Enterprises are no strangers to the idea that solving problems creates new problems. New forms of application performance monitoring for cloud-based applications, for example, create a tangled mess of logs, anomalies and alerts to constantly track. Moogsoft hopes to bring algorithmic analysis to the ever increasing problem of information overload. The company’s flagship IT operations analytics (ITOA) platform will get a facelift with the release of version 6, to be released April 12.
The big changes are focused on molding the Moogsoft workflow into modern enterprise, Kanban-style environments.
Behind this update is an increasing relevance for Moogsoft within the ITOA space. The company was co-founded by serial tech entrepreneur, Mike Silvey. Today, he is Executive Vice President at Moogsoft. In a past life, he co-founded Micromuse, a company IBM purchased and integrated into its Tivoli line.
Silvey said that IT organizations currently have trouble parsing all the data coming in. Between application performance monitoring (APM) tools like AppDynamics and NewRelic, larger system log aggregation tools like Splunk, and the thousand other systems that come in between those assets, it can be easy, said Silvey, to lose the overall performance and anomaly forest in the trees.
Instead of expecting IT workers to notice trends across multiple monitoring systems, Silvey said that it makes much more sense to observe these streams of information algorithmically. While this idea isn’t necessarily new, the novelty is in the real-time speed of the system, and in the removal of rules configuration.
“This is the big innovation at Moogsoft,” said Silvey. “Traditionally, you’d have to say, ‘when this gets below this level, that causes an incident.’ Traditionally you write rules. The problem is that with [continuous deployment], the frequency means you can’t write enough rules fast enough.”
Silvey said that Moogsoft has built up the ability to perform things like negative matrix factorizations very quickly, and this enables the immediate surfacing of anomalous trends as they occur.
Senior Analyst for Application and Infrastructure Performance Development at 451 Research, Nancy Gohring sees the world as having entered the era of ITOA 2.0. The first iteration of these products, she said, were mostly from legacy players, and worked only within their own data domains.
Newer tools, she said, are focusing on machine learning and algorithmic analysis of the incoming log files generated by the myriad tools out there in cloud infrastructures. This, she said, means the current crop of ITOA tools are actually able to impact operations effectiveness, implying that previous generations couldn’t meet user demands and needs.
“The first ITOA tools were mostly delivered by legacy vendors, and often restricted to data that was collected by that vendor,” said Gohring. “Their version of correlation was a dashboard with tools so you could visually correlate them. These weren’t very useful, so they didn’t get used very widely.”
Silvey said the benefit of this new wave of ITOA tools, among which Moogsoft is a part, is to make it clearer what problems are surfacing where, and how to find a path to fixing them. He touted the huge reduction in actionable ticketing and compressed time to remediation as the major benefits of Moogsoft.
That’s good because Gohring said that Facebook is currently capturing 26 trillion data points every single day. That volume of information cannot possibly be comprehended by a human being, much less in real time.
For more of Gohring’s thoughts on ITOA, check out our recent TNS Analysts podcast: