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Frank Frankovsky, VP of Hardware Design and Supply Chain is keynoting the Open Compute Summit.
One of his points is how plastic bezels should disappear. The bezel impedes the air flow and is a waste when it gets added to the waste stream.
Do you think the world is better with pretty bezels? Hopefully most of you say no.
I'll get a chance to look at more of the hardware as the day goes on.
Will the data center server room start to look more like an industrial area than an IT showcase?
I am at the Open Compute Summit and to start off the event is sold out.
500 people are here. 500!
This event as big as a conference. I've run into so many people here that I run into at other conferences.
Watch the livestream to see what is being presented https://www.facebook.com/Engineering?sk=app_260691170608423
The Open Compute Project is having their third summit May 2-3. It is sold out.
If you are not already registered you can watch the event on Livestream.
If you can’t attend the summit, watch the keynote on the livestream this Wednesday at 9AM CDT.
Schedule
- May 2, 2012
- 8:00am - 9:00am
- Registration
- 9:00am - 12:00pm
- Keynote presentations from Frank Frankovsky and others from the Open Compute Project community
- 12:00pm - 2:00pm
- Lunch and Exhibit Floor
- 2:00pm - 5:00pm
- Technical workshops (round 1), open sessions, and educational sessions
- 5:00pm - 6:00pm
- Travel to party venue
- 6:00pm - 10:00pm
- Open Compute Party
- May 3, 2012
- 9:00am - 12:00pm
- Technical workshops (round 2) and open sessions
- 12:00pm - 2:00pm
- Lunch and Exhibit Floor
- 2:00pm - 3:00pm
- Plenary: a synthesis of each workshop and next steps
Open Compute Projects Technical Workshops
- Open Rack
- Mechanical design and modular power distribution
- Storage
- New Hardware and building for the 100 year standard
- Virtual IO
- Pushing the limits of connectivity, software and hardware modularity
- Systems Management
- Defining open systems management for the enterprise
- Datacenter Design
- New initiatives; building for different geographies
Additionally, running in parallel to the technical workshops there will be a series of Open Sessions ---these will be a combination of informative sessions educating people on how the OCP has enabled the ecosystem to both adopt and contribute to the technology stack and how to think about web-scale technology design at scale.
Here are the event sponsors.
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GigaOm's Katie Fehrenbacher reports on Apple confirming Bloom Energy is fuel cell supplier.
Apple is (finally) confirmed as Bloom Energy’s customer
Last month I exclusively reported that Apple was buying fuel cells from Bloom Energy for its data center in Maiden, North Carolina. However at the time neither company would confirm the deal. Well, on Monday morning Bloom Energy has finally confirmed that yes, it is supplying fuel cells for Apple’s data center,reports CNET.
CNET is referenced on this.
I've been waiting for someone to make the simple point that when you look at the specifications in the Fuel Cell permit, the performance numbers in 5 different areas exactly match the specifications on Bloom's Fuel Cells.
Years ago, I worked on a closed loop monitoring system, and one of the parts of the design was an analysis of the data that was considered not relevant, looking for data that is mistakenly discarded.
A friend sent me CIA publication that discusses catching the outlier ideas, the stuff that gets discarded.
Hunting for Foxes
Capturing the Potential of Outlier Ideas in the
Intelligence Community
Clint Watts and John E. Brennan
Outlier:
—A data point far outside the
norm for a variable or population;
—An observation that “deviates so much from other
observations as to arouse
suspicions that it was generated by a different mechanism”;
—A value that is “dubious in
the eyes of the researcher”;
—A contaminant.
Source: J. Osborne, “The Power of outliersIn war you will generally
find that the enemy has at
any time three courses of
action open to him. Of
those three, he will invariably choose the fourth.
—Helmuth Von Moltke
Here is the main point made.
Of all the examinations of
intelligence surprise and failure, Richards Heuer provides
perhaps the most succinct characterization of the problem:
Major intelligence failures are usually caused
by failures of analysis, not
failures of collection. Relevant information is
discounted, misinterpreted, ignored, rejected,
or overlooked because it
fails to fit a prevailing
mental model or mind-set.
The trouble about filters is you filter out good data with the bad data.
Which brings up the issue of Outliers.
Outlier:
—A data point far outside the
norm for a variable or population;
—An observation that “deviates so much from other
observations as to arouse
suspicions that it was generated by a different mechanism”;
—A value that is “dubious in
the eyes of the researcher”;
—A contaminant.