As an IT leader, you’re asked to do it all: innovate and optimize your tech stack for business outcomes — all while being secure and compliant. It takes heroic efforts to achieve innovation and progress while also tightening budgets and teams. This is why many of you are considering migrating applications to Google Cloud, for benefits like scalability and flexibility, security and compliance, disaster recovery and business continuity, and cutting-edge technologies at lower costs.

To help you do this, we suggest using Google Cloud’s Migrate to Virtual Machines – part of Migration Center. This managed cloud service lets you lift and shift workloads at scale to Google Cloud Compute Engine with minimal changes and risk.

And recently, we rolled out our latest release which introduces support for workload migration from AWS to Google Compute Engine. With this addition, you can now migrate both your on-prem and AWS workloads at scale. This means centralized management for your end to end workload migration journey from both sources via Cloud Console or APIs. 

Simple and easy migrations from AWS and VMware sources to Google Cloud

Migration of AWS EC2 instances directly to Google Compute Engine using Migrate to Virtual Machines follows a well established and easy journey, which means a minimal learning curve for users who are already migrating workloads from VMware. Workload migration is agent-less, which means you do not need to access or alter workloads as a prerequisite for migration, allowing you to execute zero-touch migrations. Migrate instance data with no interruptions to the running workload at the source for a fast cutover to Google Cloud. In addition, our end-to-end cloud console interface surfaces your AWS EC2 inventory, migrations, and groups so you can execute migrations without ever leaving the cloud console interface. 

Large-scale migrations 

Completing a large-scale migration project in a timely manner calls for careful planning and streamlined migration sprints. The Migrate to Virtual Machines’ Groups construct enables you to group source VMs together in the planning phase. When it’s time to execute the planned migration, VM Groups let you execute migration operations on a group level, or on a subset of the group, streamlining the process at scale.

Minimal downtime and risk

Application uptime is key to keeping your business running. Every migration with this latest release of the service periodically replicates data from the source workload to the destination without manual steps or interruptions to the running workload, minimizing workload downtime and enabling fast cutover to Google Cloud. You can also launch non-disruptive migration tests — referred to as test-clones — to help you validate that these workloads will work properly in the cloud before cutting over. This helps avoid issues that might have otherwise been costly or disruptive to your business. 

How the service works

Migrations simply work, at scale, in a managed service fashion. With Migrate to Virtual Machines, there’s no requirement to provision or manage migration-specific resources in the cloud. The service uses replication-based migration technology to lift and shift workloads from source environments to Google Cloud. The Migrate Connection replicates source VM disk snapshots in the background with no interruption to the source workload. Replicated data is encrypted in transit and at rest, and when you instantiate a migrating VM using a test-clone or cut-over, the service seamlessly adapts your source VM operating system to boot and run natively in the cloud — including configuring network settings and deploying Google Cloud guest packages. 

The migration journey of an EC2 instance — or VMware VM — to Google Cloud is comprised of the following steps:

1. Onboarding a source VM for migration: Onboard one or more VMs for migration from the source environment fleet.

2. Configure landing zone target: You can migrate an instance to any Google Cloud project in your environment and update landing zone details at any time before executing a test-clone or cutover.

3. Initiate VM data replication of source workload: Migrate to Virtual Machines periodically replicates instance disks to the cloud with no interruption to the source instance. You can control replication frequently and pause or resume at any point in time. 

4. Test migrating instance: Test-clone creates a copy of your source instance in the defined landing zone to validate the migrating instance in the cloud before executing a cut-over. You can repeat the test-clone multiple times to multiple landing zones for thorough validation

5. Cutover migrating instance: Cutover operation shuts down your source instance and then performs the short final sync to Google Cloud. Migrated VM is instantiated in the target landing zone.

Getting started with Migrate to Virtual Machines 

It’s quick and easy to start migrating your AWS EC2 instances and on-premises VMs today:

1. Enable the vmmigration API in a Google Cloud project 
2. Create an AWS source in your environment
3. Onboard and initiate replication of instance data from source 
4. Set migrating instance target details. 
5. Perform non-disruptive tests of your migrating instance using test-clone
6. Cutover your instance to the cloud with minimal down time

You can also visit our website to learn more about Migrate to Virtual Machines. If you know you have to migrate in 2023 but aren’t sure how to get started, you can sign up for a free discovery and assessment of your current IT landscape so we can help craft the ideal migration plan for you and your business.

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.

Every cloud system begins with high-quality hardware infrastructure. Sometimes, however, hardware breaks — and when it happens, our most important goal is to minimize the impact on our customers and their cloud workloads.

Memory errors are the most common type of hardware failure, and they’re also one of the most challenging in terms of their impact on production workloads and system reliability. That’s why we’re excited to share what Google Cloud has been doing to minimize the impact of memory errors. If your business runs SAP HANA in the cloud, this is an important innovation —  one that Google Cloud is proud to deliver to our customers.

