Working to put food on all of our tables, today’s farmers are facing a higher amount of instability from input supply chain issues to weather patterns. Adding to this challenge are problems farmers face when trying to correctly time grain purchases, sales and transport. Farmers have always been stewards of the land, but now the demand for sustainable products has them needing to better prove their regenerative practices.

At Bushel, we understand these problems can’t be solved overnight or by a single company. We focus on empowering agribusinesses and farmers to work even more closely together to build a more sustainable agricultural supply chain by rapidly responding to market changes. With Bushel, farmers can track market prices in real time, instantly buy and sell grain, analyze inventory and transactions, and securely share verified information with grain operators and other producers. We provide the digital tools to streamline how farmers buy and sell commodities throughout the agricultural industry’s supply chain to help address market inefficiencies that can lead to waste, and have the information and resources to help them flex and adapt as complexity increases in farming operations.

Approximately 40% to 50% of all U.S. grain transactions now pass through the Bushel platform. As we continue to grow, Bushel continues to focus on what digital tEmpowering Farmers: Bushel Drives 40-50% of US Grain Transactions, Fueling Sustainable Agricultureools can support each point in the supply chain. Many focus on the first mile at the farm or last mile at the store. But Bushel is focused on modernizing the middle where grain purchasing and processing sit. We aim to help local grain industries and stabilize regional agricultural supply chains.

Starting with a simple mobile app; now scaling into an agricultural ecosystem

Bushel began its journey in 2017 as a small-scale platform for farmers that delivered grain contracts, cash bids, and receipts. As Bushel evolved into a comprehensive agricultural ecosystem, we realized we needed knowledgeable technology partners to help us rapidly scale while saving time and administrative costs. That’s why we started partnering with the Google for Startups Cloud Program to get support from Google and work with Google Cloud Managed Services partner, DoiT International to help support our use of GKE and create a multi-regional deployment as well as migrate our CUDs to new Compute Engine families and continue to optimize our footprint. We’ll also use DoiT’s Flexsave technology to reduce the management overhead of CUDs in the future.

In just one year, we expanded to over 1,200 live grain receiving locations and quickly grew our services portfolio with electronic signature capabilities, commodity balances, and web development. Because that relationship between farmer and agribusiness is so important, we provide more than 200 grain companies with white-labled digital experiences so each farmer sees their local grain facility they do business with on both desktop and mobile. To further our extension into the digital infrastructure of agriculture, we subsequently acquired GrainBridge and FarmLogs to help farmers handle specific jobs and tasks, and provide the needed insights to improve their business operations. Over 2,000 grain receiving locations across the United States and Canada now use Bushel products. We accomplished all this on Google Cloud.

We leverage the secure-by-design infrastructure to protect millions of financial transactions and keep sensitive customer data safe. Our data is processed and stored in Google’s secure data centers, which maintain adherence to a number of compliance frameworks. We utilize Google Kubernetes Engine extensively as it reduces operational overhead and offers auto scaling up to 15,000 nodes.

Database provisioning, storage capacity management, and other time-consuming tasks are automated with our Cloud SQL usage. Query Insights for Cloud SQL streamlines database observability and seamlessly integrates with existing apps and Google Cloud services such as GKE and BigQuery.

Empowering farmers and agribusinesses in North America

The Google Cloud Account Team had been instrumental in helping Bushel build an expansive agricultural platform that powers APIs, apps, websites, and digital solutions. Google’s startup experts are incredibly responsive, with deep technical knowledge that can’t be found elsewhere. Google Cloud also has provided us credits to explore new ways of analyzing the vast amounts of data we generate, verify, and transfer with solutions such as BigQuery and Pub/Sub.

With BigQuery, we can run analytics at scale with 26%–34% lower three-year TCO than cloud data warehouse alternatives. BigQuery delivers actionable insights on a highly secure and scalable platform, includes built-in machine learning capabilities, and integrates with Pub/Sub to ingest and stream analytic events via Dataflow.

With Bushel, farmers across North America are rapidly responding to sudden market changes by tracking grain prices in real time and instantly buying and selling crops. We see a future where this business information becomes insights – where a farmer can not just know where to sell their grain, but when to sell. The burden right now to engage with carbon markets is high, full of paper-based binders and verification forms. We see a world where farming practices recorded digitally can be permissioned along the supply chain for a better picture of how our food is grown.

