Most large enterprises started with their own data centers and developed in-house solutions to meet their specific business needs, regulations, and industry specifications. These solutions were based on traditional applications from legacy vendors like Oracle and Microsoft — or even mainframes — and from key technology providers like SAP and VMware. During the last decade, organizations started their digital transformation journey by migrating their IT infrastructure, applications, and data to the cloud to reduce operating costs and gain efficiencies as their needs scaled.

The next step in the journey was to take advantage of cloud-based data analytics, services and scalability to drive business growth and revenue. Rather than offer generic “industry clouds” which require multi-year IT deployments and significant customization, Google Cloud has responded to customer needs by developing production-ready industry solutions that address specific use cases with repeatability across organizations.

These industry solutions are grounded in Google Cloud’s differentiated capabilities, including AI, ML, and data analytics. With these, you can dramatically reduce implementation time and realize value from the cloud more quickly with deep insights into your customers and more efficient interactions with your suppliers and partners.

Here are some highlights from the That Digital Show Podcast where Umesh Vemuri, VP of Global Strategic Customers and Industries at Google Cloud, discusses his industry strategy.

Why Google Cloud solutions?

“We are uniquely positioned to help enterprises with solutions based on our deep understanding of customer needs that are supported by our technologies. There are three examples that come to mind.
The first is our understanding of the consumer from the standpoint of running an ecommerce platform. We have the solutions to run digital platforms at a very large scale, including the engineering practices we bring to support organizations during important high traffic events like Black Friday and Cyber Monday.

The second example deals with media and entertainment as a whole. We operate the world’s largest streaming service and support the largest digital platforms currently out there. That means that whatever problems our media customers are experiencing, there’s a good chance we’ve already seen them and know how to deal with them.

Finally, there is our experience with AI and big data. Our leadership in deploying our AI and big data healthcare technologies has given us the experience to solve complex problems — such as techniques to support evidence-based selection, drug therapy, and molecular profiling.”

B to B to C

“Google is a B to B organization, I always like to add that we are also B to B to C, because the concern the consumer ultimately has is their experiences and the journey that they’re on. Connecting the dots between the business and the consumer experience is really critical and a real big differentiator in how we could better serve our customers.

For example, take Ford Motors. Really think about the challenges they have — traditional problems around manufacturing, core I. T. modernization and information, and how to remove costs. But then when you really think about the core of the business, how do you actually make this incredible experience for drivers of Ford vehicles? What do you want to do from an infotainment perspective? What do you want to do in speech-to-text conversion?

And suddenly your business is really a direct-to-consumer experience. Ultimately, all the infrastructure and technology is designed to provide the consumer — who’s ultimately buying that vehicle — with an amazing experience that will maintain their loyalty.

From this example, we can see this kind of linkage in every industry: from retail and e-commerce, media, and direct streaming to healthcare with direct telehealth.”

Solutions that transform the consumer experience

“The consumer’s expectations are constantly shifting and we have to be able to provide the technologies, the structure, and the solutions to our customers to be able to meet those changing expectations at that consumer level.

So first, we want to be very clear on the industry segments that we’re going to focus on and what we believe our clear differentiation will be. So we’ve focussed on ten industries including: retail, financial services, manufacturing, telecommunications, media and entertainment, healthcare and life sciences, education, government, supply chain and logistics and gaming.

Second, we really have to be very prescriptive about the solution pillars in the areas where our customers tell us we have challenges. We want to build solutions that solve not only today’s issues, but the problems of the future.

And third, in those pillars, we have to be very clear on what are the specific use cases that we think have high value for our customers. Then make these available as a catalog of actual products and production-ready solutions that we and our partners in the ecosystem provide.”

Google Cloud industry solutions focus on our top ten industries where we can provide differentiated value to organizations. Whether it’s discovery in retail, AI-enabled call centers, or automotive tools for connected cars, we are delivering production-ready solutions that are ready to implement with minimum customization.

Do you need to develop, run and secure applications across your hybrid and multicloud environments? Look no further than Anthos, our managed application platform that extends Google Cloud services and engineering practices to your environments so you can modernize apps faster and establish operational consistency across them. 

To help you get started, we created the Anthos 101 video learning series. It’s a great starting point for understanding the basics of Anthos—and you can watch the whole series in less than an hour.

Let’s dive in.

1. What is Anthos?

Discover what Anthos is and how it helps enterprises manage their applications. You’ll learn about the different tools Anthos offers—like the ability to create environs and platform administrators—to help you modernize and manage your application infrastructure.

https://youtube.com/watch?v=Qtwt7QcW4J8%3Fenablejsapi%3D1%26

2. How to get started with Anthos on Google Cloud

Ready to get started with Anthos? In this lesson, you’ll create your own Anthos deployment. You’ll learn about the different tools on the Anthos dashboard—like the Service Mesh card and Cluster Status cards—plus how to deploy and alter Google Kubernetes Engine (GKE) clusters and Anthos Service mesh via Google Compute Engine.

https://youtube.com/watch?v=ghFiaz7juoA%3Fenablejsapi%3D1%26

3. How to modernize and run Windows apps in Anthos

Running a Windows application that’s in need of modernization? In this lesson, you’ll discover how you can create and deploy a Windows-based application on Anthos, allowing you to modernize existing workloads and manage your application seamlessly. You’ll even learn to do this without requiring access to source code, re-writing, or re-architecting your existing application.

https://youtube.com/watch?v=w6tzIjZhTIk%3Fenablejsapi%3D1%26

4. How to build modern CI/CD with Anthos

Continuous integration? Continuous delivery? These are two things that developers need to think about with container adoption for hybrid or multicloud environments. Learn how Anthos helps you increase your development velocity without compromising the security of your application.

https://youtube.com/watch?v=ayRz5NmM6pI%3Fenablejsapi%3D1%26

5. How to adopt a multi-cluster strategy for your applications in Anthos

There are a number of use cases that might require a multi-cluster strategy, such as maintaining multiple clusters on the cloud and in your own data center. In this lesson, learn the different tools that Anthos offers—such as GKE, Anthos Config Management, and Anthos Service Mesh—to help deploy and manage multiple clusters.

https://youtube.com/watch?v=ZhF-rTXq-Us%3Fenablejsapi%3D1%26

6. How to improve observability using golden signals in Anthos

Observability is important in application development, but without the right tools monitoring your services can be time consuming. In this episode, learn more how Anthos Service Mesh can help you monitor and manage the four Golden Signals—latency, traffic, errors, and saturation—for your application.

https://youtube.com/watch?v=EDcy3KwV22o%3Fenablejsapi%3D1%26

7. How to modernize legacy Java apps with Anthos

Looking to modernize legacy Java applications? In this lesson, you’ll learn the three categories of Java applications and their unique paths for modernization via Anthos. This can help you reduce your dependency on high-cost proprietary software, decrease operational overhead, and increase software delivery speed.

https://youtube.com/watch?v=hQWcx9iyF7E%3Fenablejsapi%3D1%26

8. How to apply a zero trust model for your deployments using Anthos

It’s time to rethink traditional security models when it comes to network observability and consistency for IAM permissions. In this lesson, learn how you can adopt a zero trust posture with Anthos. This allows you to better secure your network, detect underlying network compromises, and ensure workloads are secure before deployment.

https://youtube.com/watch?v=_qG2vazlozY%3Fenablejsapi%3D1%26

9. How to go beyond business continuity with Anthos

Sometimes a business continuity plan that only covers traditional backup and disaster recovery methods simply isn’t enough. In this lesson, learn how Anthos helps resolve issues like data redundancy, scaling without code changes, implementing measurable SLOs, and much more. You’ll also discover how Anthos can help you manage your application beyond the confines of traditional backup and disaster recovery approaches.

https://youtube.com/watch?v=kUxqdjbgcXs%3Fenablejsapi%3D1%26

10. How to simplify identity with Anthos

Managing identities across hybrid and multicloud environments can be troublesome and hard to keep track of. Luckily, Anthos is capable of simplifying identity management for users and workloads. In this lesson, you’ll learn how Anthos can extend and enable existing capabilities, while allowing you to manage IAM permissions across multiple Anthos and GKE environments.

https://youtube.com/watch?v=6P-4ZEwZqZQ%3Fenablejsapi%3D1%26

11. How to optimize costs with Anthos

Learn how you can optimize costs with Anthos through greater observability, improving existing operations, and many other practices.

https://youtube.com/watch?v=8mGICSTRoYw%3Fenablejsapi%3D1%26

Keep learning

This is just a starting point for learning about Anthos. To deepen your knowledge, check out our free on-demand training: Getting started with Anthos. Or, you can download our Anthos Under the Hood ebook, or get hands-on right now with the Anthos sandbox.

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.