Summary of Google's Anthos

Janakiram MSV has a nice post on Forbes explaining Google’s Anthos. Janakarim starts off with a description of the current state which I totally agree with.

Despite the extensive coverage at Google Cloud Next and, of course, the general availability, the Anthos announcement was confusing. The documentation is sparse, and the service is not fully integrated with the self-service console. Except for the hybrid connectivity and multi-cloud application deployment, not much is known about this new technology from Google.

What is Google’s strategy?

The core theme of Anthos is application modernization. Google envisages a future where all enterprise applications will run on Kubernetes. 

With Anthos, Google wants all your contemporary microservices-based applications (greenfield) in Kubernetes while migrating existing VMs (brownfield) to containers. Applications running in non-x86 architecture and legacy apps will continue to run either in physical or virtual machines.

How does Google Anthos relate to AWS and Azure?

Anthos is a bold move from Google. It is taking a calculated risk in moving away from the clichéd hybrid cloud narrative that its competitors are using to lure enterprises. Anthos is bound to be compared with Microsoft Azure Stack and AWS hybrid story consisting of VMware and Outposts. The fundamental difference between Google and the rest lies in the technology foundation strongly rooted in containers and Kubernetes.

Janakiram did a nice job of putting into one post that can be so hard to figure out what Anthos is.

Joe Kava's data center talk at Google Cloud Next 2019

It’s not viewed that much yet, but sure the numbers will grow. Currently the views are 22. I was at Google Cloud Next, but unfortunately could not make it to the presentation so I have been searching for the video of Joe’s talk so I can write about it and finally found it.

If you are interested in an interactive discussion here is another video taped at the conference.

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Google has a large display illustrating how secure information flow works in google data centers.

Google has a large display illustrating how secure information flow works in google data centers.

Here is one of the Cloud TPUv3 racks shown at the conference.

Here is one of the Cloud TPUv3 racks shown at the conference.

If you want to drill into more details on the sustainability you can check out this Youtube video.

Construction and AI, a bitter lesson that many will learn

One of the nice things about changing this blog to discuss construction over data centers is the audience is much broader. An example is the application of Artificial Intelligence (AI) to construction. If you Google “construction AI” you will have a wide range of companies and articles on the topic. If you talk to any big company in the construction ecosystem you will get a confirmation that they are working on the use of AI in construction. So all is good. Construction will soon have AI systems making construction better. Having worked on the construction problem for over 10 years I am more skeptical of what will be done. Why? One example is the very nature of how AI systems get developed. 

One of my super smart old friends shared this YouTube video.  How smart is he? He worked at Apple, Adobe, Microsoft, Google, Facebook, and startups. How old a friend? Have known him for 30 years.

The above video refers to this paper.

I find it interesting that so many times well written papers have the most interesting part at the end, but so many readers don’t make it to the end. Or by the time they get to the end they aren’t looking for the big points. At the end of this paper is the observation that the fundamental approach to AI is flawed. 

 “The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds”

This observation is based on studying 70 years of AI efforts and figuring out what works and what doe not. The YouTube video provides some nice graphics to illustrate this point with examples.

Now the biggest problem with doing this as this paper suggests is it says do not build your AI system with knowledge from your expert users. It feels good and is satisfying in the short term. Huh. What other things in life are bad for you that feel good and provide short term satisfaction, but plateaus and inhibits further progress. 

So what do you do? Take a contrarian approach. One based on search and learning that scales with computational capabilites. Huh what businesses have made a success based on search and learning? Google. Facebook. Amazon retail. They are all disrupting businesses with AI.

”breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.” 

Well off to Google Cloud Next 19 in SF. Ready to see what they are selling to win the battle against AWS and Azure.

Heading to Google Cloud Next '19, Apr 9-10

Google’s Cloud event is on Apr 9-11 in SF at Moscone Center. Cloud infrastructure is the industry standard. 5G architects realized in order to scale support micro services they need a cloud native design. AWS is famous for the Cloud. But in many ways Google started with Cloud ideas before AWS. The challenge is Google is more of a technical company than a Amazon and they don’t talk about the Cloud in the way as AWS.

This is my first Google Cloud event. I have track and watched from afar, and thanks to some friends I am attending Google Cloud for the first time. I don’t plan on live blogging.

One of my friends though wanted me to share my impression of Google’s communication strategy and how well that works. That is complicated to articulate, and I’ll give it a shot though to share some observations.

Here are some of the tracks.

AWS emerged organically to be a Trillion Dollar baby, not invented by any one person

Medium has a 4 year old post from Jan 2015 interviewing Andy Jassy. The paragraph that caught my eye is Andy describing the beginning. The invention of AWS.

Amazon Web Services wasn’t any one person’s single idea. No proverbial apple fell on some Newton’s head, no Henry Ford or Steve Jobs-like character had a brainstorm. Instead, it rather emerged. The idea grew organically out of the company’s frustration with its ability to launch new projects and support customers.

The other part was the willingness to experiment and fail.

Invention”, says Jassy, “requires two things: 1. The ability to try a lot of experiments, and 2. not having to live with the collateral damage of failed experiments.

These are good lessons to think about when creating new services.