Archive for the ‘Systems Engineering’ Category

Micro Data Centers – Independence Day for AOL?

Saturday, July 7th, 2012

Even as I learn to become a modern technologist, the field of computer science is changing constantly. I don’t even know what a modern technologist is, but it is a good enough word because in some ways I am already an archaic technologist (goodbye aerospace!). Since I want to keep eating and stay comfortably sheltered and clothed until I die I have to modernize. To me that means skill acquisition in computer science; skills which I will hybridize with systems engineering (the still useful, repurpose-able part of what I used to do). Still, things change fast.

No sooner than I got comfortable with the idea of the cloud as a new paradigm enabled by vast power-hungry data centers, did I just learn today that AOL is turning that concept on its head as well.

As described in his own blog post AOL’s Michael Manos relates that when he got to the company he initiated a deep review of all aspect of operations. I found his comments on what that review entailed to be very interesting in and of themselves. Basically the review sounded to me like an old-shcool systems engineering analysis of their operation. The result was a Technology Roadmap that contained three components. The first component dealt with internal efficiencies, the second, technical challenges, and the third was an aggressive wish list of game changing technical goals. That third group was referred to as “‘Nibiru’ after a mythical planet that s said to cross our solar system that wreaks havoc and brings about great change.”

Their primary Nibiru goal was to develop a data center environment that did not need a building. Their driving requirements for this was minimal physical touch. This would give them great flexibility in how they will deliver ther products and services. Their result was the Micro Data Center. Attributes of this product include:

  • new technology suite
  • deploy-ability to “anywhere in the world with minimal to no staffing”
  • extremely dense compute capacity (for longest possible use once deployed)
  • deploy-ability anywhere, regardless of temperature and humidity conditions
  • ability to support/maintain/administer, remotely
  • fits within power envelope of any ‘normal building’
  • interoperability within the AOL cloud environment and capabilities

AOL claims to have accomplished all of this and declared Independence Day on July 4th 2012, having successfully tested this in the field near Dulles airport in Virginia.

Bottom line for AOL and why this is such a game changer for them is that they can “have an incredible geo-distributed capacity at very low cost point in terms of upfront capital and ongoing operational expense.”

Manos’s post contains much more information about advantages and future implications for this breakthrough. As for me it is interesting to watch changes develop in the field even as I am learning it at a fast rate myself.

Independence Day, indeed; for both AOL and me!

not a micro data center

Not a Micro Data Center – just old Towers in my Garage!


Best Robotic Legs Ever?

Friday, July 6th, 2012

A very interesting development has been reported by the Daily Disruption News Desk regarding robotic legs that are claimed to “fully model walking in a biologically accurate manner.” This will come as good news for spinal cord injury patients. Those of us who follow developments in artificial intelligence and robotics will likely take note as well.

I read this account with fascination, and immediately wanted to sketch out my understanding in model form. Extending the colloquialism, to a hammer, everything is a nail – to a systems engineer, everything must be modeled. As conveyed in the article, human walking is controlled by a neural network called the central pattern generator (CPG), which is anatomically located in the lumbar region. It’s purpose is to generate rhythmic muscle signals. The researchers said in its simplest form the CPG can be modeled by a neuron pair that each fire signals in alternating fashion.

To complete this model in addition to the neural architecture, the robot needs muscle-skeleton and sensory feedback components. Roughly, this system can be modeled as shown:

I could be wrong, but this is how I understand the Robotic Leg System!

Co-author of the study Dr Theresa Klein was quoted as saying “…we were able to produce a walking gait, without balance, which mimicked human walking with only a simple half-centre controlling the hips and a set of reflex responses controlling the lower limb.”

So, did you catch that? That was quite a surprising statement. Two things are totally counter intuitive to me. First, she said the robot works “without balance.” Does that mean that this robot does not need an “inner ear” to balance? Second, the CPG apparently apparently converts coarse motor commands into forces applied at the hip joint only. The “dangling part of the leg, the lower limb, just follows reflexively, implementing easily programmable commands that simple follow what is happening up stream at the hip.

Another implication of this analysis is that the brain proper plays less of a role in controlling gait that I would have guessed.

This would be a good time to confess that I could be totally wrong in my interpretation of this research and its result; I am learning as I go.

Speaking of which, the CPG model of this study is apparently a good facsimile of how gait is refined from early childhood steps through later improvement during the maturing process. The CPG in humans gets better over time as it learns the best walk to walk by repetition.

This is exciting as I can see similarities between this system and what I am learning in my artificial intelligence class. The evolving understanding of complex bio-mechanical systems as well as advances in AI make this a great time to be a student of such things.