Electric vehicles already account for one in seven car sales globally, and with new gas and diesel cars being phased out across the world, global sales are forecast to reach 73 million units in 2040. But with power grids becoming increasingly dependent on variable energy sources such as wind and solar, rising demand from electric vehicles risks overstraining grids at peak times, potentially leading to power outages.

At Kaluza, we believe that our platform has a vital role to play in helping power grids and utility companies to stabilize their networks, while at the same time delivering more affordable, cleaner energy to the consumer. Powered by Google Cloud, the advanced algorithms behind our Kaluza Flex solution automatically charge electric vehicles when the power supply is at its cheapest and greenest, helping to accelerate the global transition towards a zero-carbon future. 

Decarbonizing the grid with low-cost smart charging programs

Launched by OVO Energy in 2019, Kaluza has taken its deep understanding of the energy market to partner with some of the world’s major energy suppliers and vehicle manufacturers, including AGL in Australia, Fiat and Nissan in the UK, and Mitsubishi Corporation and Chubu in Japan, to launch smart charging programs that help customers save money while reducing their carbon footprint.

A good example of this is Charge Anytime, which we recently launched with OVO Energy in the UK. With this tariff, customers use Kaluza to smart-charge their electric vehicle, and pay just 10p per kWh — a third of their household electricity rate — to do so. This means that if the customer plugs in their vehicle to charge when they get home from work at, say, 6:00 p.m. — a time when both demand and the carbon intensity on the grid are at their highest — their vehicle will then be smartly charged at the lowest cost and greenest periods throughout the night, ready for when they need it in the morning. 

This smart charging reduces the energy company’s costs by enabling them to take advantage of lower wholesale electricity prices. These savings are then passed on to the end customer through tariffs such as Charge Anytime, saving customers hundreds of pounds a year and reducing their carbon footprint. Meanwhile, the National Grid is able to reduce the strain on the network during peak hours, while simultaneously using up the excess renewable energy that might otherwise have gone to waste. 

Optimized charging schedules, fueled by Google Cloud

Behind Kaluza’s smart charging solution lies some sophisticated technology, all of which is built on Google Cloud. Our core optimization engine gathers real-time data from a wide range of sources, including battery and charging data from the electric vehicles, and data from the energy suppliers and grid operators, such as the carbon intensity, and price forecasts. 

After passing through our real-time data backbone, that data is stored in BigQuery where it’s used to train and validate our smart charging optimization models. These models are then deployed with Google Kubernetes Engine so that whenever a customer plugs in an electric vehicle, data from that vehicle passes in real-time through our optimization engine to calculate the ideal charging schedule for that vehicle, ensuring it uses the cheapest, least carbon-intensive energy available.

Customer interface: easy to use, simple to build

Of course, the customer isn’t aware of any of this complexity. All they need to do is open their charging app and use Kaluza’s intuitive interface to set what time they want their car to be ready and how much charge they want in their battery. Then they simply plug in their car, and our algorithms take care of the rest. 

Customers can also use Kaluza to view breakdowns of how much carbon and money they’ve saved, along with insights around things like billing and battery life, all of which is backed by Cloud SQL. 

With Google Cloud, we were able to roll out this end-user app very quickly. Instead of having to build a different version of the app for each operating system, we were able to build an OS-agnostic version using Flutter, which then builds the app for each platform, enabling us to get to market faster. 

This has been a benefit of our architecture in general. With Google Cloud taking the complexity out of otherwise time-consuming development processes, we’ve been able to experiment with and validate propositions rapidly, and roll out new products and features at speed, to ensure that we remain at the vanguard of a rapidly evolving sector. 

Giving energy companies full visibility with BigQuery and Looker

As for the grid operators and energy companies, the Kaluza platform allows them to visualize how many participating electric vehicles are plugged into the network at any one time. BigQuery and Looker Studio dashboards provide granular insights, such as how many vehicles are idle, how many are charging, and how well our optimization engine is working. 

The platform also allows companies to view those vehicles on an aggregate level, and identify any issues. Google Cloud machine learning capabilities can even allow grid operators to use this aggregate view to forecast how much energy will be required at any one time, and dial the power generation up or down accordingly. 

Ultimately, these insights help network operators and utility companies to optimize energy usage, and balance out the peaks and troughs of supply and demand, ensuring that excess renewable energy is captured, while carbon-intensive fuel use is reduced. 

Feeding energy back into the grid with bidirectional charging

Vehicle-to-Grid (V2G), or bidirectional, charging is something that we are very excited about, and have begun rolling out in the UK, as we prepare to launch in other global markets. Built on the same Google Cloud architecture as the rest of Kaluza Flex, V2G not only enables smart charging, but allows electric vehicles to feed stored energy back into the grid. 

