With Google Cloud, Autonom8 can run a platform that accelerates and streamlines customer journeys in a scalable, reliable, cost-effective infrastructure, while using advanced optical character recognition to enable intelligent document processing.

About Autonom8

Headquartered in the United States and India, Autonom8 has built a low-code SaaS platform that allows businesses to digitize customer-facing workflows. The business aims to help clients reduce costs and improve interactions with their customers through automation and enablement of customer journeys.

Industries: Technology

Location: United States and India

Google Cloud results:

• Increased margins by up to 30% by switching from a home-grown OCR system to Cloud Vision AI
• Enables one DevOps team member to manage up to 30 customers
• Provides real-time information about customer journeys to enable businesses to respond quickly and accurately
• Ensures use of its platform with containerization in customers’ private data centers
• Reduced operating costs by up to 20% with localized scalability and architecture through GKE

Just as cars are evolving to become autonomous, smart and self-driving, enterprises can gain self-awareness, an ability to learn and an ability to adapt. This is the value proposition put forward by Autonom8, an India- and United States-based enterprise workflow management software business. “We provide a low-code, high-intelligence customer journey automation SaaS platform,” explains ​​Ranjit Padmanabhan, Co-founder and Chief Technology Officer (CTO), Autonom8.

The Autonom8 platform includes components, such as A8Studio, a drag and drop location from which clients can create customer journeys, a chat platform that enables clients to create chatbots, and an analytics module. “With our platform and services, businesses can reduce costs and improve interactions with their customers by applying automation to accelerate and provide better customer journeys,” adds Padmanabhan.

Demand for Autonom8 is being driven by the changing customer demands of enterprises, including the expectation to interact with them over multiple channels, and the rising cost of building software with experienced developers. These trends place enterprises under growing pressure to increase the productivity of the people they do have, particularly those who are less technically inclined. In addition, changing consumer habits, regulations and the emergence of new technologies mean customer journeys cannot remain static and need to evolve.

Developing a microservices-based SaaS platform

From the start, Autonom8 planned to deliver a SaaS platform and initially deployed on a multinational cloud service, chosen due to the team’s familiarity with its products and the availability of credits. However, the company’s decision to opt for a microservices architecture that enables individual services to scale independently while running in a containerized environment, demanded high-quality container orchestration. To optimize cost, scalability and performance, Autonom8 began evaluating Google Kubernetes Engine (GKE).

The business then completed a side-by-side comparison between Google Cloud and its incumbent provider of compute, storage and other services. Google Cloud fared favorably, with Vision AI in particular providing powerful machine learning and optical character recognition (OCR) functionality, supporting a key use case for Autonom8.

In addition, many of Autonom8’s clients at the time are financial institutions in India, and legally required to retain data within the country’s borders. Google Cloud’s global network and local presence means the business could fulfill this requirement easily.

“We decided to evaluate Google Cloud, particularly GKE, from two perspectives. One, from a security perspective, as we sell to banks that audit our platform, and two, as a failover between regions because downtime costs money. We found it a compelling solution.”

— Ranjit Padmanabhan, Co-founder and Chief Technology Officer (CTO), Autonom8

A seamless move to Google Cloud

Autonom8 began deploying on Google Cloud in 2018, with its architecture comprising storage, compute, serverless, container management and orchestration, and Vision AI. “We looked at our scripting with the previous provider, and using the Google Cloud documentation available online, educated ourselves over a few weeks before moving pieces of our architecture step by step to Google Cloud,” says Padmanabhan. “We did not run into any major issues. It was pretty simple, with our experienced engineers training others in the product.”

According to the CTO, the business had two options when moving to Google Cloud. Autonom8 could either install raw virtual machines and effectively create its own virtualized data center, or rely on managed services for functions such as memory store, registration and authentication to save time and resources over the long term. Autonom8 opted for the latter and has transitioned fully to Google Cloud, with the number of cloud products and services in its architecture rising from five to about 15. While each product and service performs a key role in the delivery of Autonom8’s products and services, Padmanabhan nominates GKE, Vision AI and Cloud SQL as providing the greatest value to the business.

