Zenoss Delivers Full-Stack Monitoring + AIOps
AIOps stands for artificial intelligence for IT operations. At a high level, AIOps tools do two things — they collect data and they analyze data — in the interest of accelerating problem resolution in IT operations.
The original (Generation 1) tools do this by performing analysis on mass quantities of event data and inferring probable root causes based on data analyzed from previous similar issues. They were not designed to tell you how issues with a given system may affect IT services or applications. Those AIOps tools rely solely on processed event data. The strengths of such tools are in the analytics, while the key shortcomings are associated with the blind spots that come from having limited visibility into metrics, dependency data and other types of machine data.
Zenoss is a Generation 2 AIOps platform — it combines full-stack monitoring with analytics powered by machine learning. This means eliminating the number one problem AIOps tools have experienced thus far — limited visibility and context due to the lack of cardinality in the data they're analyzing. Zenoss delivers a new level of analytics capabilities because it is processing all data types, including metrics, dependency data, events, logs and streaming data. This provides unprecedented context and unprecedented acceleration of problem resolution.
Observability is a term from control theory that has been borrowed, and often misused, by vendors selling software for IT Ops. From Wikipedia: "In control theory, observability is a measure of how well internal states of a system can be inferred by knowledge of its external outputs."
In the context of IT Ops, some software vendors will try to convince you that observability means you can understand what's happening on the inside of the system by simply observing the outside of the system. Why do (Generation 1 AIOps) software vendors want you to believe this? Because collecting machine data is hard, and it has become much harder with the complexity and dynamic nature of modern IT systems. Collecting events is easy. So in an ideal world, a software tool just waits for systems to send events, then analyzes them, then magically spits out insights that resolve problems. This is what those vendors set out to do. The desire to have such an easy solution somehow allows us to look past the absurdity of it.
This is akin to saying you don't need gauges or diagnostic codes from your car — you'll know there’s a problem when it stops running. You might see the check engine light is on, and you'll magically know what the problem is because you can observe the "outside of the system." Apply this to software applications — when the application stops working, you just examine the events and the previous times the application stopped working, and you're supposed to be able to infer exactly what the problem is. The bottom line is that those solutions have unsurprisingly failed to produce results.
When you incorporate the difficult component of collecting all types of data from those systems, you can feed the machine learning algorithms topology/dependency information as well as information on how those systems are performing internally. Combine the power of monitoring and observability — it is only then that you can derive precise insights that are so badly needed in today's complex IT environments.
Already have an AIOps tool? That's OK! Zenoss integrates with Generation 1 AIOps vendors, providing needed, rich datasets they can’t collect on their own. The net result is definitive — dynamic Zenoss IT service modeling enriching analyzed event data from existing tools.