Worth Reading: The History of the URL
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The post Worth Reading: The History of the URL appeared first on 'net work.
ABI predicts machine-learning data analytics tools and services will be a $20 billion business by 2021.
With this summer’s announcement of China’s dramatic shattering of top supercomputing performance numbers using ten million relatively simple cores, there is a slight shift in how some are considering the future of the world’s fastest, largest systems.
While one approach, which will be seeing with the pre-exascale machines at the national labs in the United States, is to build complex systems based on sophisticated cores (with a focus on balance in terms of memory) the Chinese approach with the top Sunway TaihuLight machine, which is based on lighter weight, simple, and cheap components and using those in volume, has …
Changing the Exascale Efficiency Narrative at Memory Start Point was written by Nicole Hemsoth at The Next Platform.
In the first post we covered a bit of the basics around segment routing in the data center. Let’s return to the first use case to see if we can figure out how we’d actually implement the type of traffic steering needed to segregate mouse and elephant flows. Let’s return to our fabric and traffic flows and think about how we could shape traffic using segment routing.
There are two obvious ways to shape traffic in this way—
The first way would be to impose a label stack that forces traffic along a path that touches, or passes through, each of the devices along the path. In this case, that would mean imposing a path on the traffic originating behind the ToR at A so it must pass through [F,G,D,E]. The flow of traffic through the data center will look something like—