Scalability, real-time monitoring and intelligent document processing at low cost

With GKE, the business can now scale the nodes or containers specific to each microservice in the event traffic to a particular client surges, due to a rebate or promotion. “Through the combination of the architecture and localized scalability we achieve with GKE, we are reducing our operating costs by up to 20%,” says Padmanabhan.

Running an open source TimescaleDB on Postgres in Cloud SQL enables Autonom8 to give its clients the ability to monitor customer journey information in real time. An example of a journey is applying for a bank loan. The customer must take steps including providing income, tax and other financial details that the bank then appraises to help make a decision on the application. “The moment someone applies for a loan, for example, a bank knows about it and can monitor for fraud, bottlenecks, or other abnormalities, and immediately route to a remediation workflow,” explains Padmanabhan. “Cloud SQL enables us to maintain transactional logging and provide real-time data to our dashboards.”

After evaluating alternative services, including developing a home-grown OCR engine, the business turned to Cloud Vision AI to manage the intelligent document processing that comprises much of its transactional volume. “Vision AI is significantly better than the alternatives and the cost of maintaining our version did not make sense, because Google Cloud continues to make improvements over time that enable us to deliver more and more accurate results to our customers,” says Padmanabhan. “Switching from our home-grown service to Vision AI has enabled us to increase our profit margins by up to 30%.”

“Through the combination of the architecture and localized scalability we achieve with Google Kubernetes Engine, we are reducing our operating costs by up to 20%.”

—Ranjit Padmanabhan, Co-founder and Chief Technology Officer (CTO), Autonom8

Supporting client demands and improving developer efficiency

Google Cloud also enables Autonom8 to meet the demands of businesses that want to run its platform within their own private data centers. “We can undertake the build within Google Cloud and ship our containers to compatible hosts within those clients’ data centers,” explains Padmanabhan. “With our previous provider, we could create containers, but these would not run properly within those data centers.”

Furthermore, Google Cloud documentation and online resources help Autonom8 reduce the training needed for new developers to become productive, with the Google Cloud learning curve taking up just 10% of the overall onboarding cycle.

The organization spends the equivalent of just 3% of its overall annual revenue on DevOps, measured as DevOps Utility Ratio, while the cloud cost of revenue is about USD 1 for every USD 6 in annual recurring revenue, measured as Cloud Utility Ratio. “These two metrics are about what we can do with the people we have,” explains Padmanabhan. “Our current ratio implies that one DevOps person can handle approximately 30 customers. This is made possible by the tools we have, and the comprehensive support from Google Cloud in terms of security patches, intelligent alerts, resource overloading, and more.”

Google Cloud also provides the flexibility for Autonom8 to accommodate the varying service levels required by individual customers based on factors, such as the impact of downtime, as the business can failover seamlessly between regions to mitigate the impact of any issues that may occur.

“Our current ratio implies that one DevOps person can handle approximately 30 customers. This is made possible by the tools we have, and the comprehensive support from Google Cloud in terms of security patches, intelligent alerts, resource overloading, and more.”

—Ranjit Padmanabhan, Co-founder and Chief Technology Officer (CTO), Autonom8

Integrating Google Workspace with Autonom8 to deliver new capabilities

Autonom8 relies on Google Workspace for communication, collaboration and other workplace productivity requirements, growing its footprint from Gmail when the employee population was four or five, to a range of products including Sheets and Drive as the business grew. “It became natural to use the capabilities in Google Workspace as we matured,” says Padmanabhan. “One of the most interesting capabilities was our ability to integrate Google Workspace into our platform. For example, when someone is running a workflow, they can add data from a Sheet. We’ve added Google Workspace authentication capabilities into our products as well.”

“Everyone is using shared links to Drive and I really like the granular permissions structure,” he adds. “I can open up folders to clients while keeping an internal space within the business to ensure security and privacy.”