Memory errors: A big problem with a long history

First things first: Memory errors are a high priority because they happen often. And when they happen, the disruption can have far-reaching effects on your customers and your business. 

In 2009, Google Cloud published the first major study on memory reliability. We found an average error rate of over 8% per year in DIMM modules installed in production systems. Given that each generation of DDR RAM packs more capacity into smaller packages, it’s safe to think that memory hardware has become less reliable since then.

Memory error impacts: They could be worse, but they’re far from good

What happens when a system detects a bad segment in a DIMM module? While data loss or corruption from memory errors is not common, some errors are correctable but some are not, potentially resulting in a critical system failure..  

Modern CPUs are equipped with error-correcting memory features and are very good at correcting simple errors with ECC (Error Correction Code). The challenge is that most of the software that runs on a host system — whether it’s a hypervisor, a virtual machine, an operating system, a database or an application — will crash instantly when it encounters an uncorrectable memory error. In a cloud environment, this kind of crash can take down cached data and even data saved to a local SSD. The crashed applications will recover, but the process means several minutes of downtime. The more data you have, the longer this process will take.

Sometimes, that’s merely an inconvenience. Other times, it’s a very big deal. A Google Cloud customer running business-critical SAP applications and an in-memory HANA database might measure downtime costs well over $10,000 per minute in lost revenue and other direct impacts. Many HANA databases load into terabytes of memory, and it can take an hour or longer to get everything restarted and back to normal after a crash. For SAP HANA, a fast recovery with up to 10 minutes of downtime requires a redundant replica provisioned all the time, doubling the cost.

And statistically speaking, when a HANA instance occupies almost all of the memory on a host system, it’s also the most likely application to stumble across a memory error. You can see why this would be a problem.

 The ‘victim neighbor’ VM challenge

There’s a final problem to consider when a memory error takes out production applications: what we call the “victim neighbor” issue.

In any cloud, a single physical host is a multi-tenant environment that might run dozens of VMs, potentially owned by dozens of different customers. A memory error won’t just crash the VM actually using the bad section, it will crash every VM running on the system. That’s a standard VM response to memory errors on a host system, and it will happen to any VM architecture available on the market today to avoid memory corruption. 

Overall, this “victim neighbor” effect accounts for more than 90% of the VMs that get knocked down by a memory error on a physical server. That’s a huge blast radius for such a common problem.

A practical solution to memory-error impacts

You can see why managing this problem is a big deal for Google Cloud. While we know that some failures are inevitable, we have developed another way to tackle the problem. Google Cloud already maintains some unique and valuable tools, such as Live Migration, that help our customers minimize unplanned downtime.When we integrate these tools with recent work that leverages error-handling capabilities built into CPUs (courtesy of Intel) and into certain applications (in particular, SAP HANA), we get a solution that dramatically reduces downtime and disruptions related to memory errors — in many cases, to the point where customers won’t even know there was a problem.

The Google Cloud solution: Memory poisoning recovery

At a big picture level, we refer to our solution as Memory Poisoning Recovery (MPR). It combines some existing Google Cloud capabilities, some new capabilities, and some important third-party capabilities at the CPU (Intel) and application (SAP HANA) levels. MPR can be broken down into two main processes:

Memory Error Isolation 

• Step 1: We hardened our VM technology to be more robust against memory errors. We intercept and analyse the memory error coming from the system. Then we flag the signaled region of a memory DIMM with an uncorrectable error as “poisoned”. 
• Step 2: Then we trigger processes to keep track of these “poisoned” regions and the VMs they affect so they can’t affect data integrity. 

Memory Error Recovery

• Step 3: Then we notify the Guest OS & the MCE-aware applications that a memory error has been recorded, in a manner that allows the applications to execute application relevant memory error handling.
• Step 4: At the same time we communicate with Google Cloud Live Migration to begin moving guest VMs off the affected host. This ensures customers are running on a healthy host which reduces the probability of more uncorrectable errors happening and avoids further downtime.

Below is a simple visual of how this all works:

How MPR makes life better for customers

Let’s look again at the different groups of Google Cloud customers involved in a memory error scenario and how we can help them achieve a happier ending after a crash — starting with the customer running the VM and application that actually triggered the memory error. 

Customer Group: MCE-Aware SAP HANA with Fast Restart enabled on a VM directly affected by a memory error.

Customer Group: Customers running other, non MCE Aware applications on a VM directly affected by a memory error

Next, our “victim neighbors” group probably won’t even know there was a problem with the host system. Google Cloud Live Migration will move them to a new host, instantly and automatically, and avoid the crash-and-restart scenario.

Customer Group: Customers running other, any application on a VM not directly affected by a memory error

Simple steps for taking advantage of MPR

Our MPR capabilities will be available on our Google Cloud memory-optimized Compute Engine second generation instances in Q4 of 2021. We’ll continue to roll out the capability during the months ahead to additional instances and look for new ways to work with applications that adopt a MCE Aware architecture.

