How Cloud Networks Enable CSPs to Deliver 5G
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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
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Every business is a digital business. That’s what you’ll hear from technology folks these days. But, what exactly is a digital business? How does one define it?
Simply put, digital businesses are those that have thoroughly capitalized on the opportunity to connect people with technology. There are four parts to a digital business:
Real-time data and analytics: To stay relevant in the age of Big Data, businesses must analyze copious amounts of data to derive actionable insights—both from historical data, and in real time.
Fast, flexible application development: Rapid and continuous delivery of software to your stakeholders is no longer optional. Businesses need platforms for their applications strategy — from using container-based development tools to fully managed serverless platforms.
Secure, reliable infrastructure: How secure is your on-prem datacenter? What happens if it goes down? How many dedicated security engineers do you have on staff? What’s the cost of a system upgrade? What digital businesses need is a secure and reliable infrastructure that can power their applications.
Constant collaboration and productivity: Digital businesses are designed to keep teams seamlessly connected not only to each other, but also to the applications that keep the company running. This enables everything and everyone to work together, no matter where they sit—across the office or across the ocean.
Download this infographic to know more.
Vizrt’s Story of ‘Lift and Shift’ and Delivering Phenomenal Performance with Google Cloud
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When moving software applications from on-premise hardware to the cloud, it often “just works,” but it’s never guaranteed. This is especially the case for applications that are hardware intensive. This blog post examines what happened when a media company took software for real-time video broadcasts into the cloud. We’ll share how we, Google Cloud, collaborated with media software provider Vizrt to meet the demanding requirements of an eSports broadcaster. Together, we delivered a solution that not only met but exceeded the expected performance from a cloud-based deployment.
Video broadcasting in the cloud
Video broadcasts are a very hardware-intensive workflow. By needing to process and store data streams in near-real-time, broadcasts stress GPU, memory, disk, and CPU. In addition, the performance requirements quickly increase as producers add additional video streams into the mix, such as in this case, an eSports broadcast.
Vizrt’s customer wanted to increase their broadcast production by doubling the amount of camera feeds from 8 to 16, to have a more compelling and elaborate production.
At the heart of the eSports broadcasters’ production was Viz Vectar Plus, Vizrt’s software-based 4K switcher. While the client wanted to move more of its production into the cloud, Viz Vectar Plus was designed initially for on-premise hardware. So, when they tried a straightforward “lift and shift ” to the cloud, it surprised no one that the software didn’t run as well. They turned to Google Cloud and Vizrt to make it run the way they needed it to.
Troubleshooting lift and shift
Initially, we suspected that the issue could be in the design of the cloud deployment, i.e., the configuration of the VM hosting the software. So the focus of our troubleshooting was to find a cloud configuration that 1) made sure the software worked to spec and 2) did so optimally considering costs, robustness, and performance. Furthermore, we wanted to ensure that all components met performance specifications, particularly throughput, IOPS, and network bandwidth, as this was a video media application. Only after we validated the cloud deployment would we ask Vizrt to investigate the code itself. We would:
- Set up a test environment.
- Benchmark the environment.
- Test various configurations.
- Validate that the vendor software was optimally using the configuration.
This high-level methodology is straightforward. However, we approached the details in a particular order, considering we were optimizing for broadcast video. We honed in on the optimal cloud configuration by testing the following elements, prioritized in order of expected impact:
- VM type: The VM type largely dictates the available memory and CPU configurations. However, because this was a VM workflow, we had to pick N1’s. Today, they are the only VM type that can be attached to GPUs, which are practically a requirement for broadcast video.
- Disk type: Video broadcasts require high I/O speeds to handle high-quality video streams. We went from an HDD to a much-faster SSD.
- CPU size: We increased the VM CPU cores from 16 in increments up to 32. Increasing CPU size indeed increased performance, but did not return the level of performance we needed.