With Google Cloud, Autonom8 is now poised to continue growing its business and adding new features and capabilities for clients. “We are extremely excited at the opportunity to step up our offering to clients with Google Cloud,” concludes Padmanabhan.

Records are made to be broken. In 2019, we calculated 31.4 trillion digits of π — a world record at the time. Then, in 2021, scientists at the University of Applied Sciences of the Grisons calculated another 31.4 trillion digits of the constant, bringing the total up to 62.8 trillion decimal places. Today we’re announcing yet another record: 100 trillion digits of π.

This is the second time we’ve used Google Cloud to calculate a record number1 of digits for the mathematical constant, tripling the number of digits in just three years.

This achievement is a testament to how much faster Google Cloud infrastructure gets, year in, year out. The underlying technology that made this possible is Compute Engine, Google Cloud’s secure and customizable compute service, and its several recent additions and improvements: the Compute Engine N2 machine family, 100 Gbps egress bandwidth, Google Virtual NIC, and balanced Persistent Disks. It’s a long list, but we’ll explain each feature one by one.

Before we dive into the tech, here’s an overview of the job we ran to calculate our 100 trillion digits of π.

• Program: y-cruncher v0.7.8, by Alexander J. Yee
• Algorithm: Chudnovsky algorithm
• Compute node: n2-highmem-128 with 128 vCPUs and 864 GB RAM
• Start time: Thu Oct 14 04:45:44 2021 UTC
• End time: Mon Mar 21 04:16:52 2022 UTC
• Total elapsed time: 157 days, 23 hours, 31 minutes and 7.651 seconds
• Total storage size: 663 TB available, 515 TB used
• Total I/O: 43.5 PB read, 38.5 PB written, 82 PB total

Architecture overview

Calculating π is compute-, storage-, and network-intensive. Here’s how we configured our Compute Engine environment for the challenge.

For storage, we estimated the size of the temporary storage required for the calculation to be around 554 TB. The maximum persistent disk capacity that you can attach to a single virtual machine is 257 TB, which is often enough for traditional single node applications, but not in this case. We designed a cluster of one computational node and 32 storage nodes, for a total of 64 iSCSI block storage targets.

The main compute node is a n2-highmem-128 machine running Debian Linux 11, with 128 vCPUs and 864 GB of memory, and 100 Gbps egress bandwidth support. The higher bandwidth support is a critical requirement for the system as we adopted a network-based shared storage architecture.

Each storage server is a n2-highcpu-16 machine configured with two 10,359 GB zonal balanced persistent disks. The N2 machine series provides balanced price/performance, and when configured with 16 vCPUs it provides a network bandwidth of 32 Gbps, with an option to use the latest Intel Ice Lake CPU platform, which makes it a good choice for high-performance storage servers.

Automating the solution

We used Terraform to set up and manage the cluster. We also wrote a couple of shell scripts to automate critical tasks such as deleting old snapshots, and restarting from snapshots (we didn’t need to use this though). The Terraform scripts created OS guest policies to help ensure that the required software packages were automatically installed. Part of the guest OS setup process was handled by startup scripts. In this way, we were able to recreate the entire cluster with just a few commands.

We knew the calculation would run for several months and even a small performance difference could change the runtime by days or possibly weeks. There are also a number of combinations of parameters in the operating system, infrastructure, and application itself. Terraform helped us test dozens of different infrastructure options in a short time. We also developed a small program that runs y-cruncher with different parameters and automated a significant portion of the measurement. Overall, the final design for this calculation was about twice as fast as our first design. In other words, the calculation could’ve taken 300 days instead of 157 days!

The scripts we used are available on GitHub if you want to look at the actual code that we used to calculate the 100 trillion digits.

Choosing the right machine type for the job

Compute Engine offers machine types that support compute- and I/O-intensive workloads. The amount of available memory and network bandwidth were the two most important factors, so we selected n2-highmem-128 (Intel Xeon, 128 vCPUs and 864 GB RAM). It satisfied our requirements: high-performance CPU, large memory, and 100 Gbps egress bandwidth. This VM shape is part of the most popular general purpose VM family in Google Cloud.

