Cloud Functions, Google Cloud’s Function as a Service (FaaS) offering, is a lightweight compute platform for creating single-purpose, standalone functions that respond to events, without needing an administrator to manage a server or runtime environment. 

Over the past year we have shipped many new important capabilities on Cloud Functions: new runtimes (Java, .NET, Ruby, PHP), new regions (now up to 22), an enhanced user and developer experience, fine-grained security, and cost and scaling controls. But as we continue to expand the capabilities of Cloud Functions, the number-one friction point of FaaS is the “startup tax,” a.k.a. cold starts: if your function has been scaled down to zero, it can take a few seconds for it to initialize and start serving requests. 

Today, we’re excited to announce minimum (“min”) instances for Cloud Functions. By specifying a minimum number of instances of your application to keep online during periods of low demand, this new feature can dramatically improve performance for your serverless applications and workflows, minimizing your cold starts.

Min instances in action

Let’s take a deeper look at min instances with a popular, real-world use case: recording, transforming and serving a podcast. When you record a podcast, you need to get the audio in the right format (mp3, wav), and then make the podcast accessible so that users can easily access, download and listen to it. It’s also important to make your podcast accessible to the widest audience possible including those with trouble hearing and those who would prefer to read the transcript of the podcast. 

In this post, we show a demo application that takes a recorded podcast, transcribes the audio, stores the text in Cloud Storage, and then emails an end user with a link to the transcribed file, both with and without min instances. 

Approach 1: Building the application with Cloud Functions and Cloud Workflows

In this approach, we use Cloud Functions and Google Cloud Workflows to chain together three individual cloud functions. The first function (transcribe) transcribes the podcast, the second function (store-transcription) consumes the result of the first function in the workflow and stores it in Cloud Storage, and the third function (send-email) is triggered by Cloud Storage when the transcribed result is stored and sends an email to the user to inform them that the workflow is complete.

Cloud Workflows executes the functions in the right order and can be extended to add additional steps in the workflow in the future. While the architecture in this approach is simple, extensible and easy to understand, the cold start problem remains, impacting end-to-end latency. 

Approach 2: Building the application with Cloud Functions, Cloud Workflows and min instances

In this approach, we follow all the same steps as in Approach 1, with a slightly modified configuration that enables a set of min instances for each of the functions in the given workflow.

This approach presents the best of both worlds. It has the simplicity and elegance of wiring up the application architecture using Cloud Workflows and Cloud Functions. Further, each of the functions in this architecture leverages a set of min instances to mitigate the cold-start problem and time to transcribe the podcast.

Comparison of cold start performance

Now consider executing the Podcast transcription workflow using Approach 1, where no min instances are set on the functions that make up the app. Here is an instance of this run with a snapshot of the log entries. The start and end timestamps are highlighted to show the execution of the run. You can see here that the total runtime in Approach 1 took 17 s. 

Approach 1: Execution Time (without Min Instances)

Now consider executing the podcast transformation workflow using Approach 2, where min instances are set on the functions. Here is an instance of this run with a snapshot of the log entries. The start and end timestamps are highlighted to show the execution of the run, for a total of 6 s. 

Approach 2: Execution Time (with Min Instances)

That’s an 11 second difference between the two approaches. The example set of functions are hardcoded with a 2 to 3 second sleep during function initialization, and when combined with average platform cold-start times, you can clearly see the cost of not using min instances.

You can reproduce the above experiment in your own environment using the tutorial here. 

Check out min instances on Cloud Functions

We are super excited to ship min instances on Cloud Functions, which will allow you to run more latency-sensitive applications such as podcast transcription workflows in the serverless model. You can also learn more about Cloud Functions and Cloud Workflows in the following Quickstarts: Cloud Functions, Cloud Workflows.

Financing digital transformation poses a significant problem for many enterprises we work with. It’s not because the C-suite doesn’t support it: the need to become more agile, digital, and data-driven is nearly ubiquitous in executive public pronouncements. But most large organizations look at IT investment in a way that’s ineffective for a successful digital transformation effort.  

