# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 1 Introduction  
The widespread adoption of advanced mobile applications with high bandwidth requirements has led to a significant increase in mobile wireless network traffic in recent years. As per the projected number of 5G mobile subscriptions worldwide from 2019 to 2026 [1], it is expected that the number of 5G subscriptions will continue to grow steadily, reaching 5 billion subscriptions by 2026. This growth is predicted to include a rise of 700 million subscriptions between 2022 and 2023, followed by an additional 900 million between 2023 and 2024. Especially on backhaul links, this explosive data growth will put a huge burden on current cellular networks [2] and the traditional core network architecture cannot accommodate the rapidly growing user demand. However, the introduction of edge computing has been introduced as a promising solution. Edge computing is gaining increasing attention from industry and academia, by deploying edge servers at base stations or access points near mobile users [3], which alleviates network traffic, reduces user request latency, and decreases energy consumption. Edge computing is now widely used in various applications, including face recognition [4], the Internet of Things (IoT) [5], autonomous driving [6], smart cities [7], and smart healthcare [8]. Edge caching is one of the prominent technologies that provide computing and communication capabilities and network caching capabilities [9]. Caching input files for required computations in advance or caching the computation results of certain frequently requested computation tasks, not only relieves the pressure on the core network but also avoids network congestion caused by the repeated transmission of the same content, thus achievingthe goal of the high-speed rate transmission of content. However, the current edge caching strategy seldom considers the characteristics of cache objects and servers, resulting in low cache space utilization and the frequent replacement of data blocks, which generates high system overhead and affects the advantages of edge computing. The main challenges faced by edge caching are the selection and placement of cached content. Developing an effective caching strategy to cache high-value content on inappropriate servers to improve overall performance is an important research topic. Therefore, a caching strategy is proposed to improve the utilization of cache space, reduce system overhead, and improve user experience by considering the characteristics of cache objects and servers for the placing of data blocks to be cached in a suitable cache cluster. The main contributions are as follows:  
1. A cache value model of data blocks is built by quantifying placement factors including data block popularity, remaining validity ratio of data blocks, and server replacement rate; a comprehensive-utility-based cache placement model is constructed based on data block cache value, data block acquisition overhead, and data block placement cost and replacement cost. 2. To avoid trapping in a partial optimum, a disciplinary strategy is introduced in tabu search, and a comprehensive-utility-based cache placement optimization policy is proposed for the edge computing environment; an optimized placement of cached data blocks is achieved through an improved tabu search algorithm in this approach. 3. The performance of the proposed algorithm is verified in three ways: data response time, cache service rate, and the number of permutations, by comparing the CPOSCU algorithm with LOCAL, GPA, and NVCP algorithms. The paper is structured as follows. Section 2 provides an overview of related work. Section 3 outlines the two proposed models, including the data block cache value model and the data block placement model, and then formulates the optimal placement problem as the composite optimization problem. In Section 4, the proposed algorithm is validated through extensive experiments and analysis.