100 Gbps networking

The n2-highmem-128 machine type’s support for up to 100 Gbps of egress throughput was also critical. Back in 2019 when we did our 31.4-trillion digit calculation, egress throughput was only 16 Gbps, meaning that bandwidth has increased by 600% in just three years. This increase was a big factor that made this 100-trillion experiment possible, allowing us to move 82.0 PB of data for the calculation, up from 19.1 PB in 2019.

We also changed the network driver from virtio to the new Google Virtual NIC (gVNIC). gVNIC is a new device driver and tightly integrates with Google’s Andromeda virtual network stack to help achieve higher throughput and lower latency. It is also a requirement for 100 Gbps egress bandwidth.

Storage design

Our choice of storage was crucial to the success of this cluster – in terms of capacity, performance, reliability, cost and more. Because the dataset doesn’t fit into main memory, the speed of the storage system was the bottleneck of the calculation. We needed a robust, durable storage system that could handle petabytes of data without any loss or corruption, while fully utilizing the 100 Gbps bandwidth.

Persistent Disk (PD) is a durable high-performance storage option for Compute Engine virtual machines. For this job we decided to use balanced PD, a new type of persistent disk that offers up to 1,200 MB/s read and write throughput and 15-80k IOPS, for about 60% of the cost of SSD PDs. This storage profile is a sweet spot for y-cruncher, which needs high throughput and medium IOPS.

Using Terraform, we tested different combinations of storage node counts, iSCSI targets per node, machine types, and disk size. From those tests, we determined that 32 nodes and 64 disks would likely achieve the best performance for this particular workload.

We scheduled backups automatically every two days using a shell script that checks the time since the last snapshots, runs the fstrim command to discard all unused blocks, and runs the gcloud compute disks snapshot command to create PD snapshots. The gcloud command returns and y-cruncher resumes calculations after a few seconds while the Compute Engine infrastructure copies the data blocks asynchronously in the background, minimizing downtime for the backups.

To store the final results, we attached two 50 TB disks directly to the compute node. Those disks weren’t used until the very last moment, so we didn’t allocate the full capacity until y-cruncher reached the final steps of the calculation, saving four months worth of storage costs for 100 TB.

Results

All this fine tuning and benchmarking got us to the one-hundred trillionth digit of π — 0. We verified the final numbers with another algorithm (Bailey–Borwein–Plouffe formula) when the calculation was completed. This verification was the scariest moment of the entire process because there is no sure way of knowing whether or not the calculation was successful until it finished, five months after it began. Happily, the Bailey-Borwein-Plouffe formula found that our results were valid. Woo-hoo! Here are the last 100 digits of the result:

4658718895 1242883556 4671544483 9873493812 1206904813
2656719174 5255431487 2142102057 7077336434 3095295560

You can also access the entire sequence of numbers on our demo site.

So what?

You may not need to calculate trillions of decimals of π, but this massive calculation demonstrates how Google Cloud’s flexible infrastructure lets teams around the world push the boundaries of scientific experimentation. It’s also an example of the reliability of our products – the program ran for more than five months without node failures, and handled every bit in the 82 PB of disk I/O correctly. The improvements to our infrastructure and products over the last three years made this calculation possible.

Running this calculation was great fun, and we hope that this blog post has given you some ideas about how to use Google Cloud’s scalable compute, networking, and storage infrastructure for your own high performance computing workloads. To get started, we’ve created a codelab where you can create and calculate pi on a Compute Engine virtual machine with step-by-step instructions. And for more on the history of calculating pi, check out this post on The Keyword. Here’s to breaking the next record!

1. We are actively working with Guinness World Records to secure their official validation of this feat as a “World Record”, but we couldn’t wait to share it with the world. This record has been reviewed and validated by Alexander J. Yee, the author of y-cruncher.

Every year at CES, people from around the world experience the latest and greatest that consumer tech has to offer. In 2021, CES will be in an all-digital format for the first time.

