As we’ve discussed in a previous blog post, it can be difficult to determine whether or not a vendor is truly as open as they claim to be. Sure, your network provider says they support open solutions, but the reality is they take advantage of open networking’s flexible definition to market not-so-flexible networks. How can you be certain that you’re investing in the open network your data center deserves?
Never fear, Gartner is here! Their report on gauging vendors’ openness provides you with five easy questions to help you take a machete to the forest of false advertisement. So, how does Cumulus Networks stack up to these requirements, and how have we maintained our dedication to open networking? Let’s take a look at Gartner’s criteria — we think you’ll find that this open model fits Cumulus like a glove.
We’ve broken down the qualifications for a simple solution into three parts. First of all, managing your network should be easy from the moment it comes online. Cumulus Linux is an operating system that ensures a simple start. A few features that guarantee a confusion-free beginning include:
It supports cloud storage at AWS, Microsoft, and Google, as well as object and file storage.
Tests indicate 5G won’t require as much capex as expected.
Deepak responded to my video on network commodization with a question:
What’s your thoughts on how Network Design itself can be Automated and validated. Also from Intent based Networking at some stage Network should re-look into itself and adjust to meet design goals or best practices or alternatively suggest the design itself in green field situation for example. APSTRA seems to be moving into this direction.
The answer to this question, as always, is—how many balloons fit in a bag? I think it depends on what you mean when you use the term design. If we are talking about the overlay, or traffic engineering, or even quality of service, I think we will see a rising trend towards using machine learning in network environments to help solve those problems. I am not convinced machine learning can solve these problems, in the sense of leaving humans out of the loop, but humans could set the parameters up, let the neural network learn the flows, and then let the machine adjust things over time. I tend to think this kind of work will be pretty narrow for a long time to come.
There will be stumbling blocks here that need to be Continue reading
Arpit Joshipura serves as executive director of LFN.
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Telstra Ventures, the investment arm of the Australian teleco, led the round.