Operators now possess a vast amount of network data, but they are becoming increasingly uncertain about the happenings across intricate infrastructures. The abundance of data does not inherently provide extensive visibility. Traditional monitoring gathers important network data, yet crucial elements of the infrastructure’s actual activity may still be overlooked.
In a conventional data center, network monitoring usually depends on aggregated metrics, sampled packets and flows, SNMP polling, which involves an NMS extracting data at regular intervals, and control plane data. As Sebastian Müller, the head of sales at ipoque, a Rohde & Schwarz company, puts it:.
This offers just parts of the complete image. These tools commonly watch over bandwidth usage, interface conditions, and fundamental protocol wellbeing at a high level. In the past, for older systems, this was usually enough due to the predictable nature of network behavior.
Contemporary establishments are vastly dissimilar. With the growing use of AI and HPC applications, network activity is getting more volatile, resulting in abrupt changes in traffic patterns throughout the infrastructure. Although aggregated metrics and sampled flows can indicate the occurrence of events, they fail to provide insights into the underlying causes, the reasons behind them, or the parties involved.