So how can a virtual show like CES create immersive experiences for attendees tuning in remotely? That’s an interesting challenge for the cloud, and of course, every challenge presents an opportunity.

Google Cloud and 5G help enterprises deliver new experiences

Earlier in 2020, we announced our enterprise telecommunications strategy to deliver workloads to the network edge on Google Cloud, and during our SearchOn event last October, we announced how cloud streaming technology can power augmented reality (AR) in consumer search results.

Now, we’re merging the best of both worlds: Technology built for consumer search can take advantage of our enterprise edge capabilities. In light of the COVID-19 pandemic, this past year accelerated our support for enhanced consumer experiences across the board in novel ways. For example, we attempted to address questions such as how potential buyers can make a purchasing decision when they can’t see the product up close. This question becomes even more critical when considering a large purchase such as a new car.

That’s exactly what Fiat Chrysler Automobiles (FCA) and Google Cloud are working together to solve. As part of FCA’s Virtual Showroom CES event, you can experience the new innovative 2021 Jeep Wrangler 4xe by scanning a QR code with your phone. You can then see an Augmented Reality (AR) model of the Wrangler right in front of you—conveniently in your own driveway or in any open space. Check out what the car looks like from any angle, in different colors, and even step inside to see the interior with incredible details.

“As we continue our journey towards becoming a customer-centric mobility company, FCA is adopting emerging technologies that enable us to accelerate and deliver at the speed of our customers’ expectations,” said Mamatha Chamarthi, Chief Information Officer, FCA – North America and Asia Pacific. “Through our collaborative partnership with Google, we are able to expand our efforts to provide an immersive customer experience.”

Harnessing the power of edge with 5G

In order to create a mixed-reality experience with a 3D car model, computer-aided design (CAD)-based data sources that represent a 3D vehicle with highly detailed geometry, depth, texture, and lighting were used. High-fidelity models, such as cars with full interiors, often mean large files (GBs in size). Traditionally, depending on your connection, this can result in long waiting times as assets are downloaded onto your phone. In addition, while mobile phones are more powerful than the Apollo Guidance Computer, they are no match for the power we have in the cloud. We still want to bring these high end experiences to everyone, regardless of their respective device or geographical location.

We solve this problem by rendering the model in Google Cloud, then streaming it to the devices.

Specifically, the Cloud AR tech uses a combination of edge computing and AR technology to offload the computing power needed to display large 3D files, rendered by Unreal Engine, and stream them down to AR-enabled devices using Google’s Scene Viewer. Using powerful rendering servers with gaming console grade GPUs, memory, and processors located geographically near the user, we’re able to deliver a powerful but low friction, low latency experience. This rendering hardware allows us to load models with tens of millions of triangles and textures up to 4k, allowing the content we serve to be orders of magnitude larger than what’s served on mobile devices (i.e., on-device rendered assets). Doing so leverages high-speed 5G connectivity and streams directly from Google Cloud’s distributed edge, delivering a rich, photorealist immersive experience. Customers like FCA benefit from Google’s years of investment and expertise in streaming technology (have you tried playing Cyberpunk2077 on Stadia yet?). With the expansion of 5G networks, not only will streaming enable the experience for anyone anywhere, but it will also cut the wait time of downloading large assets required for detailed AR/VR experiences, ultimately providing instant gratification.

Applications and experiences are at the core of a winning edge proposition

We’re working to make these capabilities available to all enterprise customers to enable innovative use cases such as leveraging AR to help design teams collaborate, technicians perform machine diagnostics, creating next-generation, live video experiences for sports events, enabling new customer experiences across many industries, and supporting our customers in their digital transformation. Stay tuned!

To see the Jeep Wrangler 4xe come to life whether you’re attending CES or not, scan the QR code below, or check out the FCA CES website. Depending on your OS, device, and network strength, you will see either a photorealistic, cloud-streamed AR model or an on-device 3D car model, both of which can then be placed in your physical environment.