A varied combination of  factors affect an organization’s success in the digital ecosystem, and a lack of alignment between IT and business leaders can hamper even the most digitally sophisticated organizations. Empirical research by the Apigee Institute shows clear evidence that a solid digital transformation effort, aligned across business and marketing, whose progress is carefully tied to core enterprise metrics, must be supported by an investment strategy that uses a “real options” portfolio approach.

Let’s explore ways to think about funding digital transformation.

Planning for the Unimaginable

Common IT investment strategies are focused on straight-line net present value (NPV) business case calculations, which emphasize linear solutions and impacts. These methods ignore the value of enabling a range of options and potential future use cases, which an API program, with its reusable, highly consumable digital assets, makes available for the enterprise. In addition, these methods often ignore the value of the new categories of business data generated by such programs, as well as business capabilities that were impossible to imagine before the capabilities came online.

One media company we’re working with was astounded by a project that now makes key sales provisioning available to large distribution partners within two months, when previous estimates predicted two years and many millions of dollars, each.

More importantly, that functionality will now be reused across all of their numerous current partners and sales channels, and is enabling exciting new partnerships that were too costly before. This functionality was an unexpected by-product of their original API effort—it wasn’t even the main focus of the initial project.  

There was no way that the impact of this capability or its implications on new business could have been predicted at the outset, and no way to formally model the outsized impact this functionality will have on the enterprise. Any attempts to do so would have been deemed ludicrous.

Breaking from the Status Quo

So how do you fund an API-driven digital transformation, which by nature emphasizes speed and optionality to unlock new ways of doing business, instead of the business-as-usual single-use, point-to-point integrations that perpetuate status quo thinking? For a world where the latter is the dominant paradigm and you don’t have a clear C-level mandate, here are three suggestions.

1.  Enable your web/e-commerce teams. Many such teams find their responsibilities expanded to include mobile, kiosk, and many other digital initiatives, while being trapped in the traditional web server, do-it-yourself paradigm. These teams are actually quite open to technology, and often harbour weekend hackers who love to explore the new capabilities that are expanding the digital ecosystem at light speed. Tapping this passion by funding new approaches and quickly realizing immediate projects can create first the technical, and then the business proof points that resonate with a strategy team. These capabilities must be nurtured to move out of the lab into production, where real results are created.  

2. Focus on partnerships. Opening and accelerating  your relationships with partners via APIs (read more about partner API initiatives here) is high-impact and relatively easy to model. This business area can prove out an API platform, and in our experience creates clear business value that can support future justifications for other business units. What’s best about these wins is that sales operations groups are typically very tactical and demanding, so when they see the value, these groups will send a message to the rest of the organization that APIs have business value. Further, your enterprise’s core partnerships have the focus of executive attention. Adding value here will elevate the stature of the API program in the C-suite.

3. Work on your CFO/financial organization. Using your successes in e-commerce and partnerships, you can propose a more robust investment using a real options approach that leads to treatment of APIs as products. This path is predicated on what research has proven to work: the adoption of a digital KPI target, coupled with a portfolio of programs whose net result is to achieve the target KPI.  

Building Toward the Big Vision

But what about the visions of ground-breaking business models, ubiquitous connectivity, and enablement of an ever-shifting set of opportunities on big platforms and with unlimited partnership possibilities? Yes, these are still the endpoints to work toward, and fundamentally comprise the strategic reasons to embark on a digital transformation. If your enterprise has the C-Level leadership (in the form of a CDO or CDO-like figure), supported by a well-funded, sophisticated investment approach, by all means, build that new world now.

However, should that vision be shared only within your business unit, a forward-thinking IT group, or a dedicated innovation team, the most important thing to do is to build your team’s capability—and your program’s credibility—while delivering value along the way. The approach we suggest here has been proven in many of the organizations we work with; once your organization begins working faster and at lower costs by orders of magnitude, you’ll have created enough leverage to change the way your organization invests in its API program.

