Tag Archives: Compute

Whitepaper: Embark on a journey from monoliths to microservices



Today we introduced the next in a series of white papers about migration entitled “Taking the Cloud-Native Approach with Microservices.” This paper switches gears from “lift-and-shift,” and introduces the idea of “move-and-improve.” If you missed the first white paper, you can read the blog and download a copy.

The white paper provides context on monolithic software application architecture, as well as microservices architecture. You’ll also learn about the shortcomings of monoliths: They can be challenging to scale properly, and their faults are harder to isolate. Deploying monoliths can also be cumbersome and time consuming, and they generally require a long-term commitment to a particular technology stack. Alternatively, microservices are thought to be more agile, fault-resilient and scalable, because the application is modularized into a system of small services with well-defined, narrowly scoped functions and APIs.

PetShop is an eCommerce website reference implementation that is well known within both the Java and Microsoft .NET development communities, and the white paper uses it to step through the process of deconstructing a monolith into microservices. Specifically, the paper considers three different layers that may or may not be deployed in different physical tiers: the presentation, business logic and data access layers.

In addition, you’ll be introduced to the concept of domain-driven design (DDD), which advocates modeling based on a business’s practical use cases. In its simplest form, DDD consists of decomposing a business domain into smaller functional chunks, at either the business function or business process level, so that the complexity of both a business and problem domain can be better understood and resolved through your application.

Download your copy of the white paper, and GitHub repositories; then, take a look at how you can deconstruct the PetShop reference implementation and build a microservice-based version. You’ll be well on your way to deconstructing and rebuilding your own monoliths!

Running dedicated game servers in Kubernetes Engine: tutorial



Packaging server applications as container images is quickly gaining traction across tech organizations, game companies among them. They want to use containers to improve VM utilization, as well as take advantage of the isolated run-time paradigm. Despite their interest, many game companies don't know where to start.

Using the orchestration framework Kubernetes to deploy production-scale fleets of dedicated game servers in containers is an excellent choice. We recommend Google Kubernetes Engine as the easiest way to start a Kubernetes cluster for game servers on Google Cloud Platform (GCP) without manual setup steps. Kubernetes will help simplify your configuration management and select a VM with adequate resources to spin up a match for your players for you automatically.

We recently put together a tutorial that shows you how to integrate dedicated game servers with Kubernetes Engine, and how to automatically scale the number of VMs up and down according to player demand. It also offers some key storage strategies, including how to manage your game server assets without having to manually distribute them with each container image. Check it out, and let us know what other Google Cloud tools you’d like to learn how to use in your game operations. You can reach me on Twitter at @gcpjoe.

Get latest Kubernetes version 1.9 on Google’s managed offering



We're excited to announce that Kubernetes version 1.9 will be available on Google Kubernetes Engine next week in our early access program. This release includes greater support for stateful and stateless applications, hardware accelerator support for machine learning workloads and storage enhancements. Overall, this release achieves a big milestone in making it easy to run a wide variety of production-ready applications on Kubernetes without having to worry about the underlying infrastructure. Google is the leading contributor to open-source Kubernetes releases and now you can access the latest Kubernetes release on our fully-managed Kubernetes Engine, and let us take care of managing, scaling, upgrading, backing up and helping to secure your clusters. Further, we recently simplified our pricing by removing the fee for cluster management, resulting in real dollar savings for your environment.

We're committed to providing the latest technological innovation to Kubernetes users with one new release every quarter. Let’s a take a closer look at the key enhancements in Kubernetes 1.9.

Workloads APIs move to GA


The core Workloads APIs (DaemonSet, Deployment, ReplicaSet and StatefulSet), which let you run stateful and stateless workloads in Kubernetes 1.9, move to general availability (GA) in this release, delivering production-grade quality, support and long-term backwards compatibility.

Hardware accelerator enhancements


Google Cloud Platform (GCP) provides a great environment for running machine learning and data analytics workloads in containers. With this release, we’ve improved support for hardware accelerators such as NVIDIA Tesla P100 and K80 GPUs. Compute-intensive workloads will benefit greatly from cost-effective and high performance GPUs for many use cases ranging from genomics and computational finance to recommendation systems and simulations.

