Eclipse Che is a developer workspace server and cloud IDE. With Che, you can define a workspace with the project code files and all of their dependencies necessary to edit, build, run, and debug them. You can share your workspaces with other team members. And Che drives Codenvy, cloud workspaces for development teams, with access control and other features.
Today in the keynote at CheConf 2016, Tyler Jewell made several Docker related announcements.
One interesting trend of the last year or two is the rising use of data analytics and ANI (Artificial Narrow Intelligence) in solving network engineering problems. Several ideas (and/or solutions) were presented this year at the IETF meeting in Seoul; this post takes a look at one of these. To lay the groundwork, botnets are often controlled through a set of domain names registered just for this purpose. In the same way, domain names are often registered just to provide a base for sending bulk mail (SPAM), phishing attacks, etc. It might be nice for registrars to make some attempt to remove such domains abused for malicious activities, but it’s difficult to know what “normal” activity might look like, or for the registrar to even track the usage of a particular domain to detect malicious activity. One of the papers presented in the Software Defined Network Research Group (SDNRG) addresses this problem directly.
The first problem is actually collecting enough information to analyze in a useful way. DNS servers, even top level domain (TLD) servers collect a huge amount of data—much more than most engineers might suspect. In fact, the DNS system is one of those vast sources of information Continue reading
IoT keeps getting smarter.
The carrier wants to get the jump on non-standalone New Radio (NR).
This is one of many acquisitions Verizon has made to fortify its IoT business and smart city efforts.
Nokia pats itself on the back for outperforming Ericsson.
Deep learning and machine learning are major themes at this year’s annual Supercomputing Conference (SC16), both in terms of vendors showcasing systems that are a fit for both high performance computing and machine learning, and in the revelation of new efforts to combine traditional simulations with neural networks for greater efficiency and insight.
We have already described this momentum in the context of announcements from supercomputer makers like Cray, which just unveiled a Pascal GPU-based addition to their modeling and simulation-oriented XC supercomputer line, complete with deep learning frameworks integrated into the stack. The question was, how many HPC workloads …
A Deep Learning Supercomputer Approach to Cancer Research was written by Nicole Hemsoth at The Next Platform.
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