A Novel Two-Dimension Adaptive Data Collection Method for Network Management

In this paper, we study the network data collection problem for high-quality status observation and management purposes. Traditional data collection strategy typically uses static configuration, which may not only take too much network resources, but hardly reflect the data change trend accurately. To this end, we propose a novel two-dimension adaptive data collection method (TD-ADCM) according to the network data correlation and variation routines. TD-ADCM selects the data collection content in association with network data variation, and adjusts collection frequency based on the ratio of the data variation amplitude. Theoretical analysis and simulation results show that TD-ADCM significantly decreases the data collection amount and improves the data collection accuracy achieving equivalent network management tasks. As a result, the network resources are more reasonably utilized.