Kubernetes is an increasingly key part of the application deployment strategies at large organizations, and one of the most recommended options for the teams we work with. An idea brought to life at Google, organizations throughout the world use containers and Kubernetes on-premises, in Google Cloud, or in a multi-cloud scenario, and it has emerged as a leading application deployment platform. And because it’s open source, anyone can pop the hood, so to speak, to see how each component of it works, creating a trusted, verifiable framework that users can rely on.

Customers begin their Kubernetes and containerization journeys using various offerings in our product portfolio. Some, like Colgate, started their modernization journey with Kubernetes. Colgate is an $18B global consumer products company with ~34,000 diverse and dedicated people serving over 200 countries and territories. Through science-led innovation, they drive growth and reimagine a healthier future for all people, their pets, and our planet. 

During ideation, they talked through various considerations: What technology strengths does their organization have? What skills do their teams need? What will this initiative look like a decade from now? They implemented the following Kubernetes-focused architecture:

Supporting a variety of use cases and teams

Google Cloud also helped Colgate break new ground over the years, especially in the areas of cloud-native networking, security, monitoring, pub/sub, managed containerization, and multi-tenant environments.

Over time Colgate began to leverage Google’s managed container portfolio, which includes Cloud Run. Cloud Run lets you run containers on top of a serverless platform, unlocking workload possibilities for public websites, private services, APIs and batch jobs and eliminating a lot of the time spent on infrastructure management. Cloud Run also requires no prior knowledge of Kubernetes or containers.

Simplified enterprise-level management

Many teams have found that they prefer the serverless, hands-off approach that Cloud Run provides, and Colgate now evaluates Google Cloud’s serverless solutions alongside GKE for any applications destined for the cloud. At the same time, Cloud Run lets them continue to leverage their investment in workloads based on the Open Container Initiative. 

For example, Cloud Run is designed for Kubernetes compatibility with consistent management capabilities such as the ability to manage resources using kubectl via the config controller, and the ability to browse logs and metrics from both platforms in Cloud Logging and Monitoring. Cloud Run and GKE data planes are also interoperable, allowing Cloud Run and GKE services to be exposed behind a VPC behind private IPs using an internal load balancer. Cloud Run as an option has contributed to faster innovation, allowing Colgate to bring smiles to many more faces globally.

Google’s managed container offerings provide a composable and comprehensive set of solutions for customers’ applications. At Colgate, container-based managed services are used across the stack: on the front-end, where they use Identity-Aware Proxy to manage authentication and External Load Balancers to handle incoming traffic with high availability and low latency; at the application layer, where they can choose from Cloud Functions, Cloud Run, or GKE, depending on the level of control they need over the application; and at the internal load balancing level where NGINX® controllers serve internal applications. Together, these managed services ensure that Colgate has the flexibility to choose the right toolchain and maximize their goals for each use case. 

Colgate wanted to build internal applications on Cloud Run in a way that complied with their organization’s policies while maximizing developer productivity. They were able to use new features like the Cloud Run Identity Aware Proxy GA to build a secure, serverless deployment for their applications.

A great partnership with Google Cloud

Colgate and Google Cloud have enjoyed a deep partnership for many years, engaging across many technologies, teams, and design patterns.

They’ve engaged with Product and Engineering across compute, networking, Kubernetes, and serverless as they brought this new way of thinking to their users.

For Colgate and many of our customers looking to address the needs of the modern user, Google’s managed container offerings are a breath of fresh air. Its reliability, scalability, and control offer the  flexibility to build applications that meet the demands of both internal and external consumers. In addition, the variety of container offerings available on Google Cloud — GKE and Cloud Run — allow customers to make app deployment decisions based on the amount of Day 2 operations the users are willing to take on. Platform administrators appreciate the reduced management effort, users enjoy reduced downtime, and developers can simply deploy.

^{Google Cloud contributors: Rex Orioko, Rachel Tsao
Colgate-Palmolive contributors: Matthew Tattoli, Nicholas Farley, and David Wiser}