Imagine an electric vehicle battery that can store 40 kWh of energy, but only uses 5 kWh a day. That leaves 35 kWh a day that can be charged to the battery during times of low demand, when energy is cheapest and greenest, then fed back into the network during peak periods, removing the need for more fossil fuels to be burned to meet demand. 

Not only does V2G go further than conventional smart charging to make use of renewable energy when it’s abundant and support the balancing of the energy system, it also results in even lower energy prices for the customer, as they are effectively selling energy back to the grid. For example, as part of a large domestic V2G trial made in partnership with OVO and Nissan, Kaluza saved customers an average of £450 a year, with some customers saving up to £800/year by selling surplus energy back to the grid — transforming their homes into mini power stations.

With Kaluza Flex, we have a platform that offers benefits for all parties, from the grid operators, through to energy retailers and vehicle manufacturers, and all the way to the customer. Now, our aim is to bring this exciting offering to as many markets as possible, a goal which is made easier thanks to the scalable infrastructure and time-saving solutions of Google Cloud. 

As more people make the switch to electric vehicles, our goal is to ensure that smart charging becomes standard practice, as we help to deliver on the potential of electric vehicles to contribute to a greener, decarbonized future.

If you’re looking to migrate and modernize your Microsoft and Windows workloads, Google Cloud is your premiere destination. No matter what migration strategy you’ve selected or what value you’re looking to achieve, with Google Cloud you’re able to:

• simplify your migration and modernization journey
• reduce your on-prem footprint and increase agility
• optimize license usage to reduce costs
• modernize to reduce single-vendor dependencies
• rely on enterprise-class support backed by Microsoft

Whether you’re looking to migrate applications running on Windows virtual machines, adopt Windows containers in Google Kubernetes Engine (GKE), convert SQL databases to Cloud SQL, or something else, Google Cloud offers you the first-class experience you need. 

But we don’t want you to take our word for it. Try it out yourself with our new online Microsoft and Windows on Google Cloud Demo Center without any commitment or friction.

The demo center uses hands-on guided simulations to walk you through several scenarios for solving business critical challenges with Google Cloud’s Microsoft and Windows solutions. Because these are all simulated, you’ll see how it works without any deployment, configuration, or commitment. It’s a seamless way for you to see exactly how Google Cloud can help you.

Run dedicated hardware and optimize with sole tenant

Sometimes you might want to run your workloads on dedicated hardware (with oversubscription options) for compliance, licensing, and management. Google Cloud provides sole-tenant nodes that allow you to easily deploy your virtual machines onto dedicated machines to avoid “noisy neighbor” issues, address regulatory or licensing constraints, and optimize inter-VM communications.

Plus, the CPU Overcommit option allows oversubscribing sole-tenant node resources by up to 2x, therefore helping save on per-physical core licensing for many licensed workloads like SQL Server.

Learn how to set up a sole tenant group and node.

Optimize license costs with premium images & custom VMs

One of the easiest ways to optimize your cloud experience with virtual machines (VM) is to pick the right VM image. Google Cloud provides premium license-included VM images that are thoroughly tested and optimized, including SQL Server options with pay-as-you-go licensing. These are great for workloads that don’t need to run all the time or when you do not have spare licenses for bring your own license (BYOL). 

Explore some Windows & SQL images and learn how CPU/Memory options can help optimize deployment and save on licensing.

Modernize your databases with Managed SQL Server

Sometimes you need to manage your SQL Server instance to achieve certain business or operational goals. But more often, managing SQL Server deployments can be undifferentiated: backups, high-availability, updates, and patching are just some of the many things you have to take care of when going the do-it-yourself route. One way to modernize your database tier is to migrate to a managed service like Cloud SQL, which is a fully managed Relational Database service for SQL. 

Explore the process of creating an instance in just a few clicks!

Extract apps from VMs and move to containers in GKE with Migrate for Anthos

Many Windows workloads running on virtual machines such as Internet Information Services (IIS) are ideal candidates for migrating to containers without major changes like rewriting or rearchitecting. However, doing this migration manually can be tedious, which is why Migrate for Anthos can help easily re-platform a .NET app running on IIS into a container-based app. 

Simulate intelligently extracting, migrating, and modernizing applications to run natively on containers in GKE and Anthos clusters.

Move .NET applications to GKE on Windows without code changes

When you’re looking to go fully cloud native, you can leverage Windows containers in GKE without rewriting your .NET applications. Simply create clusters with Windows nodes and deploy containerized Windows workloads in a few clicks, even alongside Linux containers. These deployments reduce operational overhead with features such as auto-upgrade, auto-repair, and release channels. 

Learn  how easy it is to build a GKE cluster with a Windows node and deploy an app. 

Now that you’ve gotten a feel for what’s available, go check out the Demo Center. You can also visit us at Windows and Microsoft on Google Cloud to learn 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}