TempSched: A Temperature-Aware Storage Scheduler for Time Series Across Cloud-Edge-Device

Storage scheduling is crucial for time series storage. However, designing an efficient hot and cold tiered storage scheduling strategy for time series across Cloud-Edge-Device (CED) architecture remains challenging. Although numerous research have studied hot and cold classification for relational data, these methods are not suitable for time series which has strong timeliness and complex access patterns. Therefore, in this paper, we present TempSched, a temperature-aware storage scheduler for time series across CED, which can identify hot and cold time series and predict data temperature efficiently to perform storage scheduling in advance. By employing Newton's law of cooling and the thermal radiation law, TempSched establishs a temperature model and encapsulates data temperature. It supports classifying hot and cold data and scheduling time series across CED. Subsequently, TempSched designs a workload prediction model and a frequent timestamp discovery algorithm to forecast access patterns and predict the future temperature. This can timely adjust to hot and cold storage. We validate TempSched on a public dataset, and the experimental results show that it can achieve about 94% hit rate for data access on the edge and device, which is 12% better than existing methods. It can help CED avoid storage overhead caused by storing the full data at all three sides, and greatly reduce data transfer overhead.