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Hierarchical data storage is crucial for cloud-edge-end time-series database.Efficient hierarchical storage will directly reduce the storage space of localdatabases at each side and improve the access hit rate of data. However, noeffective hierarchical data management strategy for cloud-edge-end time-seriesdatabase has been proposed. To solve this problem, this paper proposesTS-Cabinet, a hierarchical storage scheduler for cloud-edge-end time-seriesdatabase based on workload forecasting. To the best of our knowledge, it is thefirst work for hierarchical storage of cloud-edge-end time-series database. Bybuilding a temperature model, we calculate the current temperature for thetimeseries data, and use the workload forecasting model to predict the data'sfuture temperature. Finally, we perform hierarchical storage according to thedata migration policy. We validate it on a public dataset, and the experimentalresults show that our method can achieve about 94% hit rate for data access onthe 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 storageoverhead caused by storing the full amount of data at all three sides, andgreatly reduce the data transfer overhead between each side when collaborativequery processing.

TS-Cabinet: Hierarchical Storage for Cloud-Edge-End Time-series Database