One of the things that tends to hold back adoption of DevOps are all the low-level events that need to be correlated by an engineer to generate anything that approaches actionable intelligence (AI). To eliminate that requirement, Loom Systems created a Loom AIops platform to automate many of the tasks that normally would need to be performed by a DevOps engineer.
Loom Systems has upgraded the Loom AIops platform by adding Distributed Entity Tracing, which makes it easier to analyze events across a range of interdependent applications and associated middleware. In addition, Loom has created Correlations View, which simplifies identifying how alerts being generated are related to one another.
Finally, the latest version of AIops adds support for the OpenStack cloud management framework, in addition to sporting a revamped set of dashboards that are easier to navigate.
Dror Mann, vice president of product for Loom Systems, says one of the reasons that DevOps is not widely implemented is that today it requires IT staffs to accumulate and correlate massive amounts of low-level event data to create anything approaching actionable intelligence. Loom AIops is designed to automate the low-level tasks relating to DevOps to enable the IT staff to focus more of their time and effort on fixing issues and reducing the time required to release and update applications.
Loom AIops, says Mann, accomplishes that goal by first streaming all the application log data into its platform, followed by all the log data generated by any dependencies that application might have. Loom AIops then applies machine learning algorithms in real time to identify the root cause of an issue. Armed with that information, it becomes a lot easier to reduce the mean time to resolution (MTR), which then provides the IT organization with the time needed to address more complex management challenges, he says.
Mann notes the single biggest challenge IT organizations encounter with DevOps is that it requires them to change their modus operandi. By automating many of the lower-level DevOps tasks, there’s more time available for IT organizations to think through the cultural changes required to successfully implemented integrated DevOps processes.
Most IT professionals will view the application of AI to IT operations with a combination of skepticism and trepidation. It will be difficult initially for many IT professionals to trust the analysis being generated by AI software. Once they do begin to trust that analysis, there naturally will be some concerns over to what degree an AI program can eliminate the need for a DevOps engineer altogether. But, Mann notes, there are many complex tasks that never get addressed because most DevOps engineers spend an inordinate amount of time and effort trying to correlate the right data.
At this juncture, usage of AI within the context of DevOps is now all but inevitable. The challenge now is not figuring out the line between where humans add higher orders of value compared to machines that are generally going to be limited to identifying patterns. The advantage a machine has it that once it learns something, it doesn’t forget it, take days off or switch jobs. However, being able to identify a pattern is not nearly the same as being able to apply reasoning to what the sum total of those patterns really means for the business.