# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 2 Related Work  
The caching placement strategy is an important issue in edge computing research and a key factor in improving caching performance. An increasing number of researchers are considering studying caching placement in the context of edge computing. To address the challenge of data placement caused by user mobility, reference [10] proposed a mobility-aware caching strategy by considering the randomness of user mobility. This strategy aims to find the optimal location for the requested content to minimize the average load on the base station. Reference [11] presented a popularity-based cache placement strategy by considering the connection quality of the network, the demand for content, and user activity, which divides the requested data blocks into three popularity levels and strategically caches the content into fog nodes with different activity levels to reduce energy consumption. Reference [12] studied a genetic algorithm-based cooperative cache placement strategy for contact duration-aware mobile edge networks by analyzing the impact of user mobility and contact duration on cache placement in mobile edge networks. Reference [13] investigated how to perform caching placement on SBS and mobile devices using user mobility to maximize the cache hit rate, which is described as a mixed integer nonlinear programming (MINLP) problem, and they solved the problem using greedy algorithms. Designing placement strategies based on user needs is currently an urgent issue to be addressed. Reference [14] proposed an intelligent caching scheme that considers not only the location of user requests but also categorizes them according to different caching policies to solve the caching problem of content-centric network (CCN) routers. Reference [15] determined the cached content on each network node based on the relative popularity of the content and proposed a greedy-algorithm-based content placement strategy to maximize the overall network hit rate. Reference [16] studied task caching in multi-user and multi-server mobile edge computing systems. By calculating the size and popularity of tasks and considering the actual distribution of users and task requests, a task was cached on multiple MEC servers to improve the hit rate of popular tasks. To cache content demanded by users on the nearest IoT device, reference [17] used a recurrent neural network method to predict the user location. They used an FP-growth algorithm to forecast the location of each IoT device and the interests of users, which precaches the interested content to reduce access latency and improve caching efficiency and user experience. Aiming at the limitations of non-cooperative strategies for data block placement, reference [18] proposed a cooperative edge caching scheme by considering the location and prevalence of vehicle request content to minimize transmission latency and service cost. Reference [19] presented a dynamic collaborative caching strategy for latency-sensitive applications in an edge computing environment, which reduces redundant forwarding and improves the hit ratio of the entire network by considering the popularity of the content at the edge nodes and the caching capacity of the nodes, and placing popular content on the core nodes. Reference [20] suggested a CE-D2D collaboration framework that fuses edge networks and device-to-device clusters. In this framework, different mobile users and MEC servers collaborate to store different blocks of a video to optimize the use of storage resources and transmission resources. Reference [21] offered a caching scheme that considers the trade-off between cache diversity and redundancy, which achieves an optimal redundancy ratio for the content of each base station placement through collaboration between placements in the radio access network.