Kubernetes – a New Way to Bridge Cloud Infrastructures

As more businesses begin using Cloud computing, the concern over “lock-in” is increasing. For example, if a business chooses to use Amazon Web Services (AWS), the chance is that it will stay with Amazon Cloud for a while, since moving to another Cloud is expensive. If your company is using Office 365, the chance is that you are locked in with the Windows Azure. Your company’s emails and documents are stored in the Microsoft Cloud. This is how Microsoft obtains additional enterprise market with its integrated solution.

A friend told me that if he moved his product from AWS to Azure, it would cost him over $200,000. I replied, “Maybe you should have looked into RightScale, but then you would end up with a dependency on RightScale”.  There are tools like RightScale addressing the lock-in issues, and ultimately you will be locked in with a specific technology or vendor with these tools.

Last week, I attended a Cloud Talk Meetup session. Aja Hammerly, a developer advocate from Google Cloud platform was elaborating on the power of Kubernetes. AppOrbit, a Silicon Valley based start-up, demonstrated their platform that used Kubernetes.  Kubernetes is an open source container orchestration tool. It builds an abstract layer that can be used on top of different Cloud infrastructures. This presents a promising approach to solving the lock-in concerns.

The Kubernetes project was started by Google in 2014 and is now maturing. It is a platform for automating deployment, scaling, and operations of application containers across clusters of hosts. According to Google, Kubernetes provides the following benefits:

  • Microservices: Having a cluster manager enables the management and scalability of smaller parts of an application.
  • Self healing: Auto-placement, auto start, auto-replication and auto-scaling can be performed in the face of failures.
  • Low friction horizontal scaling: Adding more capacity can be achieved easily.
  • High utilization and efficiency rates: Google was able to dramatically increase resource utilization and efficiency after moving to containers. Developers are able to focus more on the service they are building instead of on the underlying infrastructure.
  • Portable: It can be used on top of public, private, hybrid, and multi-cloud.

It seems to me that Kubernetes has the true potential to make Cloud infrastructure interoperable. The key is to design an agnostic data base so that portability can be achieved for various Cloud infrastructures. I invite your comments about this type of database design. What are your experiences with Kubernetes?

[This was originally posted on Linkedin on 6-5-2016]

Deep Learning

It’s a beautiful Friday night in Kirkland, WA. A Mandarin speaking meetup was hosted by the SeattleStartup on the subject of “Deep Learning” at 7:30pm. On my way there, I wondered who’s going to show up and why was Mandarin being used. By 7:40pm, to my surprise, there were 70+ young Chinese professionals in the room; a good number of Microsoft employees mixed with a good number of Amazon employees.

Deeplearning.net summarizes Deep Learning as follows: “Deep learning is a new area of Machine Learning research, which has been introduced with the objective of moving Marching Learning closer to one of its original goals: Artificial Intelligence.

This meetup agenda was as follows:

  1. Dr. Dong Yu, a principle researcher at Microsoft Research, Deep Learning Talk
  2. Panel Discussion about the Deep Learning Application with two AI companies:
  • Kitt AI founder Xuchen Yao and Guoguo Chen. Kitt AI is backed Paul Allen’s AI2, Amazon Alexa Fund and Madrona Venture.
  • Orbeus CEO Yi Li and her team. Orbeus was an AI startup from Silicon Valley in Image Recognition. It was acquired by Amazon and the company has recently moved to Seattle.

Dr. Yu introduced the basic concept of Deep Learning and described the key models such as Deep Neural Networks, Convolutional Neural Networks and Long Short-term Memory Recurrent Networks. He also illustrated the core design principles of Deep Learning models when introducing other new models.

He explained that Deep Learning is feasible only with the computer power and big data available today. It’s actually a rebranding and extension of neural networks. Among the three Deep Learning definitions he gave, the shortest one was “Any system that involves more than one layer of nonlinear processing”

He stated that Deep Learning’s essentials are:

  • Learn complicated feature representation through many layers of nonlinear processing
  • Learn representation automatically and jointly with classification (or whatever) tasks (end-to-end optimization)
  • Key: design the model structure and training criterion.

One of the examples given was AlphaGo; a computer program developed by Google DeepMind in London to play a board game.

AlphaGo’s deep learning is as follows

  • Supervised learning on expert games
  • Reinforcement learning; improve through self-play
  • Build a strategy network and a value network
  • Monte Carlo Tree Search (MCTS) to determine moves through real-time play

Dr. Yu continued to explain Artificial General Intelligence (AGI)

  • The intelligence of a machine that could successfully perform any intellectual task that a human being can (Wikipedia)
  • An emerging field aiming at the building of “thinking machines”; i.e.: General purpose systems with intelligence comparable to that of the human mind (agi-society.org)
  • The general-purpose mechanisms and learning principles that allow machines to explore the world, form connects and clusters, develop and validate theories, learn and generalize from a small number of examples and reason and plan with uncertainty.

The Panel Discussion was quite interesting. In order to learn these two startup’s journey and products, the audience was very actively asking questions and obtaining information. Since these are Chinese startups, it seems using native language Mandarin is a natural way to network and brain storm. That answered my question: why Mandarin.

Personally, I was very happy to have the opportunity to learn about Deep Learning and was impressed by the positive energy brought by these young professionals. I see entrepreneurship emerging among the corporate employees and that’s a good sign. My next blog will explain my perspective about the entrepreneurial culture in corporation. Stay tuned!

[This was originally posted on Linkedin on 5-3-16]

Wireless – A Vibrant and Growing Marketplace

Wireless is a vibrant world. I have been in this industry since 1992 and have never been bored with its rapid development.

My dad is 88 year old. Last year, I gave him my iPhone 4S so that we could do FaceTime. It has been a good experience which has made communication easier. Last month, his car was hit by another car. When I was notified, I rushed to the scene. He showed me the pictures he took with his phone. I was surprised that he remembered how to use the phone camera. Cell phones surely become a part of our everyday life.

My friend’s teen has ADD (Attention Deficit Disorder). He lost 3 trumpets this school year, but he didn’t lose his cell phone. We joked about it as I recalled that none of my three kids ever lost their phones either. Apparently, they were all intimately attached to their cell phones.

CTIA posted an analytics report last week that revealed some interesting data:

  • In 2014, the wireless industry generated $194.8 billion of domestic economic value (excluding imports and exports) in the US, up 34% from 2011.
  • In 2014, the wireless industry generated $282.1 billion in US GDP, up 44% from 2011.
  • The overall annual wireless consumer surplus in the US today is $640.9 billion.

There are 7 distinct groups of players in the industry according to the report:

  • Manufacturers that create, engineer and manufacture the devices
  • Wireless operators that sell and deliver services to users
  •  Retailers and third-party dealers that brings products to the public
  • Ad agencies that market products and services
  • Suppliers of equipment and services that provide hardware and know how
  • App providers that create a variety of apps engineered just for mobile devices
  • The on-demand economy

The wireless industry accounts directly for more than 4.6 million jobs and an induced employment of more than 7.0 million using the multiplier effect.

If you are interested in investing in the industry, check out this report “The Wireless Industry: Revisiting Spectrum, The Essential Engine of US Economic growth”  and familiarize yourself with the capacity and impact of this industry. If you are in your MBA program, the value chain model covered in the report could be an interesting case study.

[This was originally posted on Linkedin on 4-15-16]