With the Bushel platform, millions of farmers around the world will have the digital tools to modernize local grain industries, build more sustainable agricultural supply chains, and help to address global food inequity.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Last year, we analyzed the aggregate data from all customers across Google Cloud, and found over 600,000 gross kgCO₂e in seemingly idle projects that could be cleaned up or reclaimed — which would have a similar impact to planting almost 10,000 trees¹. Today, we’re making it easy for you to identify if any of those idle workloads are yours, with new Active Assist sustainability recommendations.  

Active Assist is a part of Google Cloud’s AIOps solution that uses data, intelligence, and machine learning to reduce cloud complexity and administrative toil. Under the Active Assist portfolio, we have products and tools like Policy Intelligence, Network Intelligence Center, Predictive Autoscaler, and a collection of Recommendations for various Google Cloud services — all focused on helping you achieve your operational goals. Today, we are broadening the scope of Active Assist to help you achieve your sustainability targets and reduce the carbon footprint of your workloads.

The carbon emissions associated with your cloud infrastructure can be a big part of your overall environmental footprint. Choosing to run on Google Cloud is a great first step — we’ve matched the energy used by our data centers with 100% renewable energy since 2017, and are committed to running our operations on carbon-free energy 24/7 by 2030. But once you’re running on Google Cloud, if you want to reduce the gross carbon emissions of your workload you can take action to optimize your usage.

Assessing the gross carbon impact of unattended projects

You can now estimate the gross carbon emissions you’ll save by removing these idle projects with Active Assist Unattended Project Recommender, which provides rich utilization insights for all the projects in your organization, and uses machine learning to identify ones that are idle and most likely unattended. The data points Active Assist surfaces as a part of its utilization insights now include the carbonFootprintDailyKgCO2 field, which allows you to estimate carbon emissions associated with any given project. Recommendations also estimate the impact of removing an idle project in terms of kilograms of CO₂ reduced per month. The capability is available via the Recommender API, Recommendation Hub, the Carbon Footprint dashboard, and BigQuery export of recommendations, making it easy for you to integrate with your company’s existing tools and workflows.

Introducing the Carbon Sense suite

Increasing the sustainability of digital applications and infrastructure is a priority for 90% of global IT leaders², and we’ll be continuing to invest across a number of product areas in Google Cloud, including AIOps features like Active Assist’s recommendations, to help you make progress towards your sustainability goals. To make it easy for you to find and consume these new features, we’re bundling our existing and future product work into the Carbon Sense suite — a collection of features that makes it easy to accurately report your carbon emissions, and reduce them. Active Assist joins products like Carbon Footprint, which provides you with the ability to understand and measure the gross carbon emissions of your Google Cloud usage, and our low-carbon signals, which help users choose cleaner regions to run their workloads, in the Carbon Sense suite. Stay tuned for more updates on Carbon Sense in the coming months.

Getting started with sustainability recommendations

To get started with Active Assist sustainability recommendations, check the Carbon Footprint dashboard and Recommendation Hub to review projects that may be idle and assess the carbon emissions associated with them. See recommendations in Google Cloud Console.

To view the recommendations, you will need IAM permissions for Unattended Project Recommender itself and permissions to view resources in a given organization.

You can also automatically export the recommendations from your Organization to BigQuery and then investigate any idle projects with DataStudio or Looker. Or, you can use Connected Sheets to use Google Workspace Sheets to interact with the data stored in BigQuery without having to write SQL queries.

As with any other Recommender, you can choose to opt out of data processing for your organization or your projects at any time by disabling the appropriate data groups in the Transparency & Control tab under Privacy & Security settings.

We hope you use Unattended Project Recommender to reduce the carbon footprint associated with your idle cloud resources, and can’t wait to hear your feedback and thoughts about this feature! Please feel free to reach us at active-assist-feedback@google.com. We also invite you to sign up for our Active Assist Trusted Tester Group if you would like to get early access to new features as they are developed.

^{1. https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator}

^{2. https://inthecloud.withgoogle.com/it-leaders-research-21/sustainability-dl-cd.html}

Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.

Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.

Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.

With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE. 

“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.” 

Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem. 

Windows Server running on-prem with Anthos 

The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.

Get started today

When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.

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.