Local storage enhancements for stateful applications


Improvements to the Kubernetes scheduler in this release make it easier to use local storage in Kubernetes. The local persistent storage feature (alpha) enables easy access to local SSD on GCP through Kubernetes’ standard PVC (Persistent Volume Claim) interface in a simple and portable way. This allows you to take an existing Helm Chart, or StatefulSet spec using remote PVCs, and easily switch to local storage by just changing the StorageClass name. Local SSD offers superior performance including high input/output operations per second (IOPS), low latency, and is ideal for high performance workloads, distributed databases, distributed file systems and other stateful workloads.

Storage interoperability through CSI


This Kubernetes release introduces an alpha implementation of Container Storage Interface (CSI). We've been working with the Kubernetes community to provide a single and consistent interface for different storage providers. CSI makes it easy to add different storage volume plugins in Kubernetes without requiring changes to the core codebase. CSI underscores our commitment to being open, flexible and collaborative while providing maximum value—and options—to our users.

Try it now!


In a few days, you can access the latest Kubernetes Engine release in your alpha clusters by joining our early access program.

Introducing Preemptible GPUs: 50% Off



In May 2015, Google Cloud introduced Preemptible VM instances to dramatically change how you think about (and pay for) computational resources for high-throughput batch computing, machine learning, scientific and technical workloads. Then last year, we introduced lower pricing for Local SSDs attached to Preemptible VMs, expanding preemptible cloud resources to high performance storage. Now we're taking it even further by announcing the beta release of GPUs attached to Preemptible VMs.

You can now attach NVIDIA K80 and NVIDIA P100 GPUs to Preemptible VMs for $0.22 and $0.73 per GPU hour, respectively. This is 50% cheaper than GPUs attached to on-demand instances, which we also recently lowered. Preemptible GPUs will be a particularly good fit for large-scale machine learning and other computational batch workloads as customers can harness the power of GPUs to run distributed batch workloads at predictably affordable prices.

As a bonus, we're also glad to announce that our GPUs are now available in our us-central1 region. See our GPU documentation for a full list of available locations.

Resources attached to Preemptible VMs are the same as equivalent on-demand resources with two key differences: Compute Engine may shut them down after providing you a 30-second warning, and you can use them for a maximum of 24 hours. This makes them a great choice for distributed, fault-tolerant workloads that don’t continuously require any single instance, and allows us to offer them at a substantial discount. But just like its on-demand equivalents, preemptible pricing is fixed. You’ll always get low cost, financial predictability and we bill on a per-second basis. Any GPUs attached to a Preemptible VM instance will be considered Preemptible and will be billed at the lower rate. To get started, simply append --preemptible to your instance create command in gcloud, specify scheduling.preemptible to true in the REST API or set Preemptibility to "On" in the Google Cloud Platform Console and then attach a GPU as usual. You can use your regular GPU quota to launch Preemptible GPUs or, alternatively, you can request a special Preemptible GPUs quota that only applies to GPUs attached to Preemptible VMs.
For users looking to create dynamic pools of affordable GPU power, Compute Engine’s managed instance groups can be used to automatically re-create your preemptible instances when they're preempted (if capacity is available). Preemptible VMs are also integrated into cloud products built on top of Compute Engine, such as Kubernetes Engine (GKE’s GPU support is currently in preview. The sign-up form can be found here).

Over the years we’ve seen customers do some very exciting things with preemptible resources: everything from solving problems in satellite image analysis, financial services, questions in quantum physics, computational mathematics and drug screening.
"Preemptible GPU instances from GCP give us the best combination of affordable pricing, easy access and sufficient scalability. In our drug discovery programs, cheaper computing means we can look at more molecules, thereby increasing our chances of finding promising drug candidates. Preemptible GPU instances have advantages over the other discounted cloud offerings we have explored, such as consistent pricing and transparent terms. This greatly improves our ability to plan large simulations, control costs and ensure we get the throughput needed to make decisions that impact our projects in a timely fashion." 
Woody Sherman, CSO, Silicon Therapeutics 

We’re excited to see what you build with GPUs attached to Preemptible VMs. If you want to share stories and demos of the cool things you've built with Preemptible VMs, reach out on Twitter, Facebook or G+.