Most customers in the “victim neighbor” category will not need to lift a finger to experience the benefits. By marrying our Live Migration feature to some awareness of those MCE signals, we ensure that it hears the alarm first and gets a critical head start on the migration process before issues begin with the guest VMs. Our customers land safely on a new host, and their applications keep running.

For our SAP customers running HANA, MPR is all about protecting against loss. Unplanned downtime for a HANA environment is incredibly expensive, the recovery process from a hard crash is extremely long, and the business disruptions can be truly damaging to the business. Thanks to MPR, all of that cost and worry can get compressed almost to nothing — with Fast Restart reducing what can be an hour or more of downtime to a matter of seconds.

But our SAP customers have to take a critical first step to claim these benefits. Fast Restart is a crucial piece of the MPR solution, and it is not enabled by default. Configuring your SAP HANA instance for Fast Restart involves changing a few configuration settings; the process is fast, easy, and doesn’t involve risk. 

Finally, if you’re not running your workloads — SAP or otherwise — on Google Cloud, consider the benefits of running on a cloud that mitigates a hardware reliability issue affecting businesses of every size and industry. And consider the value of tools like Live Migration that already help Google Cloud customers improve uptime and reduce risk.

Hardware failures happen, and they probably always will. But we’re proving how valuable it can be to avoid the bad things that usually happen when memory failures occur. Right now, only Google Cloud has a practical solution to this very difficult problem.    

Learn more about Fast Restart for SAP HANA, Live Migration and other key Google Cloud capabilities for your SAP environment.

About Vimeo

Vimeo gives video creators the tools to host, share, and sell videos of the highest quality possible. It reaches viewers in over 150 countries who can watch content anytime, on nearly every Internet-connected device.

Industries: Media & Entertainment

Location: United States

About Fastly

Fastly helps the world’s most popular digital businesses keep pace with their customer expectations by delivering fast, secure, and scalable online experiences. Businesses trust the Fastly edge cloud platform to accelerate the pace of technical innovation, mitigate evolving threats, and scale on demand.

Industries:

Location:

Vimeo is a video-sharing platform that’s home to imaginative video creators and hundreds of millions of viewers. Sixty million people create, host, and sell high-quality videos on Vimeo, including more than 800,000 who subscribe to the service’s premium tools. Over 240 million people in more than 150 countries watch videos monthly.

Vimeo was using its own servers to allow users to upload videos to its service, a cloud storage platform for storing videos, and as an alternative solution for streaming. It was looking for a solution that would do away with its own servers for uploading. Vimeo built a new adaptive video-delivery service on Google Cloud Platform and the Fastly edge cloud that can scale on demand to meet Vimeo’s growing needs for video streaming.

“Our business is dependent on delivering high-quality video; that’s our competitive edge,” says Naren Venkataraman, Senior Director of Engineering at Vimeo. “Thanks to Fastly and Google Cloud Platform, we’re delivering more high-quality videos than ever at less cost, leading to our continuing success and growth.”

Tuning video delivery

Building a great video experience begins with a fast, reliable upload service. Vimeo replaced its servers for accepting video uploads with Google Cloud Storage, fronted by the Fastly edge cloud to help ensure regional routing and low-latency, high-throughput connections for Vimeo’s publishers. Multi-regional Google Cloud Storage offers fast, resumable upload capability that helps make for better user experience.

The video delivery service transcodes videos and streams them to users—videos are customized depending on network traffic and the devices to which the videos are delivered. The goal is to deliver the highest-quality, smooth playback experience across all platforms over varying network conditions and device capabilities.

Google Compute Engine packages the videos, which are stored on Google Cloud Storage. Google Compute Engine can automatically scale to allow Vimeo to deliver videos on the fly, even when demand spikes and many users stream videos simultaneously across a very diverse library. The low latency of Google Cloud Storage helps with fast startup times, while providing scalable storage to host millions of videos from Vimeo’s loyal community of content creators.

“Fastly and Google Cloud Platform enabled us to build a low-latency, highly scalable, on-the-fly adaptive video streaming packager in a short period of time with a small team,” says Naren.

High-quality video means more users

With Fastly and Google Cloud Platform, Vimeo is delivering more and higher-quality videos to its users because of the platform’s low latency, high bandwidth, and ability to scale. Because of the system’s reliability, fewer users stop watching videos because of delays and glitches. Vimeo engineers do not have to spend their time managing infrastructure and now focus on improving the video delivery service, leading to improved customer satisfaction. Costs are reduced because Vimeo does not have to manage the infrastructure in-house.

“We’ve chosen Google for Fastly’s Cloud Accelerator because at Google innovation happens faster, and Google Cloud Platform is driving cloud computing and cloud storage in the right direction,” says Lee Chen, Head of Strategic Partnerships at Fastly.