- SSD size: We increased the SSD size (and the accompanying higher IOPS and throughput that comes with increasing the size) enabling more simultaneous recordings. Again, this only partially worked.
- Disk count. We noticed read/write problems when the application was reading/writing with a single drive. There are two typical ways to approach this: 1) Separate read/writes tasks among two discs and 2) striping the data streams across discs. Implementing these had improved but marginal improvements in performances.
After our testing, we arrived at the following optimal configuration:
- VM: n1-standard-32 instance w/ 500Gb boot drive
- GPU: 1 T4 GPU
- SSD: 1 persistent disk with 1TB*
* We would later determine that two SSDs for separate read and write operations would be more optimal
This configuration was able to produce between 6 – 12 streams. Compared to the on-premise target of 8 streams, this was about as good but was not the customer’s target of 16. So we would need Vizrt to take the ball from here to optimize the software itself.
Optimizing media applications for cloud
We provided Vizrt our recommended configuration, performance notes, and the following best practices that are generally applicable to cloud-based video workloads:
- Separate read and write operations to two different disks to enable higher performance for both operations.
- A second 1 TB SSD persistent disk can be attached to the VM instance to increase performance.
With this information, Vizrt engineers worked their magic, providing daily patches to test; with each daily iteration the overall solution was found quickly. Not only were they able to meet the broadcaster’s request of 16 feeds, but they were also able to go even further to 44. Over a 5x improvement by optimizing for the cloud!
Teamwork in troubleshooting
Because of the specialized nature of media and entertainment, workflow situations across multiple companies are common as specialized applications hand their work from one to another.
“By working in partnership with Google Cloud we managed to build a system that can scale in ways that probably none of us thought would be possible. This allowed Viz Vectar Plus to run fully in the cloud using NDI and opened up amazing possibilities for making shows,” Dr. Andrew Cross, President R&D, Vizrt Group. “We ended up with great feedback from the customer, who were appreciative of how Google Cloud and Vizrt collaborated on a solution.”
The results speak for themselves: A satisfied customer with over a 5x improvement in results. That’s what we call a good game.
How to Get Your Cloud Migration Journey Started Off on the Right Foot
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When moving to the cloud, many organizations concentrate their focus on the change in technology, and overlook an area just as complex: cultural change.
At Google, we’ve spent years nurturing our culture and workforce to best operate in the cloud, and the Google Cloud Professional Services team leverages the lessons we’ve learned for the benefit of enterprise customers embarking on their own cloud journeys.
While it can be tempting to believe in a universally ‘correct’ strategy for change management, there is no one-size-fits-all answer. Every organization will have its own unique considerations. But with that said, there are some core strategies we’ve found to be relevant and useful across a broad range of businesses.
1. Define your purpose for moving to Cloud
While pockets of cloud use and experimentation can evolve independently and in parallel across an organization, it’s important to make some deliberate decisions before starting a larger migration. At this stage, we recommend having a detailed answer to two key questions to ensure a successful cloud migration:
- Where do you want to go? (Or “What’s your cloud vision?”)
- How do you plan to get there?
Start by having a conversation with leaders and those who will be key to the journey about how far you want to push your cloud vision. This alignment ensures everyone is on the same page—and will provide greater direction, allowing more deliberate action.
2. Find the change path which is right for you
Whether a ‘lift and shift’ approach to the cloud is right for you, or a more transformative approach with a lot of re-architecting—the most important thing is to find the flavor of change which is appropriate to your context and level of ambition.This will both shape your key migration activities, but also the level of impact to be managed within your organization.
There are many ways to embark on a change journey for cloud migration (which one can find in the chart below). It is important to deeply understand the needs of your business and its people and determine what strategy makes the most sense.
3. Learn from best practices
Based on the lessons we’ve learned along our own journey, and the work we’ve done with customers, there are a number of recommendations we can share that can make a cloud migration more successful. We go into these in more detail in our new whitepaper, but below you can find the ones we think are most relevant:
- Share the vision—and measure, measure, measure. Once you’ve crystallised your cloud vision with leadership and key stakeholders, share that vision widely. Set success goals and communicate them to hold yourself accountable.