For more details on Preemptible GPU resources, please check out the preemptible documentation, GPU documentation and best practices. For more pricing information, take a look at our Compute Engine pricing page or try out our pricing calculator. If you have questions or feedback, please visit our Getting Help page.

To get started using Preemptible GPUs today; sign up for Google Cloud Platform and get $300 in credits to try out Preemptible GPUs.

What a year! Google Cloud Platform in 2017



The end of the year is a time for reflection . . . and making lists. As 2017 comes to a close, we thought we’d review some of the most memorable Google Cloud Platform (GCP) product announcements, white papers and how-tos, as judged by popularity with our readership.

As we pulled the data for this post, some definite themes emerged about your interests when it comes to GCP:
  1. You love to hear about advanced infrastructure: CPUs, GPUs, TPUs, better network plumbing and more regions. 
  2.  How we harden our infrastructure is endlessly interesting to you, as are tips about how to use our security services. 
  3.  Open source is always a crowd-pleaser, particularly if it presents a cloud-native solution to an age-old problem. 
  4.  You’re inspired by Google innovation — unique technologies that we developed to address internal, Google-scale problems. So, without further ado, we present to you the most-read stories of 2017.

Cutting-edge infrastructure

If you subscribe to the “bigger is always better” theory of cloud infrastructure, then you were a happy camper this year. Early in 2017, we announced that GCP would be the first cloud provider to offer Intel Skylake architecture, GPUs for Compute Engine and Cloud Machine Learning became generally available and Shazam talked about why cloud GPUs made sense for them. In the spring, you devoured a piece on the performance of TPUs, and another about the then-largest cloud-based compute cluster. We announced yet more new GPU models and topping it all off, Compute Engine began offering machine types with a whopping 96 vCPUs and 624GB of memory.

It wasn’t just our chip offerings that grabbed your attention — you were pretty jazzed about Google Cloud network infrastructure too. You read deep dives about Espresso, our peering-edge architecture, TCP BBR congestion control and improved Compute Engine latency with Andromeda 2.1. You also dug stories about new networking features: Dedicated Interconnect, Network Service Tiers and GCP’s unique take on sneakernet: Transfer Appliance.

What’s the use of great infrastructure without somewhere to put it? 2017 was also a year of major geographic expansion. We started out the year with six regions, and ended it with 13, adding Northern Virginia, Singapore, Sydney, London, Germany, Sao Paolo and Mumbai. This was also the year that we shed our Earthly shackles, and expanded to Mars ;)

Security above all


Google has historically gone to great lengths to secure our infrastructure, and this was the year we discussed some of those advanced techniques in our popular Security in plaintext series. Among them: 7 ways we harden our KVM hypervisor, Fuzzing PCI Express and Titan in depth.

You also grooved on new GCP security services: Cloud Key Management and managed SSL certificates for App Engine applications. Finally, you took heart in a white paper on how to implement BeyondCorp as a more secure alternative to VPN, and support for the European GDPR data protection laws across GCP.

Open, hybrid development


When you think about GCP and open source, Kubernetes springs to mind. We open-sourced the container management platform back in 2014, but this year we showed that GCP is an optimal place to run it. It’s consistently among the first cloud services to run the latest version (most recently, Kubernetes 1.8) and comes with advanced management features out of the box. And as of this fall, it’s certified as a conformant Kubernetes distribution, complete with a new name: Google Kubernetes Engine.

Part of Kubernetes’ draw is as a platform-agnostic stepping stone to the cloud. Accordingly, many of you flocked to stories about Kubernetes and containers in hybrid scenarios. Think Pivotal Container Service and Kubernetes’ role in our new partnership with Cisco. The developers among you were smitten with Cloud Container Builder, a stand-alone tool for building container images, regardless of where you deploy them.

