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.

Communication services providers (CSPs) are experiencing a period of disruption. Overall revenue growth is decelerating and is projected to remain below 1 percent per year, following a trend that started even before the pandemic.¹ At the same time, driven by the pandemic, data consumption in 2020 increased by 30 percent relative to 2019, with some operators even reporting increases of 60 percent.² 

The combination of pressure on revenues with rising data traffic costs is forcing operators to innovate in three fundamental ways. First, operators are looking to establish new sources of revenue. Second, increased network utilization must be met with a reduction in network cost. And third, there is an opportunity to gain new customers by improving the customer experience.

Fortunately, 5G offers a path forward across each of these three areas. Concepts such as network slicing and private networks allow CSPs to offer differentiated network services to public sector and enterprise customers. The disaggregation of hardware and software allows new vendors with unique strengths to enter the market and to enable CSPs to build, deploy, and operate networks in fundamentally new ways. And the ability to place workloads at the edge permits CSPs to offer compelling experiences to consumers and businesses alike. In this blog, we will discuss how CSPs can create a solid foundation for their cloud networks. 

Understanding telecommunications networks

First, it is useful to consider the way telecommunications networks were traditionally built. Initially, networks were built using physical network functions (PNFs) — appliances that used a tight combination of hardware and software to perform a specific function. PNFs offered the benefit of being purpose-built for a specific application, but they were inflexible and difficult to upgrade. As an example, deploying new features frequently required replacing the entire PNF, i.e., deploying a new hardware appliance.

The first step in improving deployment agility came with the concept of virtualized network functions (VNFs), software workloads designed to operate on commercial off-the-shelf (COTS) hardware. Rather than utilizing an integrated hardware and software appliance, VNFs disaggregated the hardware from the software. As such, it became possible to procure the hardware from one vendor and the software from another. It also became possible to separate the hardware and software upgrade cycles. 

However, while VNFs offered advantages over PNFs, VNFs were still an intermediate step. First, they typically needed to be run within a virtual machine (VM), and as such required a hypervisor to interface between the host operating system (OS) and the guest OS inside the VM. The hypervisor consumed CPU cycles and added inefficiency. Second, the VNF itself was frequently designed as a monolithic function. This meant that while it was possible to upgrade the VNF separately from the hardware, such an upgrade, even for a feature that affected only a portion of the VNF, required deployment of the entire large VNF. This created risk and operational complexity, which in turn meant that upgrades were delayed just as they were with PNFs.

Creating the foundation for cloud networks

The trick to establishing your cloud based network resides in the challenge of moving from VNFs to containerized network functions (CNFs) — network functions organized as containers as a collection of small programs, each of which can be independently operated. 

The concept of containers is not new. In fact, Google has been using containerized workloads for over 15 years. Kubernetes, which Google developed and open-sourced, is the world’s most popular container orchestration system, and is based on Borg, Google’s internal container management system.³ There are lots of benefits to using containers, but fundamentally, it frees developers from worrying about resource scheduling, interprocess communication, security, self-healing, load balancing, and many other tedious (but important!) tasks. 

Consider just a couple examples of benefits that containerization brings to network functions. First, when upgrading the network function to implement new features, you no longer need to re-deploy the entire network function. Instead, you only need to re-deploy the containers that are affected by the upgrade. This improves developer velocity and reduces the risk of the upgrade because, rather than infrequent upgrades that each introduce substantial changes, you can now have frequent upgrades that each deploy small changes. Small changes are less risky because they are easier to understand and to roll back in case of anomaly. Incidentally, this also improves your security posture because it reduces the time between when a security vulnerability is discovered and when a patch is deployed.

Speaking of security, another example of the benefits that containerization brings to network functions is an automatic zero-trust security posture. In Kubernetes, the communication among microservices can be handled by a service mesh, which manages mundane aspects of inter-services communication such as retries in case of failure and providing observability into communication. It can also manage other essential aspects such as security. For example, Anthos Service Mesh, which is a fully-managed implementation of the open-source Istio service mesh (also co-developed by Google), includes the ability to authenticate and encrypt all communications using mutual TLS (mTLS) and to deploy fine-grained access control for each individual microservice.

