Effective Management of Time Series Data

Cloud computing systems, consisting of numerous nodes and components, require constant monitoring to satisfy the Quality-of-Service (QoS), making the management of large-scale time series data challenging. To address this issue, age threshold retention policies have been implemented to remove historical data, but this eliminates valuable information from older periods. In this paper, we proposed an alternative approach that applies time series deduplication with metric-based tolerance to discard readings that stabilize within a calculated tolerance window. This approach can reduce the data volume by 70.38% on average. Once the data-reduced interval is queried, the readings can be reconstructed to retrieve the original granularity with low query runtime overhead and a Mean Absolute Percentage Error of 0.74%.