But our open source efforts aren’t limited to Kubernetes — we also made significant contributions to Spinnaker 1.0, and helped launch the Istio and Grafeas projects. You ate up our "Partnering on open source" series, featuring the likes of HashiCorp, Chef, Ansible and Puppet. Availability-minded developers loved our Customer Reliability Engineering (CRE) team’s missive on release canaries, and with API design: Choosing between names and identifiers in URLs, our Apigee team showed them a nifty way to have their proverbial cake and eat it too.

Google innovation


In distributed database circles, Google’s Spanner is legendary, so many of you were delighted when we announced Cloud Spanner and a discussion of how it defies the CAP Theorem. Having a scalable database that offers strong consistency and great performance seemed to really change your conception of what’s possible — as did Cloud IoT Core, our platform for connecting and managing “things” at scale. CREs, meanwhile, showed you the Google way to handle an incident.

2017 was also the year machine learning became accessible. For those of you with large datasets, we showed you how to use Cloud Dataprep, Dataflow, and BigQuery to clean up and organize unstructured data. It turns out you don’t need a PhD to learn to use TensorFlow, and for visual learners, we explained how to visualize a variety of neural net architectures with TensorFlow Playground. One Google Developer Advocate even taught his middle-school son TensorFlow and basic linear algebra, as applied to a game of rock-paper-scissors.

Natural language processing also became a mainstay of machine learning-based applications; here, we highlighted with a lighthearted and relatable example. We launched the Video Intelligence API and showed how Cloud Machine Learning Engine simplifies the process of training a custom object detector. And the makers among you really went for a post that shows you how to add machine learning to your IoT projects with Google AIY Voice Kit. Talk about accessible!

Lastly, we want to thank all our customers, partners and readers for your continued loyalty and support this year, and wish you a peaceful, joyful, holiday season. And be sure to rest up and visit us again Next year. Because if you thought we had a lot to say in 2017, well, hold onto your hats.

One year of Cloud Performance Atlas



In March of this year, we kicked off a new content initiative called Cloud Performance Atlas, where we highlight best practices for GCP performance, and how to solve the most common performance issues that cloud developers come across.

Here’s the top topics from 2017 that developers found most useful.


5. The bandwidth delay problem


Every now and again, I’ll get a question from a company who recently updated their connection bandwidth from their on-premises systems to Google Cloud, and for some reason, aren’t getting any better performance as a result. The issue, as we’ve seen multiple times, usually resides in an area of TCP called “the bandwidth delay problem.”

The TCP algorithm works by transferring data in packets between two connections. A packet is sent to a connection, and then an acknowledgement packet is returned. To get maximum performance in this process, the connection between the two endpoints has to be optimized so that neither the sender or receiver is waiting around for acknowledgements from prior packets.

The most common way to address this problem is to adjust the window sizes for the packets to match the bandwidth of the connection. This allows both sides to continue sending data until an ACK arrives back from the client for an earlier packet, thereby creating no gaps and achieving maximum throughput. As such, a low window size will limit your connection throughput, regardless of the available or advertised bandwidth between instances.

Find out more by checking out the video, or article!

4. Improving CDN performance with custom keys


Google Cloud boasts an extremely powerful CDN that can leverage points-of-presence around the globe to get your data to users as fast as possible.

When setting up Cloud CDN for your site, one of the most important things is to ensure that you’re using the right Custom Cache Keys to configure what assets get cached, and which ones don’t. In most cases, this isn’t an issue, but if you’re leveraging a large site with content re-used across protocols (i.e., http and https) you can run into a problem where your cache fill costs can increase more than expected.

You can see how we helped a sports website get their CDN keys just right in the video, and article.


3. Google Cloud Storage and the sequential filename challenge


Google Cloud Storage is a one-stop-shop for all your content serving needs. However, one developer continued to run into a problem of slow upload speeds when pushing their content into the cloud.

The issue was that Cloud Storage uses the file path and name of the files being uploaded to segment and shard the connection to multiple frontends (improving performance). As we found out, if those file names are sequential then you could end up in a situation where multiple connections get squashed down to a single upload thread (thus hurting performance)!

As shown in the video and article, we were able to help a nursery camera company get past this issue with a few small fixes.

