLSTM for service popularity prediction by modeling the distribution of service popularity in a MEC system as a heatmap image. Additionally, we propose a procedure that predicts service popularity in each MECS through a sequence of heatmap images. Through simulation studies using real-world datasets, we compare the performance of our method with that of the LSTM-based method. In the LSTM-based method, each MECS predicts the service popularity independently. On the contrary, our method takes a holistic approach by considering spatio-temporal correlations among MECSs during prediction. As a result, our method increases the average cache hit rate by more than 6.97% compared to the LSTM-based method. From an implementation standpoint, our method requires only one ConvLSTM model while the LSTM-based method requires at least one LSTM model for each MECS. Thus, compared to the LSTM-based method, our method reduces the deep learning model parameters by 32.15%. proactive service caching; cache hit rate; spatio-temporal correlation; heatmap sequence +
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users, the MEC system effectively mitigates service delays and alleviates the strain on the backhaul network. In the realm of MEC system design, a pivotal challenge lies in the task offloading decision problem. Various task offloading methods have been proposed [9; 10], generally falling into two primary categories. The first group involves users making intelligent offloading decisions, aiming to optimize service delay and the energy required for service completion [11; 12]. Conversely, another research strand focuses on MEC system operators striving to optimize the utilization of computing and communication resources while ensuring users experience reasonable service delays [13; 14]. However, these approaches often overlook the crucial aspect of service availability for the offloaded tasks. Essentially, they assume that services requested by tasks offloaded from users to the MECS are always available at the MECS. However, due to the inherent limitations of MECS in terms of computing power and storage compared to cloud servers, it can only accommodate a subset of the services available on cloud servers. Consequently, if MECS lacks the service requested by the offloaded task, it must fetch the service from the cloud server. Given that the installation and loading of services on MECS entail time, the effective management of service caching emerges as a critical factor in mitigating service delays within the MEC system. Ensuring the inclusion of the most popular services in each MECS cache is important in achieving a higher cache hit rate. The popularity of services within MECS is intricately linked to the service preferences of users in the respective MECS service area. The dynamic nature of user mobility causes changes in the set of users within the MECS service area over time. Moreover, user service preferences evolve over time as well. Consequently, determining the optimal service set to be cached in a specific MECS within the MEC system presents a significant challenge. To address this challenge, popularity prediction methods have been proposed [15; 16]. These methods delve into diverse metrics for each service, scrutinize their changing patterns, and extrapolate the future popularity of each service.