Chameleon: A Self-adaptive cache strategy under the ever-changing access frequency in edge network

In recent years, with the maturity of 5G and Internet of Things technologies, the number of mobile applications and the amount of data access have increased explosively. However, the frequency of these accesses varies considerably at different times of the day, requiring different caching strategies in those limited-capacity edge servers. Existing caching strategies perform well when the access frequency is stable. However, they ignore the time-varying characteristics of user access frequency in different periods, resulting in a low hit rate in ever-changing frequency scenarios. To improve the hit rate in such scenarios, we propose a cache replacement policy called Chameleon, which consists of two components, AutoFre, and Crates. AutoFre is an admission algorithm that predicts the future access frequency category and calculates the admission thresholds based on the prediction result. While Crates is an eviction algorithm, it selects the contents evicted by designing a customized principal component analysis algorithm. We conduct a series of experiments with real application traces from ChuangCache. The trace has 9,839,213 user accesses in 48 h. The results demonstrate that Chameleon reaches about 98% in caching hit rate and outperforms SecondHit-Crates algorithm about 8% in frequency-changing edge networks.