Automation and orchestration for cloud networks

CNFs bring tremendous benefits, but they also bring challenges. In place of a relatively small number of network appliances, we now have a large number of containers, each of which requires configuration, management, and maintenance. In the past, many of these processes were accomplished using manual techniques, but this is impossible to accomplish economically and reliably at the scale required by CNFs.

Fortunately, there are cloud-native approaches to solving these challenges. First, consider the problem of autonomously deploying and maintaining CNFs. The ideal way is to use the concept of Configuration as Data. Unlike imperative techniques such as Infrastructure as Code, which provide a detailed description of a sequence of steps that need to be executed to achieve an objective, Configuration as Data is a declarative method whereby the user specifies the desired end state (i.e., the actual desired configuration) and relies on automated controllers to continuously drive the infrastructure to achieve that state. Kubernetes includes such automated controllers, and the great news is that this method can be used not just for infrastructure but also for the applications residing on top of it, including CNFs. This cloud-native technique frees you from the toil and associated risk of writing detailed configuration procedures, so you can focus on the business logic of your applications.

As another example, consider the problem of understanding your network performance, including anomaly detection, root cause analysis, and resolution. The cloud-native approach starts with creating a data platform where both infrastructure and CNF monitoring data can be ingested, regularized, processed, and stored. You can then correlate data sets against each other to detect anomalies, and with AI/ML techniques, you can even anticipate anomalies before they happen. AI/ML is likewise indispensable in gaining an understanding of why the anomaly is happening, i.e. performing root cause analysis, and automated closed-loop controllers can be developed to correct the problem, ideally before it even happens.

Architecting for the edge

The transition from VNFs to CNFs is a critical piece in addressing the challenge that CSPs face today, but it alone is not enough. CNFs need infrastructure to run on, and not all infrastructure is created equal. 

Consider a typical 5G network. There are some functions, such as those associated with an access network, that need to be deployed at the edge. These functions require low latency, high throughput, or even a combination of the two. In 5G networks, examples of such functions include the radio unit (RU), distributed unit (DU), centralized unit (CU), and the user plane function (UPF). The first three are components of the radio access network (RAN), while the last is a component of the 5G core. At the same time, there are some other control plane functions such as the session management function (SMF) or the authentication and mobility management function (AMF) that do not have such tight latency and high throughput requirements and can thus be placed in a more centralized data center. Furthermore, consider an AI/ML use case where a particular model (perhaps for radio traffic steering) needs to run at the network edge because of its latency requirements. While the model itself needs to run at the edge, model training (i.e., generating the model coefficients) is frequently a compute-intensive exercise that is latency-insensitive and is thus more optimal to run in a public cloud region.

All of these use cases have one thing in common: they call for a hybrid deployment environment. Some applications must be deployed at the edge as close to the user as possible. Others can be deployed in a more centralized environment. Still others can be deployed in a public cloud region to take advantage of the large amount of compute and economies of scale available therein. Wouldn’t it be convenient — if not transformational — if you could use a single environment for deploying at the edge, in a private datacenter, and in public cloud, with a consistent set of security, lifecycle management, policy, and orchestration resources across all such locations? This is indeed what Google Distributed Cloud, enabled by Anthos, brings to the table.

With Google Distributed Cloud, you can architect a 5G network deployment such as the one shown below.

Business benefits of cloud networks

Beyond the technical benefits, consider the business benefits of such an architecture. First, by following the best practices of hardware and software disaggregation, it permits the CSP to procure the infrastructure and the network functions from different vendors, spurring competition among vendors. Second, each workload is placed in precisely the right location, enabling efficient utilization of hardware resources and offering compelling low-latency, high-throughput services to users. Third, because the architecture utilizes a common hybrid platform (Anthos), it makes it easy to move workloads across infrastructure locations. Fourth, the separation of workloads into microservices accelerates time-to-market when developing new features or applications, such as those enabling enterprise use cases. And finally, the container management platform supports the simultaneous deployment of both network functions and edge applications on the same infrastructure, allowing the operator to deploy new experiences such as AR/VR directly on bare metal as close to the user as possible.

