In Ethernet Summit 2014, Alan Weckel of the Dell’Oro Group showed a very interesting chart on projections for server adoption. Due to copyright issues, I’d summarize the info as follows:

In 2013, cloud and server providers account for ~20% server unit shipments, by 2018, this group of customers is forecasted to account for up to 50% of server unit shipments. If this trend continues, the there would no growth to server shipments to enterprise customers.

Since servers account for part of the data center, the implication is that both networking and storage gear would move this way as well. Cloud and SP are significantly changing the data center equipment market.

Another interest point, 2 players dominate in the cloud, Google and Amazon, while Facebook could be an up-and-comer. These players design their own data center equipment and directly work with ODMs to manufacture their own equipment. It would take some hard maneuvers for an IT equipment vendor to get into these accounts. HP is trying such as maneuver: creating low-cost entry servers in partnership in Foxconn. Time will tell whether this would work.


Data is the New Oil



This cartoon was posted on the screen at a recent BigData Guru meetup. “Data is the new oil.” While this concept was first stated in 2006, it’s still very relevant today, especially with explosion of the internet, mobile, IoT, and the multitude of tools for people to generate content and to enable transactions of all types. IBM indicated in 2013 that 90% of the world’s data was generated in the last 2 years.

The premise is that much of data from email, tweets, IM, Facebook posts, Google searches, Amazon purchases, Ebay transactions, mobile and IoT sensor data, retail transactions could be mined for value, analogous to crude oil being processed and refined into gasoline and other ingredients to make plastic and other materials. The value of the data could result in better targeting of customers to buy the right products, in better understanding of the environment from sensor data to increase productivity, such as increasing crop production or solar energy yield. The possibilities seem endless.

All the while, both the companies extracting the value from the data and the vendors producing tools and equipment (e.g. data center servers and storage) to enable this extraction are being rewarded handsomely with $$$. Data is the new oil.


This is the claim by Nvidia CEO Jen-Hsun Huang, that 3 Nvidia Titan Z CUDA based GPU cards could offer the same performance in running deep learning neural nets as done in the Google Brian project using Intel processors.

  • 1/150 the acquisition cost reduction
  • 1/150 heat consumption

If this could be done in general for deep learning type problems, we could have many more machines to do machine learning on the explosion of data. At the same time, to use the CUDA cores, software programmers would need to learn program this hardware and / or use OpenCL. The cost savings could warrant pushing over the learning curve.

This paper is referenced: “Deep Learning with COTS HPC Systems” by A. Coates, B. Huval, T. Wang, D. Wu, A. Ng, B. Catanzaro, published on ICML 2013


Google announced Project Tango on February 20, 2014.  It’s a cell phone that captures and reconstructs the environment in 3D, wherever the user points the back cameras. There are 2 cameras, a color imaging camera and a depth camera (or Z-camera), very much like the first generation Kinect. But Project Tango is much more than the Kinect, it performs in real time all the computation of the 3D reconstruction using co-processors from Movidius.

This reminds me of what Dr. Illah Nourbakhsh said in 2007 in the inaugural presentation of the IEEE RAS OEB/SCV/SF Joint Chapter:  that some day, we’d be able to wave a camera and capture the entire 3D image of our environment. Project Tango is just that simple, just aim the cameras to the areas to create the 3D reconstruction.  To complete a room, you’d have to walk around the whole room to capture all the information.

Using SLAM algorithm, aGPS, and orientation sensors, Project Tango is also able to localize the 3D reconstructed image to its location on earth and relative to the location of the device itself.

Project Tango is running a version of Android Jelly Bean, rather than the latest Kitkat release.  What’s more, it apparently is using a PrimeSense sensor, which now is no longer available after Apple’s acquisition of PrimeSense. (Interesting that Google did not push to outbid Apple for PrimeSense. After all, there are plenty of alternative depth sensor technologies out there.) Furthermore, Battery life is very limited. These and other issues will eventually be solved, for real-world deployment.

Applications for real time 3D reconstruction and mapping include augmented reality, architectural design, and many others. Most interesting would be the use in mobile robots to maneuver in the real world.  Just imagine in-door drones, armed with this capability, would be able to move autonomously and safely anywhere in a building, monitoring and transporting items from one location to another.  The applications are endless.

Google has advanced computing technology to enable real interaction with the physical world, by demonstrating the real-time 3D reconstruction and mapping capability in Project Tango.

Andy Feng of Yahoo presented his work on Apache Storm. This picture shows the 3 types of Hadoop 2 processing scenarios:

  • Hadoop batch processing (MapReduce or the newer Tez providing DAG based processing)
  • Spark iterative processing (for machine learning where the algorithms crunch on the same data repeated to minimize some objective function. Spark supports the Directed Acyclic Graph processing model. With the capabilities of Spark, it has drawn increasing interests.
  • Storm stream processing for real time data



This platform presents an “operating system”-like set of functions to manage cluster of compute and storage:

  • HDFS to manage storage
  • YARN to manage compute resources
  • MapReduce/Tez/Storm and Spark to schedule and run tasks

All open source and changing quickly.

How to draw an owl

I attended Ted Blosser‘s presentation on Box’s new metadata API and he showed a great description of the fast-fail approach to developing products, developed by the founder of Twillio:


To draw an owl, just start with 2 ovals that look like an owl, then fill in the details via quick iterations.  Twillio describes this as “There’s no instruction book, it’s ours to draw. Figure it out, ship it and iterate.”

Ted said that while Box’s target customers are enterprises, Box can still approach product releases using this approach.

This applies well to new product development with the goal of getting to the product with features that maximizes customer value and thus revenue, in a series of quick iterations.

I think key to success consists of

(1) starting with a framework (the 2 ovals) that has a high chance of success

(2) having the discipline to identify likely failure of the framework if that occurs, and then abandon the project quickly, not be bothered with the sunk cost.

This past weekend at Code Camp, I saw Yosun Chang‘s presentation on hacking Google Glass. I liked how her slides flow from 10,000 foot view of all the slides and zoomed into each slides. She was using Prezi.

I’ve seen Prezi presentations before but had not the opportunity to use it. I had been using PowerPoint and Keynote.

I have to give a speech at Startup Speakers, and I turned the opportunity into using Prezi. I like how Prezi enforces a structure to the presentation by just a simple graphical layout.

Here’s my 6-minute speech on my excursion on evenings and weekends into 3d printing in the last few years: