As we reflect on the past year, Heraclitus’ phrase “The only constant in life is change” has never rang more true. With the pandemic, companies had to shift operations, launch new products and adapt to extreme demand patterns, sometimes within a matter of weeks.

To respond to customer needs faster and more efficiently, many companies turned to serverless technology, designing applications with real-time signals and intelligence built in. From apps and sites for healthcare appointments and vaccinations, public-sector employment benefits, contact tracing, retail logistics, curbside delivery, hotel and travel booking—you name it, companies built it with serverless.

Redefining serverless

The world changed, the market changed, our lives changed and we here at Google Cloud also changed, introducing new products to meet our customers’ needs and grow with them.

Serverless technology, in particular, has changed a lot since it was first introduced. Google first launched serverless compute in 2008 with the launch of App Engine, helping customers scale their applications faster and seamlessly. We then added the ability to run Functions as a Service with Cloud Functions, giving customers a simple developer experience with integrated telemetry and observability. In parallel, we also introduced innovations to the container market with Kubernetes. Pretty soon, customers started asking us if we could combine the awesome serverless attributes of auto-scaling and developer experience with the flexibility of containers. 

Enter Cloud Run, the next generation of serverless. Serverless is now no longer just about event-driven programming or microservices. It’s also about running complex workloads at scale while still preserving a delightful developer experience. In fact, serverless with Cloud Run is about having a true developer platform with the flexibility to run any language, any library, any binary.

There are three capabilities that make Cloud Run the next-generation of serverless, and not the same ‘serverless’ you find elsewhere:

• A great developer-centric experience
• Versatility: expanding to a broader set of containerized apps
• Built-in DevOps and security

Let’s take a look at the attributes in greater depth.

A great developer experience

Being developer-centric comes from having fully-managed self-operating infrastructure and a great developer experience. We want everyone to be able to develop smart applications and for that we have to make it easy. We also want to be sure we are bringing your technical talent closer to where you generate your business value. 

To make things easy, last year we introduced buildpacks, which creates container images directly from source code. No need to learn Docker or containers. Although there are containers underneath, they’re transparent to the developer.

To simplify things further, we also introduced a single “gcloud run deploy” command to build and deploy code to Cloud Run. These types of features are some of the reasons why 98% of Cloud Run users deploy an application on their first try in less than 5 minutes. 

In fact, in the past year alone, we added over 25 new features and services to our serverless stack, making development of complex apps easier. One of our main launches was Workflows, which lets you combine Cloud Run with any Google Cloud product or any HTTP-based API service. As a developer, this is very useful when automating complex processes, or integrating GCP’s analytic services across a variety of systems. 

Taken together, all these new features make the Cloud Run developer experience far easier than its competitors’, according to a recent report by User Research International.

Versatility

Next-generation serverless is also about versatility. It supports a wider variety of applications and caters to enterprise requirements. Functions and web apps of course, but also heavyweight applications, and in the fullness of time, also brownfield and third-party containerized apps. This versatility is enabled by the container primitive, which removes restrictions on languages, run times, and hardware. 

Being able to run a greater variety of apps on our serverless stack means you can optimize for predictable usage. Today, we announced new spend-based committed use discounts for Cloud Run. Enterprises with stable, steady-state, and predictable usage can now purchase committed use contracts directly in the billing UI. There are no upfront payments, so these discounts are a perfect way to reduce your spend by as much as 17%. RELATED ARTICLEMaximize your Cloud Run investments with new committed use discountsCommitted use discounts in Cloud Run enable predictable costs—and a substantial discount!

Another way we provide versatility is with support for WebSockets and gRPC in Cloud Run. With these new additions, you get the benefits of serverless infrastructure to build responsive, high-performance applications. We also added the use of min instances with Cloud Run. This feature allows you to cut cold-start times and run latency-sensitive applications on Cloud Run! At the same time, you can still scale to zero, or keep a minimum amount of compute available, for example when running brownfield Java applications.

Built-in DevOps

Serverless doesn’t just make it faster for developers to set up their apps—it also helps once the application is up and running, taking a big management load off of operations teams. Notably, serverless systems take care of “scaling” an application up or down. That means that if your application suddenly starts fielding a lot of traffic, the serverless platform automatically spins up more resources to handle the load. No more dreaded timeouts, wheels or hourglasses—or work for your operations team. Likewise, as soon as demand goes down, the platform takes care of decommissioning resources, i.e., scaling down, so that you’re not paying for resources that you no longer need. Want to run your service globally with low latency, without an operations team, and zero stranded costs? Cloud Run takes care of global load balancing and autoscaling to zero for you in every Google Cloud region.

Further, features like support for gradual rollouts and rollbacks allow developers to experiment and test ideas quickly, as well as sophisticated traffic management in Cloud Run. Likewise, Cloud Run provides access to distributed tracing with no setup or configuration, allowing developers to find performance bottlenecks in production.

Next up: serverless security

As part of DevOps best practices, we build in security for your serverless applications at every layer: deployment time, runtime and networking. For example, built-in vulnerability scanning ensures you only deploy artifacts you trust. 

Today, we are announcing Cloud Run support for Google Secret Manager and customer-managed encryption keys (CMEK), making it easy to protect data at rest and store sensitive data. We’re also integrating Cloud Run with Binary Authorization, which lets you enforce specific policies to make sure only verified images make it to production. And finally, we added a new integration with Identity-Aware Proxy, support for VPC-SC, and egress controls that you can use to enforce a security perimeter, limiting both who can access specific services and what resources can be accessed when these services run in production. You can read more about these security enhancements here. RELATED ARTICLE4 new features to secure your Cloud Run servicesWe’re improving the security of your Cloud Run environment with things like support for Secret Manager and Binary Authorization.

In summary, the next generation of serverless combines the best of serverless with containers to run a broad spectrum of apps, with no language, networking or regional restrictions. The next generation of serverless will help developers build the modern applications of tomorrow—applications that adapt easily to change, scale as needed, respond to the needs of their customers faster and more efficiently, all while giving developers the best developer experience. Learn more by attending The Power of Serverless, a two-hour virtual event where we’ll lay out our vision for serverless compute, and where serverless subject matter experts will present on in-depth serverless development topics. Hope to see you there!

Want to learn even more about serverless and cloud-native application development? Check out the upcoming Modern App Dev & Delivery workshop, and our Ask the Experts roundtable.

Most enterprises have applications in disparate locations—in their own data centers, in multiple public clouds, and at the edge. These apps run on different proprietary technology stacks, which reduces developer velocity, wastes computing resources, and hinders scalability. How can you consistently secure and operate existing apps, while developing and deploying new apps across hybrid and multicloud environments? How can you get centralized visibility and management of the resources? Well, that is why Anthos exists!

This post explores why traditional hybrid and multicloud deployments are difficult, and then shows how Anthos makes it easy to manage applications across multiple environments. 

Why is traditional hybrid and multicloud difficult?

In hybrid and multicloud environments, you need to manage infrastructure. Let’s say you use containers on the clouds, and you develop apps using services on Google Cloud and AWS. Regardless of environment, you will need policy enforcement across your IT footprint. To manage your apps across the environment, you need monitoring and logging systems. You need to integrate that data into meaningful categories, like business data, operational data, and alerts.

Digging further, you might use operational data and alerts to inform optimizations, implement automations, and set policies or SLOs. You might use business data to do all those things, and to deploy third-party apps. Then, to actually enact the changes you decide to implement, you need to act on different parts of the system. That means digging into each tool for policy enforcement, securing services, orchestrating containers, and managing infrastructure. Don’t forget, all of this work is in addition to what it takes to develop and deploy your own apps.

Now, consider repeating this set of tasks across a hybrid and multicloud landscape. It becomes very complex, very quickly. Your platform admins, SREs,  and DevOps teams who are responsible for security and efficiency have to do manual, cluster-by-cluster management, data collection, and information synthesis. With this complexity, it’s hard to stay current, to understand business implications, and to ensure compliance (not to mention the difficulty of onboarding a new hire). Anthos helps solve these challenges!

How does Anthos make hybrid and multicloud easy?

With Anthos, you get a consistent way to manage your infrastructure, with similar infrastructure management, container management, service management, and policy enforcement across your landscape.

As a result, you have observability across all your platforms in one place, including access to business information, alerts, and operations information. With this information you might decide to optimize, automate, and set policies or SLOs. 

Digging deeper into Anthos 

Environs

You may have different regions that need different policies, and also have different development, staging, or production environments that need different permissions. Some parts of your work may need more security. That’s where environs come in! Environs are a way to create logical sets of underlying Kubernetes clusters, regardless of which platform those clusters live on. 

By considering, grouping, and managing sets of clusters as logical environs, you can think about and work with your applications at the right level of detail for what you need to do, be it acquiring business insights over the entire system, updating settings for a dev environment, or troubleshooting data for a specific cluster. Using environs, each part of the functional stack can take declarative direction about configuration, compliance, and more.

Modernize application development

Anthos also helps modernize application development because it uses environs to enforce policies and processes, and abstracts away the cluster and container management from application teams. Anthos enables you to easily abstract away infrastructure from application teams, making it easy for them to incorporate a wide variety of CI/CD solutions on top of environs.  It lets you view and manage your applications at the right level of detail, be it business insights for services across the entire system, or troubleshooting data for a specific cluster. Anthos also works with container-based tools like buildpacks to simplify the packaging process. It offers Migrate for Anthos to take those applications out of the VMs and move them to a more modern hosting environment. 

What’s in it for platform administrators? 

Anthos provides platform administrators a single place to monitor and manage their landscape, with policy control and marketplace access. This reduces person-hours needed for management, enforcement, discovery, and communication. Anthos also provides administrators an out-of-the-box structured view of their entire system, including services, clusters, and more, so they can improve security, use resources more efficiently, and demonstrate measurable success. Administrators also save time and effort by managing declaratively, and they can communicate the success, cost savings, and efficiency of the platforms without needing to manually combine data. 

Interested in getting started with Anthos? Check out the free on-demand training here and my YouTube series. 

For more resources, you can also read the Anthos ebook at no cost. For more #GCPSketchnote and similar cloud content, follow me on twitter @pvergadia and keep an eye out on thecloudgirl.dev

Do you know how much money you could save by adjusting workload requests to better represent their actual usage? If you’re not rightsizing your workloads, you might be overpaying for resources that your workloads aren’t even using or worse, putting your workloads at risk for reliability issues due to under provisioning.

As we’ve previously discussed, setting the resources is the most important thing you can do to increase the reliability of your Kubernetes workloads. In this blog we will help you with the second key finding from the State of Kubernetes Cost Optimization report!

According to our research findings, workload rightsizing is the most important golden signal. Workload rightsizing measures the capacity of developers to properly use the CPU and memory they have requested for their applications. 

Rightsizing is challenging

It can be quite difficult to predict the resource needs of your applications, which historically has not been a concern for developers in traditional data center environments.In traditional data center environments, resources were typically over-provisioned upfront to ensure capacity for peak demand and future growth, so developers didn’t need to focus on accurately predicting resource needs as they were covered by the excess capacity, whereas in cloud environments, resources are consumed on-demand. Finding a balance between efficiency and reliability can often feel like a delicate balancing act.

Tools for workload rightsizing

There are native tools in Cloud Monitoring and the GKE UI you can use to rightsize your workloads running on GKE. 

Rightsizing in the console

The Workload Cost Optimization tab helps you identify workloads that can be optimized by displaying the resources used versus what’s requested.

To take advantage of potential cost savings, you can drill into clusters to see workload level resource recommendations.

To view workload resource recommendations for Deployment objects only:

1. In the GKE Cost Optimization.
2. Select a cluster.
3. Click Workloads > Cost Optimization.
4. Select one Deployment workloads
5. In the workload’s detail page, select Actions > Scale > Edit Resource Requests

Rightsizing with Cloud Monitoring

Cloud Monitoring provides built-in VPA scale recommendations metrics that you can use to monitor the performance of your workloads and to identify opportunities to rightsize them without the need to create VPA objects.

To view these metrics:

1. Go to the Cloud Monitoring > Metric Explore console.

2. In the Metric dropdown, select the metrics:

• Memory recommendations: 
  Kubernetes Scale > autoscaler > Recommended per replica request bytes
• CPU recommendations: 
  Kubernetes Scale > autoscaler > Recommended per replica request cores

Rightsizing at scale

If you’re interested in viewing recommendations across clusters and projects, We’ve created a guide that you can use today to help you right-size your GKE workloads at scale. This solution leverages your actual cluster’s metric data and built-in workload recommendations provided by Cloud Monitoring. You can determine the resource requirements for all your workloads without having to create additional VPA autoscaler objects in each of your clusters. The guide walks you through deploying the solution.

In conclusion

In conclusion, rightsizing your workloads is essential for both cost savings and reliability. By following the tips in this blog, you can ensure that your workloads are using the right amount of resources, which will save you money and increase your workload’s reliability.

Links to the solution presented in this blog and other useful tools to help you optimize your cluster are listed below:

• The Right-sizing workloads at scale solution guide
• Setting resource requests: the key to Kubernetes cost optimization
• The simple kube-requests-checker tool
• An interactive tutorial to get set up in GKE with a set of sample workloads

Download the State of Kubernetes Optimization report, review the key findings, and stay tuned for our next blog post.