Moogsoft raises $11.3 million for continuous availability through DevOps
For some time now, the DevOps discussion has been dominated by the paradigm of continuous delivery—develop, test, deploy in an uninterrupted cycle—but Moogsoft Inc. believes that the next stage of DevOps is pushing into continuous availability. The position that DevOps holds in the industry is surrounded by services, cloud and mobile and this means services need to have maximum uptime to keep up with customer needs.
Moogsoft announced today the closing of an $11.3 million Series B funding round led by new investor, Wing Venture Capital, in addition to existing investors including Redpoint Ventures. The company intends to use this funding round to expand technological innovation around its collaborative DevOps products as well as fuel geographic expansion.
As a company, Moogsoft is more than just an interesting team and a cow-themed logo (replete a website featuring a barn, weather-vane, and windmill) as behind the scenes that team is working towards innovating the next big paradigm for DevOps. To help companies provide continuous availability, Moogsoft’s product combines the best of Application Performance Monitoring with social collaboration and leverages the power of Machine Learning.
The flagship product of Moogsoft, Incident.MOOG, delivers collaborative situational awareness using all of the above methods to IT Operations Management (ITOM) and IT Service Management (ITSM) teams.
Enabling the collaborative social side with the “situation room”
When something goes wrong, crisis control teams spring into motion. Many businesses don’t have a specific team for crisis management, but instead pull together numerous actors from across the different teams who work on a particular project. Incident.MOOG’s collaborative “situation room” simplifies this project by identifying the people needed to understand and handle a potential problem and pulls them into a collaborative chat.
To the Moogsoft team this is called the “situation room,” a virtual space where every participant can see the same data, the context that Incident.MOOG has given it, and can communicate freely about what they see on their end and how the problem can be managed.
Ideally, Incident.MOOG should be able to detect problems before they actually happen, allowing teams to quickly produce a stopgap solution before a failure; but it also places a lot of information in the hands of crisis management at time of failure leading to swifter resolution of problems.
Leveraging Machine Learning to provide context to “situations”
In Incident.MOOG a potential or current problem is dubbed a “situation.” These situations can arise from any number of complex interacting systems that are common to every IT solution in the enterprise and most business ventures produce more than 200 million events a day.
Often, ninety-nine percent of which are totally mundane and can be discarded. The old model of operations used rigid models and rules to decide what to keep and what to discard. However, with the increasingly dynamic and complex nature of infrastructure these solutions are falling behind.
To stay ahead of the curve Incident.MOOG uses Machine Learning to adaptively learn from the system by watching it in normal operation and highlighting anomalies as they arise. Combined with the collaborative “situation room” the platform is capable of not just detecting anomalies in normal operation, but can also provide a context for anomalies by referring back to previous events and situations.
Bringing DevOps to continuous availability
According to Moogsoft executives, the majority of Moogsoft customers have already embraced the DevOps movement and deploy many of the practices throughout their operations.
DevOps practices are currently making IT more agile and bringing collaborative techniques along with it providing for a swifter lifecycle for software. Moogsoft believes in the paradigm of pushing the DevOps philosophy of unbroken continuity of coverage also into availability and believes it can do that through collaboration, context awareness, and Machine Learning to escape from strict rules and rigid models.
There’s huge focus on continuous delivery in DevOps; the next phase is about continuous availability, and that’s really what Moogsoft is all about.
Top image courtesy of Moogsoft: http://www.moogsoft.com
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