Accelerating digital transformation

Digital transformation is what propels businesses and industries forward. Organizations of all sizes—from startups to global enterprises—focus on digital transformation not only to make scaled improvements, but also to drive significant change and fully embrace the digital age. The pandemic has jump started and pushed many organizations into full gear to digitize their business models and transform with increased business agility, resiliency, and velocity, while driving new innovation and business values for the customers.

However, according to the Boston Consulting Group, only about 30% of companies navigate a digital transformation successfully. Many large scale digital transformation programs failed because of lack of clear business priorities, top-down executive sponsorship, or dedicated resources and commitment to see it through.

Laying the foundation for digital transformation success

Digital transformation drives foundational change in how an organization operates, optimizes internal resources, and delivers value to customers; however, this doesn’t just happen overnight. Digital transformation requires a programmatic approach through an incremental yet agile, cost-effective, value-driven, and sustainable strategy to drive successful transformation across the organization.

One of the critical factors foundational to success is Cloud FinOps (Cloud Financial Operations). Cloud FinOps is an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and accelerate business value realization through cloud transformation. In the context of Digital Transformation, it requires new ways of working and operating models to drive behaviors and cultural change that foster cross-functional collaboration, drive accountability, provide greater cost transparency, and promote a blameless culture.

Most importantly, Cloud FinOps serves as an enabling function to drive successful digital transformation programs and enable business agility by breaking down the boundaries between technology, finance, and business teams. Through this cross-functional team collaboration, technology leaders partner with finance and business leaders to better understand the technology investments to create sustainable business outcomes. By doing so, business priorities become more clear and the focus shifts to value creation, customer-centricity, and innovation.

As such, companies are reinventing their business models to fund value streams and connect cloud technology investments to strategic business outcomes. With the increased visibility of the cloud costs, finance teams are also gaining greater accuracy in tracking cloud spend against budgets. Organizations can align the TCO of the technology services to the value metrics to make better informed future investment decisions and forecast demand.

Cloud FinOps to accelerate business value realization

The successful deployment and implementation of Cloud FinOps building blocks will enable organizations to accelerate digital transformation beyond cost savings, including the ability to:

• Accelerate business value realization and innovation
• Drive financial accountability and visibility
• Optimize cloud usage and cost efficiency
• Enable cross organizational trust and collaboration
• Prevent cloud spend sprawl
• Break down of departmental silos

Organizations that are successful in digital transformation most often have established processes to measure and track business value. One of the key building blocks of Cloud FinOps is “Measurement & Realization.” By establishing a robust value measurement approach to track and monitor the business value metrics toward business goals, we are bringing technology, finance, and business leaders together through the discipline of Cloud FinOps to show how digital transformation is enabling the organization to create new innovative capabilities and generate top-line revenue.

Business value metrics fall across several factors: cost efficiency, resiliency, velocity, innovation, and sustainability. We suggest assigning KPIs to the following metric categories:

Cost efficiency: Measure cost efficiency through infrastructure savings, migration, and support costs. Customers will commonly start with metrics such as cost of compute and storage per day-week-month, and evolve to unit metrics such as cost per customer served or cost per transaction, where the cost of an application stack is aligned to customer drivers.

Resiliency: Enhance operational resiliency with improvement in service quality and security risk posture. Traditional measures such as system service level and the frequency and duration of critical downtime events are effective measures of IT durability. Customers can also augment these metrics by associating a cost per minute of downtime events, reflecting not only the direct impact of these events but opportunity costs as well.

Velocity: Decrease time to market by accelerating fluidity in product and service delivery. By moving to a cloud-based microservices architecture, customers commonly achieve benefits of increasing software release frequency, as well as being able to run many more test scenarios prior to release, resulting in higher quality code. As an example, our recent Google’s State of DevOps Report 2021, shows that elite performers have 973x more frequent code deployment and release frequency than the low performers.

Innovation: Enable a culture of rapid experimentation to drive innovation and cloud transformation. With cloud technology, companies can avoid the financial constraints of fixed cost investments and lengthy procurement lead times. As a result, the marginal cost of experimentation and time from ideation to experimentation can drop significantly while the number of experiments per unit of time can grow dramatically.

Sustainability: Embed true environmental and social sustainability metrics across the organization by adopting a circular economy strategy and building sustainability into everything we do – from running applications on zero net emissions virtual machines to reducing carbon footprint with enhanced productivity and collaboration services. According to Accenture, companies with average on-premise to cloud migrations can drive 65% energy reduction and carbon emission reduction of 84%1.

Getting started

The Cloud FinOps journey starts with defining or updating your metrics. Since business goals and strategic imperatives will likely change over time, it is important to review the Cloud FinOps metrics whenever the goals change. The review of metrics should include the changes in business goals when there are changes in the internal priorities of the team. Executive leaders need to identify dependency relationships between technology and business outcomes to improve the impact of metrics on decision making and to better prioritize and invest in evolving business and technology capabilities. Defining good metrics is not just about aligning to business goals and demonstrating value. It is also important to help prioritize strategic initiatives, guide effective resource allocation, and generate awareness across the organization to drive a shift in mindset with the new way of operating in the cloud.