- Be clear about the capabilities you will need in the future—and where you’ll get them. For example, if your vision is to become a cloud-first, data and AI-led organization, ensuring you have the right data science skills and machine learning capabilities in your organization to achieve that vision becomes a critical step—be they home-grown or bought-in.
- Find the right balance between capabilities that should be under central control, and capabilities that should be decentralized, or agile. For example, should machine learning be something that sits centrally, or should it be spread across your organization? For every business, the solution will be a little different, and there’s no “one true answer.” There’ll be lots of different opinions about this, so the sooner the conversation starts, the better.
- Start thinking about the needed tech and non-tech skills now, and how you’ll fill the gaps. Building the tech skills will take time, and not everyone will feel comfortable with the future picture of collaboration, innovation, and agility.
To help businesses navigate their own cloud journeys, Google Cloud Professional Services has released a new whitepaper that can help guide organizations. “Managing Change in the Cloud” is closely aligned with the Google Cloud Adoption Framework and is a practical guide for organizations looking to maintain momentum in their cloud adoption. You can download the whitepaper here.
Google Cloud Featured at TechCrunch Disrupt 2021
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Startups need to move quickly and focus their limited resources on areas where they can differentiate. If infrastructure isn’t your differentiator, don’t put a lot of energy into your infrastructure when someone else can do it for you. What’s more, time-to-market matters to startups more now than ever. Successful early stage companies know this, and leverage existing tools, libraries, frameworks and innovations whenever possible.
Next week Google Cloud will be featured at TechCrunch Disrupt, the iconic annual event where “founders and investors shaping the future of disruptive technology” come together to share stories, network, and learn from each other. Google Cloud will host a session and Google Cloud engineers will be available in a virtual booth to answer startup questions.
The session, “Demo Derby – How startups are disrupting the status quo with innovative data analytics, AI and modern app development” will be a fun, fast-paced set of presentations showing short demos of startups and startup projects built with Google Cloud.
Demo Derby Presenters
- Andrea Le Vot, Chief Data Protection Officer with startup BlueZoo will deliver a demo showing how they are revolutionizing the property insurance industry with AI and Cloud – and doing so while actually protecting people’s privacy.
- Dale Markowitz and Zack Akil, Applied AI Engineers, will show how easy it is to use AI for everything from video search to editing to automated translation.
- Vidya Nagarajan, Group Product Manager from Google Cloud will deliver a fast paced demo that shows how startups can drive developer productivity with serverless innovations.
“Disrupt is iconic for its engagement with founders, investors, and the broad community of early stage companies,” said Andrea Le Vot, Chief Data Protection Officer at BlueZoo. “We are thrilled to share our insights with others in our community, and to show how we have partnered with Google Cloud to innovate faster than most of the larger, well funded insurance companies in our market.”
The series of snippet sized demos will be followed by a roundtable discussion with the demo developers focused on lessons learned, best practices, how to reduce time-to-market, and how to focus on where you can most effectively differentiate.
The session will air on Day One of the event: Tuesday September 21, 2021 at 1pm PT and will be available on demand after the event for all Disrupt attendees.
Gartner Names Google Cloud a Leader in the 2019 Cloud Infrastructure as a Service Market
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As increasing amount of cloud IaaS is being bought for traditional IT, with an emphasis on cost reduction, safety and security, service providers are offering a high-quality service, with excellent availability, good performance, high security and good customer support.
In its Magic Quadrant for Cloud Infrastructure as a Service (IaaS), Gartner notes that the distinctions among providers are apparent in the market for cloud IaaS in terms of worldwide enterprise adoption, capabilities and service availability. Infrastructure and operations leaders should evaluate providers with broad capabilities and a positive track record for customer success.
Download the Gartner Magic Quadrant to understand why Google is a leader in the market.
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