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.

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.

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

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.

• Application development

• Architecture

• Biz Tech Exchange

• Collaboration and productivity

• Compute

• Cost management

• Data analytics

• Databases

• DevOps and SRE

• Diversity & Inclusion

• Hybrid Cloud and Containers

• Industries

• IoT

• ML and AI

• Mobility and devices

• Networking

• Security

• Serverless

• Storage

Can Carbon Relay deliver AI efficiency like what Google's Data Center group uses?

The media covers Foxconn’s back of Carbon Relay. Here is tech crunch’s post.

Taiwanese technology giant Foxconn International is backing Carbon Relay, a Boston-based startup emerging from stealth today, that’s harnessing the algorithms used by companies like Facebook and Google for artificial intelligence to curb greenhouse gas emissions in the technology industry’s own backyard — the datacenter.

According to LinkedIn the founder Matt Provo has been with the company since Aug 2015.

Carbon Relay has on its web site a graph that shows how its model matches the actual PUE

Which looks a lot like Google’s predictive accuracy which is in this paper.

I don’t know Matt Provo or anyone at Carbon Fiber. You can see the team on this page which has their LinkedIn profiles. From taking a quick look I don’t see any mechanical engineers or data center operations people.

Google’s AI/ML energy efficiency project was headed up by Jim Gao who i do know. Jim is a mechanical engineer from UC Berkeley. Go Bears! I also have my engineering degree from Cal, but long before Jim went there. Jim had years of working in Google’s data center group and started down the path of machine learning and he had one of the biggest sources of training data, Google’s data centers. Which may explain why Jim’s predictive models look more accurate than Carbon Relay.

Jim published his latest findings as part of work in Alphabet’s Deepmind where is now a Team Lead.

So can Carbon Relay’s 14 people deliver a solution as good as Deepmind’s Jim Gao? Jim has gone through the painstaking efforts to get clean accurate data from systems. There are so many small details. I love the one example where Jim ran the model to be the most energy efficient, so it turned off all the systems bringing energy use to 0. And Jim has overcome the resistant to change from a well trained data center operations staff to trust a computer model.

Looking at the number of technical team on the Carbon Relay project I am reminded how the first models Jim ran could be performed on one PC. Time will tell if Carbon Relay can deliver on data center efficiency, but even if they have a technical solution getting clean data from all the BMS environments and executing a model that is used is so hard.

The paper that Jim published has Amanda Gasparik on the paper. Got curious looked her up on LinkedIn as she is senior data center mechanical engineer. Been at Google 5 years. 8 years as Nuclear Electronics Technician for US Navy. Masters System engineering and Bachelor’s mechanical engineer.

Add another DeepMind PhD Research Engineer and you have three people who have a broad range of skills that impress me much more than Carbon Relay.

Google's data center AI puts safety first just like airplanes fly-by-wire while saving 30% of cooling energy

Google has a post on its latest application of AI from the Deepmind group to its data center cooling systems. The tech media nicely covered the post. Here is a full coverage link.

https://news.google.com/stories/CAAqOQgKIjNDQklTSURvSmMzUnZjbmt0TXpZd1NoTUtFUWljLWRIc2pJQU1FVG02XzQzUkI2OGhLQUFQAQ?hl=en-US&gl=US&ceid=US%3Aen

Google choose to emphasize the system was designed for safety as indicated in the title of their post.

“Safety-first AI for autonomous data center cooling and industrial control”

— https://www.blog.google/inside-google/infrastructure/safety-first-ai-autonomous-data-center-cooling-and-industrial-control/

The following graphic illustrates the safety principles used like a fly-by-wire system. 

“While traditional mechanical or hydraulic control systems usually fail gradually, the loss of all flight control computers immediately renders the aircraft uncontrollable. For this reason, most fly-by-wire systems incorporate either redundant computers (triplex, quadruplex etc.), some kind of mechanical or hydraulic backup or a combination of both. A “mixed” control system with mechanical backup feedbacks any rudder elevation directly to the pilot and therefore makes closed loop (feedback) systems senseless.[1]”

— https://en.wikipedia.org/wiki/Fly-by-wire#Efficiency_of_flight

It has been a pleasure watching Jim Gao make progress with with AI in data center cooling and you can bet there will be much more coming.