Identification of Hot data and Caching strategy for Industrial Big Data Based on Temperature Model
Yao Wang, Jiong Xin Zhao, Qicai Zhou, Xiaolei Xiong, Heng Zhang, Chuanlin Chen · 2023
In storage systems, there are heat differences between data. Traditional algorithms such as LRU are limited by specific data structures. These methods cannot be well applied to industrial big data storage systems. Methods based on “temperature” are usually limited by static parameters and it is unable to adapt to dynamic load. Based on Newton's law of cooling, we proposed an identification model called AdjustDTM, with adjustable parameters. Our method identifies the hot and cold by assigning the attribute “temperature” to the data. Then, the model can dynamically adjust the parameters according to accessing interval and frequency. Our model can also preheat the correlative data. Finally, The experimental results showed that the hit rate of AdjustDTM is higher than other strategies.