RESUMO
This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms.