The pandemic has accelerated the need for companies to modernize their digital capabilities. With technology-driven disruptions across all industries, it has never been more important for organizations to transform themselves, embrace an agile mindset, and make bold investments in cloud technology and capabilities to achieve sustainable business outcomes.

So, where are you now in your cloud FinOps journey, and how do you move beyond the challenges ahead? Google can help you start the conversation and accelerate your path to maximizing business value with the cloud.

No matter where you are on the cloud transformation journey, through an interactive session with Google, we can bring executives across the organization together to work toward a shared vision and a plan to accelerate and realize business value in the cloud. If you are interested in more information, please contact us.

Special thanks to Pathik Sharma, Bruce Warner, Jon Naseath, and Nihar Jhawar for their contributions and sharing their domain expertise to this important Cloud FinOps topic.

About inshorts

Mobile news platform inshorts condenses the latest national and international news into 60-word briefs in English and Hindi. With 10 million downloads so far, it’s expanding to cater to millions more on-the-go Indians reading news on mobile devices.

Industries: Media & Entertainment

Location: India

Across India, hundreds of millions of people turn to their smartphones for news that will enhance their lives and help them achieve their goals. According to Professor Rasmus Kleis Nielsen, Director of the Reuters Institute for the Study of Journalism: “The past few years have seen explosive growth in mobile internet access, and the rapid move to digital media will have profound implications for the practice of journalism, the business of news, media institutions, and thus by extension political and public life in India.” The Institute reports that Indians with internet access have risen from 100 million to 500 million in the past decade alone. The report identifies India’s news industry as “a mobile-first market,” with 66% of Indians citing smartphones as the device they most frequently use to access online news.

Seeing an opportunity to meet the growing need for news on the go, inshorts developed a mobile news platform for Indians on the move who want to stay informed but don’t have time to read long articles. The inshorts solution condenses news, from politics to cricket, into briefs of under 60 words and has been well received, with 10 million downloads since launching in 2013.

To keep the attention of its always-on news consumers and attract more users for continued growth, inshorts knows it must continuously evolve and improve its platform. The company turned to Google Cloud in 2016 in order to free its developers from time-consuming infrastructure maintenance tasks and enable them to focus on creating innovative applications instead. It was also looking for powerful yet cost-effective data and scaling tools to reach more remote corners of India and found this in Google Cloud.

“There’s a new wave of 300 to 400 million first time mobile users projected for India,” says Manish Bisht, Head of Technology at inshorts. “We want to capture those users, and Google Cloud is helping us succeed.”

Freeing up resources for new solutions

By migrating to Google Cloud, inshorts has been able to free up its development team to experiment with new ideas, without worrying about backend management or costs. The company worked with cloud consultancy Searce, a Google Cloud Premier Partner, to define the best solutions for achieving its goals. Searce partners with clients to help them scale their business by leveraging Cloud, AI/ML, and data analytics while reducing the operational IT infrastructure spend. Because it specializes in AI/ML, Anthos, and Cloud Search, inshorts chose it as the ideal partner for futurifying its business on Google Cloud. According to Manish, the Searce team was focused not only on helping inshorts to migrate and adopt a on-demand computing mindset, but helping it to decrease monthly costs as well. “There’s always a positive push from Searce, to help us move forward, and to really put our company’s priorities first,” he says.

Through the application development solutions of Firebase, for example, inshorts is able to test new features in a rapidly shifting market. The product’s ease-of-use and built-in data analytics mean that inshorts can now allocate its developer resources more efficiently across multiple projects, instead of needing an entire team to focus on one project. Product cycles are now just one week long, instead of one month, and each backend and frontend developer in the team of four is able to focus on a separate project, meaning that more work gets done in a shorter time.

Dataflow, which enables real-time data processing, has been a major factor in allowing inshorts to instantly recommend content to users. Previously, the company had managed its own instances, first manually recording what users were doing and later pushing out recommendations. That all changed with Dataflow. “Dataflow freed a lot of our development and instance management time, because we can process data in real time now,” says Manish. “And that means giving users what they want instantly.”

Perhaps one of the biggest time savings came from using Dataproc, which released the inshorts team from the task of managing clusters. “We simply moved to Dataproc, provisioned the machines, and let Google Cloud take over all the management for us,” says Manish. “This one change translated into labor savings of up to six hours a day.”

Where costs are concerned, inshorts is happy to report that Google Cloud has led to not just time savings, but monetary savings as well. The company had begun its cloud journey with a leading provider, but soon found that data analytics services were driving up costs. Meanwhile, the service required high levels of technical expertise to manage the growing infrastructure, which meant it needed to hire a DevOps expert. After switching to Google Cloud products such as Google Kubernetes Engine (GKE), Cloud Deployment Manager, and Cloud Build, inshorts has now reduced most of its DevOps burden.

“At the time, we were spending around USD100,000 per month in data analysis and data-related fields alone. We had to pursue a cost optimization drive,” recalls Manish. “We had no idea how much we would save just by migrating to Google Cloud. We brought down our costs for the entire infrastructure to around USD20,000 per month.”

