Last Friday the popular DNS service Dyn suffered three waves of DDoS attacks that affected users first on the East Coast of the US, and later users worldwide. Popular websites, some of which are also Cloudflare customers, were inaccessible. Although Cloudflare was not attacked, joint Dyn/Cloudflare customers were affected.
Almost as soon as Dyn came under attack we noticed a sudden jump in DNS errors on our edge machines and alerted our SRE and support teams that Dyn was in trouble. Support was ready to help joint customers and we began looking in detail at the effect the Dyn outage was having on our systems.
An immediate concern internally was that since our DNS servers were unable to reach Dyn they would be consuming resources waiting on timeouts and retrying. The first question I asked the DNS team was: “Are we seeing increased DNS response latency?” rapidly followed by “If this gets worse are we likely to?”. Happily, the response to both those questions (after the team analyzed the situation) was no.
CC BY-SA 2.0 image by tracyshaun
However, that didn’t mean we had nothing to do. Operating a large scale system like Cloudflare that Continue reading
The IEEE standard was approved at lightning speed. What's next?
Here are three things to think about once you've got a software-defined network up and running.
One of my readers left this comment (slightly rephrased) on my Network Automation RFP Requirements blog post:
Given that we look up to our *nix pioneers as standard bearers for system automation, why do we demand an API from network devices? The API requirement would make sense if the vendor OS is a closed system. If an open system vendor creates APIs for applications running on their system (say for BGP configs) - kudos to them, but I no longer think that should be mandated.
He’s right - API is not a mandatory prerequisite for reliable network automation.
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Two former EVPs will run the new Products and Cloud group.
For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools.
To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a …
The State of Enterprise Machine Learning was written by Nicole Hemsoth at The Next Platform.
For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools.
To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a …
The State of Enterprise Machine Learning was written by Nicole Hemsoth at The Next Platform.