2. Improving Compute Engine boot time with custom images


Any cloud-based service needs to grow and shrink its resource allocations to respond to traffic load. Most of the time, this is a good thing, especially during the holiday season. ;) As traffic increases to your service/application, your backends will need to spin up more Compute Engine VMs to provide a consistent experience to your users.

However, if it takes too long for your VMs to start up, then the quality and performance for you users can be negatively impacted, especially if your VM needs to do a lot of things during its startup script, like compile code, or install large packages.

As we showed in the video, (article) you can pre-compute a lot of that work into a custom image of boot disks. When your VMs are loaded, they simply need to copy in the custom image to the disk (with everything already installed), rather than doing everything from scratch.

If you’re looking to improve your GCE boot performance, custom images are worth checking out!

1. App Engine boot time


Modern managed languages (Java, Python, Javascript, etc.) typically have a run-time dependencies step that occurs at the init phase of the program when code is imported and instantiated.

Before execution can begin, any global data, functions or state information are also set up. Most of the time, these systems are global in scope, since they need to be used by so many subsystems (for example, a logging system).

In the case of App Engine, this global initialization work can end up delaying start-time, since it must complete before a request can be serviced. And as we showed in the video and article, as your application responds to spikes in workload, this type of global variable contention can put a hurt on your request response times.


See you soon!


For the rest of 2017 our Cloud Performance team is enjoying a few hot cups of tea, relaxing with the holidays and counting down the days until the new year. In 2018, we’ve got a lot of awesome new topics to cover, including increased networking performance, Cloud Functions and Cloud Spanner!

Until then, make sure you check out the Cloud Performance Atlas videos on Youtube or our article series on Medium.

Thanks again for a great year everyone, and remember, every millisecond counts!

A developer’s toolkit for building great applications on GCP



Whether you're looking to build your next big application, learn some really cool technology concepts or gain some hands-on development experience, Google Cloud Platform (GCP) is a great development platform. But where do you start?

Lots of customers talk to us about their varying application development needs  for example, what are the best tools to use for web and mobile app development, how do I scale my application back-end and how do I add data processing and intelligence to my application? In this blog, we’ll share some resources to help you identify which products are best suited to your development goals.

To help you get started, here are a few resources such as quick start guides, videos and codelabs for services across web and mobile app development, developer tools, data processing and analytics and monitoring:

Web and mobile app development:


Google App Engine offers a fully managed serverless platform that allows you to build highly scalable web and mobile applications. If you're looking for a zero-config application hosting service that will let you auto-scale from zero to infinite-size without having to manage any infrastructure, look no further than App Engine
Cloud Functions is another great event-driven serverless compute platform you can use to build microservices at the functions level, scale to infinite size and pay only for what you use. If you're looking for a lightweight compute platform to run your code in response to user actions, analytics, authentication events or for telemetry data collection, real-time processing and analysis, Cloud Functions has everything you need to be agile and productive.

Build, deploy and debug:

Developer tools provide plugins to build, deploy and debug code using your favorite IDEs, such as IntelliJ, Eclipse, Gradle and Maven. You can use either cloud SDK or a browser-based command line to build your apps. Cloud Source Repositories that come as a part of developer tools let you host private Git repos and organize the associated code. Below are a sampling of resources, check out the developer tools section for more.

Data Processing and Analytics:

BigQuery offers a fast, highly scalable, low cost and fully managed data warehouse on which you can perform analytics. Cloud Pub/Sub allows you to ingest event streams from anywhere, at any scale, for simple, reliable, real-time stream analytics. BigQuery and Pub/Sub work seamlessly with services like Cloud Machine Learning Engine to help you add an intelligence layer to your application.

Monitoring, logging and diagnostics:

Google Stackdriver provides powerful monitoring, logging and diagnostics. It equips you with insight into the health, performance and availability of cloud-powered applications, enabling you to find and fix issues faster. It's natively integrated with GCP, other cloud providers and popular open source packages.
I hope this gives you enough content to keep you engaged and provide great learning experiences of the different application development services on GCP. Looking for tips and tricks as you're building your applications? Check out this link for details. Sign up for your free trial today and get a $300 GCP credit!