The company now uses BigQuery with Looker Studio to analyze and predict its tech expenditure. With Looker Studio, it can easily visualize infrastructure costs and break them down by team, services, and project stakeholders.

Processing millions of images quickly, delivering news instantly

inshorts cites the move to GKE as one of its most important moves. “At one point, we were processing a quarter of a million images per day. Because images are uploaded by our users, traffic is unpredictable; planning the exact amount of compute needed in a given moment is almost impossible. Load balancing itself was no longer enough,” shares Manish. “Google Kubernetes Engine makes life easier for our developers, reducing the need for them to be ever present, and giving them a tool that’s easy to work with.”

The inshorts team moved to GKE primarily to gain this ease of use, where its team can now push a few configurations, then spin up clusters, tell them how to handle the load and what kind of machines to provision, and manage resources effectively. The team has reduced its dependency on DevOps since there’s no need for developers to worry about what size of machines they need for their applications. “The size of machine needed depends on the specific job, and with Google Kubernetes Engine it’s very easy to accommodate a whole range of jobs. We simply optimize our resources, scaling up or down as needed,” says Manish.

As inshorts’ platform becomes more feature-rich and sophisticated, it is taking advantage of the global network of Google Cloud. With its data center in the United States, the platform had begun to experience latency of about 600ms, too great a lag in an age of instant news. Because Google Cloud has regional data centers around the world, inshorts was able to simply move its data center closer to home, bringing down average latency to 100 ms. “We achieved significant app performance improvements thanks to being able to migrate our data center,” says Manish. “The loading was faster; everything was faster.”

Mapping the future with localized news

Being able to develop features even faster is enabling inshorts to pursue its goal of expanding into every corner of India. To capture the wave of new mobile consumers, inshorts realizes that it needs to start by finding out exactly where they are. Using Google Maps Platform, the company has launched a location-based social video app called Public, which offers news that’s highly localized to specific Indian communities and relevant to what they want to read.

As a developing nation, India’s mapping landscape is constantly changing. There were 722 administrative regions when inshorts began its work, but by 2018, 10 new districts had been added to this number. Because Public serves rural and suburban audiences, it’s crucial that it can stay on top of these rapid and sometimes confusing changes. According to Manish, Google Maps Platform not only offers granular geo-location through the Geolocation API but adapts to mapping changes in near real time. It’s a capability that inshorts is harnessing to make the Public app into its next success. “The app is experiencing tremendous growth in the tier two and three cities,” shares Manish, “In the short span of six months, it has already become the category’s number-one ranked app on the Google Play store.”

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Visualizing trends in your logs is critical when troubleshooting an issue with your application. Using the histogram in Logs Explorer, you can quickly visualize log volumes over time to help spot anomalies, detect when errors started and see a breakdown of log volumes. But static visualizations are not as helpful as having more options for customization during your investigations. 

That’s why we’re excited to announce that we recently added three new query controls along with separate colors for log severity to the histogram. These new features make it even easier to refine and analyze your logs by time range. The new histogram controls help find logs before or after the current period, jump to a specific time range represented in a histogram bar and zoom in/out of the current time window in the histogram.

Histogram colors

The histogram now makes it easier to view the breakdown of logs by severity with the introduction of color coding. For example, the severity colors make it easy to spot an increasing number of errors even when the volume of requests is relatively constant. Looking at the histogram below, the red vs blue shading makes it clear that there has been an increase in overall log volume and provides a visual breakdown of errors within that log volume.

Pan left/right to scroll through time

Sometimes in your troubleshooting journey, you may want to look at the logs directly before or after the current set of logs. Perhaps there was an unexpected spike in errors at the beginning of the time range and you need to see the logs in the time period directly preceding the current time range. Pressing the left arrow on the left side of the histogram shifts the time range earlier while the arrow on the right side of the histogram shifts the time range ahead. Either arrow will refine the time range in the query and rerun the query to return the logs in the new time range.

Zooming in or out 

Zooming in or out from a given time range may be useful to visualize fine-grained details or a broader trend Clicking the zoom in or out icons in the upper right corner of the histogram refines the time range in the query and then reruns the query, returning the logs in the newly defined time range.

Scrolling to time 

If you see a large spike in logs volume in the histogram, it’s useful to quickly review the logs generated during that spike. Clicking on the histogram bar that contains the spike now scrolls you to the logs generated during that time period.

Where to find the histogram 

The histogram is a panel in Logs Explorer that can be displayed or hidden using the controls in the Page Layout menu. When you no longer want to display the histogram, click the “X” button in the upper right corner to quickly close it. To open it again, use the same Page Layout menu to enable the histogram display.

Get started with the histogram

These improvements move the histogram from a utility for visualization to an integral part of the troubleshooting journey. We are continuously working to launch new features that make Cloud Logging the best place to troubleshoot your Google Cloud logs. If you are not already a Cloud Logging user, review this getting started documentation or watch a quick video on troubleshooting services on Google Kubernetes Engine (GKE) to learn more. If you have specific questions or feedback, please join the discussion on our Google Cloud Community, Cloud Operations page.

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}