Even with all of the new and exciting product updates, our development team somehow finds the time to outdo themselves. I'm pleased to be able to share with you an early beta of one of our most exciting projects to date: code-named "DPI."
With the ability to sniff the wire and analyze packet-traffic, DPI provides real observed network response time (NRT) and application response time (ART.) In addition, DPI has the ability to classify and categorize ~1300 different applications by associated purpose and risk-level.
Let's take a look at a few of the data points we will start to capture:
Network Response Time (NRT)
Is the problem the application or the network? Now you'll be able to prove your pipes are pristine and start to focus on the troublesome app server:
Application Response Time (ART)
The opposite end of the network problem- how long did it take to receive the first byte for a response? Good insight into the Quality of Experience that your users perceive...
Sure, with NPM interface statistics or Netflow, you can determine how much of your pipe is being filled, and potentially by whom. However, unless you are able to stay on top of the latest in social apps and malware, who may not realize what you should be looking for.
Our list of ~1300 pre-defined applications makes this easy- whether you are looking for Exchange traffic, or Torrent:
Very likely this technology would begin to surface in a NPM release in the near future- so stay tuned.
For beta purposes, we're limited to just gathering data from a packet filter driver on the Orion server itself, but we have a few ideas on how we can scale that out.
Bear in mind, we're not yet storing any captured data, but rather just analyzing and discarding. Storage would potentially come later.
Interested? Sign up here: DPI Beta Survey
Already signed up, or want to learn more? Join our DPI Beta forum
PLEASE NOTE: We are working on these items based on this priority order, but this is NOT a commitment that all of these enhancements will make the next release. We are working on a number of other smaller features in parallel. If you have comments or questions on any of these items (e.g. how would it work?) please let us know!