Getting started with Google Compute Engine: a guide to all the guides



Happy holidays from all of us on the Google Cloud team. As we move into the final days of 2017, many people (myself included) reflect on the year, enjoy some downtime, and think about their goals and ambitions for the new year. Does this sound like you?

When the pace of family gatherings and social obligations begins to slow, I intend to use my downtime to learn about a few technologies. What will you do with your downtime? Have you ever wondered about Infrastructure-as-a-Service (IaaS) and what you could do with cloud computing? Ever wondered about Google Compute Engine?

Compute Engine is a type of IaaS. It takes most of the work out of procuring and setting up a VM. Compute Engine provides practically unlimited computing power using VMs in the cloud.

Below you'll find a collection of resources designed to help fast-track your learning journey with Compute Engine. Use them to quickly get up to speed on concepts, launch a virtual machine (VM) instance and use how-to guides to configure and manage your VM. You can also run through tutorials aimed at more interesting and sophisticated use cases, such as running PostgreSQL, a LAMP stack or even a Minecraft server.

These Compute Engine learning resources are neatly organized to help you quickly find what’s most interesting and relevant to you. If you’re a cloud novice, you’ll benefit from reading through the concepts section, and using the Quick Start Guides to launch a VM. If you’re already comfortable with Compute Engine, you may wish to skip to tutorials, or even the APIs and References section.

The Concepts page introduces topics such as VMs, storage, networking, access control, regions and more. This is a great place to start if you want to develop a foundational understanding of the basics. Learn the fundamentals here: https://cloud.google.com/compute/docs/concepts

Quick Start Guides provide you with step-by-step instructions for creating and accessing either a Linux or Windows virtual machine instances. Launch one now: https://cloud.google.com/compute/docs/quickstarts

How-to Guides dive into VM instance-configuration, access and management topics. These guides do a great job of answering the question of what to do after you launch a VM. Try adding storage to your VM, or fine-tuning your firewall rules here: https://cloud.google.com/compute/docs/how-to

Tutorials explore more sophisticated use cases, such as installing and running application and web services on your VMs. If you are interested in machine learning, check out the tutorial on running distributed TensorFlow. https://cloud.google.com/compute/docs/tutorials

The APIs and References section provides you with developer resources for programmatically interacting with GCP and Compute Engine. This page is organized by resource type. Each resource type has one or more data representations and one or more methods. Try adding a persistent disk to an existing project. https://cloud.google.com/compute/docs/apis

The Additional Resources section provides information about the service, and acts as a kind of catch-all. If you aren’t finding the information you're looking for in the other sections, be sure to check out this section. It contains information on pricing, quotas, release notes, third-party software and much more. https://cloud.google.com/compute/docs/resources

Hopefully you found enough content to satisfy your curiosity about Compute Engine, and maybe you even learned something new. We’ll be publishing additional resources in the new year, so you can learn and do even more. If you’re ready to get started, sign up for your free trial today and get a $300 GCP credit!

What a year! Google Cloud Platform in 2017



The end of the year is a time for reflection . . . and making lists. As 2017 comes to a close, we thought we’d review some of the most memorable Google Cloud Platform (GCP) product announcements, white papers and how-tos, as judged by popularity with our readership.

As we pulled the data for this post, some definite themes emerged about your interests when it comes to GCP:
  1. You love to hear about advanced infrastructure: CPUs, GPUs, TPUs, better network plumbing and more regions. 
  2.  How we harden our infrastructure is endlessly interesting to you, as are tips about how to use our security services. 
  3.  Open source is always a crowd-pleaser, particularly if it presents a cloud-native solution to an age-old problem. 
  4.  You’re inspired by Google innovation — unique technologies that we developed to address internal, Google-scale problems. So, without further ado, we present to you the most-read stories of 2017.

Cutting-edge infrastructure

If you subscribe to the “bigger is always better” theory of cloud infrastructure, then you were a happy camper this year. Early in 2017, we announced that GCP would be the first cloud provider to offer Intel Skylake architecture, GPUs for Compute Engine and Cloud Machine Learning became generally available and Shazam talked about why cloud GPUs made sense for them. In the spring, you devoured a piece on the performance of TPUs, and another about the then-largest cloud-based compute cluster. We announced yet more new GPU models and topping it all off, Compute Engine began offering machine types with a whopping 96 vCPUs and 624GB of memory.

