  Hierarchical data storage is crucial for cloud-edge-end time-series database.
Efficient hierarchical storage will directly reduce the storage space of local
databases at each side and improve the access hit rate of data. However, no
effective hierarchical data management strategy for cloud-edge-end time-series
database has been proposed. To solve this problem, this paper proposes
TS-Cabinet, a hierarchical storage scheduler for cloud-edge-end time-series
database based on workload forecasting. To the best of our knowledge, it is the
first work for hierarchical storage of cloud-edge-end time-series database. By
building a temperature model, we calculate the current temperature for the
timeseries data, and use the workload forecasting model to predict the data's
future temperature. Finally, we perform hierarchical storage according to the
data migration policy. We validate it on a public dataset, and the experimental
results show that our method can achieve about 94% hit rate for data access on
the cloud side and edge side, which is 12% better than the existing methods.
TS-Cabinet can help cloud-edge-end time-series database avoid the storage
overhead caused by storing the full amount of data at all three sides, and
greatly reduce the data transfer overhead between each side when collaborative
query processing.
