This blog explains how we configured and use Grafana to graph and visualize PDU data stored in InfluxDB.
Background InfluxDB is a time series database that we use to store dozens of metrics from all of our power distribution units (PDUs). This database is updated periodically with a python script that parses snmp data from our PDUs and stores it in the correct table and row. After getting the data into the db, we used Grafana to create graphs of some of the power consumption metrics so that we could easily see how much power was being used by each PDU.
Virtual Desktops are a topic that runs hot and cold through the enterprise space. Many enterprises begin pilots and projects in the space before building a clearly defined plan, and these are the environments that need additional remediation down the road. Before we start in with architecture, getting a firm answer to “Why do we need them?” is critical to a successfully scope, cost, and validate the design. This month we will focus on the security use case which is one of the ‘easy wins’ for virtual end user compute.
In this second installment of a two-part series, we’ll be going over Phase Two, the build out of standard pre- and post-patching automation, and Phase Three, the build out of application-specific pre- and post-patching automation. Click here for Phase One.
Status Report With basic patching and reboots automated, a patching session for application environments without any special pre-patching and post-patching activities was reduced from 20 minutes per server, down to 6 minutes per server.