The next generation cloud network is now

There is a lot more we could say, but perhaps the most important takeaway is that this architecture is not a future dream. It exists today, and Google is working with leading CSPs and network vendor partners to deploy it, helping them realize the promise of 5G to deliver new revenues, reduce operating costs, and enable new customer experiences.

To learn more, watch the video series on the cloudification of CSP networks.

Discover what’s happening at the edge: How CSPs Can Innovate at the Edge.

^{1.Statista, Forecast growth worldwide telecom services spending from 2019 to 2024
2 PricewaterhouseCoopers, Global entertainment and media outlook 2021-2025
3. Borg: The Predecessor to Kubernetes}

Editor’s note: This post was originally published in TechPulse Belgium, where Jack Buser, Google Cloud’s Director of Game Industry Solutions, shared his trends for the industry this year.

The year 2022 will hold many surprises (with a few already dropping!), but there’s one near certainty: By this time next year there will be millions more gamers worldwide. It’s thanks to the industry’s little-noted technology growth drivers, which are getting stronger. 

Games are no longer a niche hobby; it’s global, mainstream entertainment. There were 3.1 billion gamers worldwide in 2020. It’s estimated that by 2024 there will be another 500 million. With a global population of 7.7 billion, that’s nearly half the planet. Revenues in 2021 may reach $175 billion—likely more than movies, music, and books combined. 

It’s a strength unique to the games industry, driven by the way its creators deploy new technology. Advances we see in everything from pricing and emerging markets, to backend computing and delivery suggests it will grow from here, on things like artificial intelligence and planet-scale networks.

Games forever, game better

It’s no surprise that videogames have become a huge business. Playing games is one of our deepest human expressions. There is a 4,500 year old board game that people still play, a testimony to the human love of testing cleverness, honing reactions, building trust, or just enlivening a day.

Video games do all that and for decades game makers have taken the most cutting-edge computing tech to make things better. We’ve seen electronic ping pong become shooting asteroids, to simple (yet iconic) 2D sprites evolve into believable, expressive 3D characters, all leading to today’s immersive worlds hosting tens of millions of players worldwide.

There’s more to come. I work at technology’s cutting edge for games, offering the world’s leading game creators access to powerful cloud computing, a low-latency global network, Machine Learning and AI, and much more. As usual, game innovators are taking up all these tools, building new narratives, new ways to play, new markets, and new jaw-dropping moments.

And game creators aren’t just thinking about players; they’re also thinking about viewers. More and more people are watching eSports, and creators are using the games medium to engage their viewers in increasingly immersive ways.

Streaming like never before 

AI and ML are sharpening games and attracting new players in other ways. These technologies increasingly power everything from network performance to global player matching. The delicate but essential work of encouraging newcomers and discouraging bad actors, optimizing monetization, or building and scaling up new player environments.

Another important area we’re seeing cloud tools being put to use is in coordinating the workflows of global teams of developers. It’s not just a question of building faster anymore, but of collaborating and responding more effectively. The distributed work trends that featured so strongly during the pandemic had already featured in much of the game industry, increasingly it’s an industry standard. This will likely mean even faster and more diverse game development, addressing the needs of new markets where we’re seeing double-digit growth.

Tools like cloud streaming, 5G, and edge computing are likely to accelerate the use of Game Streaming, Augmented Reality, and other types of immersive gameplay. If my metaverse doesn’t have games, count me out! We’re looking at some exciting new developments in coming months with some major industry players. Stay tuned.

A big year for the games industry

As a premier provider of solutions, tools and services to games companies, our engineering job is complex and global. Fortunately, our mission is simple: Help game companies transform to meet new global opportunities with planet scale solutions. Technology has brought the gaming industry to astonishing heights, and is the perfect compliment to great storytelling and creative ingenuity. 

As a decades-long industry veteran, I’ve never been more impressed with what technology can do for our industry. Looking past 2022, when we’ll see more compute, more AI, more AR, and more innovation around healthy and exciting game play, there is just one near certainty: More growth.