Network traffic monitoring with SNMP



June 4, 2020

In these days with a lot of focus on network traffic and capacity, we decided to quickly develop a SNMP agent that can monitor individual ports on switches and routers. Both inbound and outbound port traffic is covered on per minute resolution. This first version covers up to 24 ports but we can quickly expand this to support 48 ports on request.

The agent is running on Linux / Unix and is tested on a variety of Linux implementations including Raspbian for Raspberry Pi. Simply register the agent with a configurable bash script, then register the agent bash script as a cron task and make it run per minute. 

As with any other agent connected to AIMS, machine learning will be applied to each parameter to create baselines, and anomaly detection paired with correlation will pinpoint issues and highlight impacts.

You can of course pair network traffic with application performance, so that the anomaly detection will work across the two layers.

Screenshot 2020-03-22 at 17.09.15

Since the agent is script based, you can easily modify it to fit your needs, or use it as a baseline to create other custom agents. 

With the AIMS Community Edition you can monitor network traffic with AI for free.

Topics from this blog: Technical

RECENT ARTICLES

Technical

BizTalk: Undiscovered secret of DTA purge and Archive stored procedure

DTA Database is one of the most important databases in the BizTalk DB component. The issue in this database can cause a lot of performance issues in BizTalk. To keep the DB healthy, Microsoft...
Technical

Recovery of Master Secret Server

As a part of the recovery process during DR (Disaster Recovery) or any other unforeseen scenarios, one might have to restore the master secret to re-use the existing setup and data. This article will...
aiops

AIMS teams up with the Norwegian Computing Center to make predictions to alert of a possible problem even before the problem itself arises

At AIMS, we strive to alert as early as possible that some problem is arising for a business-critical system. We want to arrive as far as to make predictions to alert of a possible problem even...