Dynamic decision-making strategy of replica number based on data hot

As a fast-rising storage model in recent years, cloud storage has adopted a “pay-as-you-go” approach to provide users with highly reliable, highly available, low-cost, and secure storage services that have received widespread attention and use within enterprises and individuals. Data replica management technology, as an essential part of cloud storage systems, has irreplaceable advantages in improving cluster fault tolerance and availability, so it has become the focus of many experts and scholars. It replicates multiple data blocks and places them in various nodes in the cluster; this makes the data more secure and reliable and improves the access rate while ensuring system load balance. Data replica technology runs through the process from replica creation to consistency maintenance. Each part of it has an essential impact on the performance of the cloud storage system. This article focuses on the dynamic decision of the number of data replicas in the cloud storage system. Considering the shortcomings of the static replica strategy, a dynamic decision strategy for the number of replicas based on the popularity of the data is proposed. By using the gray prediction model GM (1, 1) to predict the future data access frequency and using the Markov model to modify the prediction result, the data can be divided into hot data and non-hot data according to the predicted value, thereby determining the data replica number. Finally, through simulation experiments, the experimental results of the static replica strategy and the data hot-based replica number dynamic decision strategy are compared and analyzed.