It wasn’t just our chip offerings that grabbed your attention — you were pretty jazzed about Google Cloud network infrastructure too. You read deep dives about Espresso, our peering-edge architecture, TCP BBR congestion control and improved Compute Engine latency with Andromeda 2.1. You also dug stories about new networking features: Dedicated Interconnect, Network Service Tiers and GCP’s unique take on sneakernet: Transfer Appliance.

What’s the use of great infrastructure without somewhere to put it? 2017 was also a year of major geographic expansion. We started out the year with six regions, and ended it with 13, adding Northern Virginia, Singapore, Sydney, London, Germany, Sao Paolo and Mumbai. This was also the year that we shed our Earthly shackles, and expanded to Mars ;)

Security above all


Google has historically gone to great lengths to secure our infrastructure, and this was the year we discussed some of those advanced techniques in our popular Security in plaintext series. Among them: 7 ways we harden our KVM hypervisor, Fuzzing PCI Express and Titan in depth.

You also grooved on new GCP security services: Cloud Key Management and managed SSL certificates for App Engine applications. Finally, you took heart in a white paper on how to implement BeyondCorp as a more secure alternative to VPN, and support for the European GDPR data protection laws across GCP.

Open, hybrid development


When you think about GCP and open source, Kubernetes springs to mind. We open-sourced the container management platform back in 2014, but this year we showed that GCP is an optimal place to run it. It’s consistently among the first cloud services to run the latest version (most recently, Kubernetes 1.8) and comes with advanced management features out of the box. And as of this fall, it’s certified as a conformant Kubernetes distribution, complete with a new name: Google Kubernetes Engine.

Part of Kubernetes’ draw is as a platform-agnostic stepping stone to the cloud. Accordingly, many of you flocked to stories about Kubernetes and containers in hybrid scenarios. Think Pivotal Container Service and Kubernetes’ role in our new partnership with Cisco. The developers among you were smitten with Cloud Container Builder, a stand-alone tool for building container images, regardless of where you deploy them.

But our open source efforts aren’t limited to Kubernetes — we also made significant contributions to Spinnaker 1.0, and helped launch the Istio and Grafeas projects. You ate up our "Partnering on open source" series, featuring the likes of HashiCorp, Chef, Ansible and Puppet. Availability-minded developers loved our Customer Reliability Engineering (CRE) team’s missive on release canaries, and with API design: Choosing between names and identifiers in URLs, our Apigee team showed them a nifty way to have their proverbial cake and eat it too.

Google innovation


In distributed database circles, Google’s Spanner is legendary, so many of you were delighted when we announced Cloud Spanner and a discussion of how it defies the CAP Theorem. Having a scalable database that offers strong consistency and great performance seemed to really change your conception of what’s possible — as did Cloud IoT Core, our platform for connecting and managing “things” at scale. CREs, meanwhile, showed you the Google way to handle an incident.

2017 was also the year machine learning became accessible. For those of you with large datasets, we showed you how to use Cloud Dataprep, Dataflow, and BigQuery to clean up and organize unstructured data. It turns out you don’t need a PhD to learn to use TensorFlow, and for visual learners, we explained how to visualize a variety of neural net architectures with TensorFlow Playground. One Google Developer Advocate even taught his middle-school son TensorFlow and basic linear algebra, as applied to a game of rock-paper-scissors.

Natural language processing also became a mainstay of machine learning-based applications; here, we highlighted with a lighthearted and relatable example. We launched the Video Intelligence API and showed how Cloud Machine Learning Engine simplifies the process of training a custom object detector. And the makers among you really went for a post that shows you how to add machine learning to your IoT projects with Google AIY Voice Kit. Talk about accessible!

Lastly, we want to thank all our customers, partners and readers for your continued loyalty and support this year, and wish you a peaceful, joyful, holiday season. And be sure to rest up and visit us again Next year. Because if you thought we had a lot to say in 2017, well, hold onto your hats.

With Google Kubernetes Engine regional clusters, master nodes are now highly available



We introduced highly available masters for Google Kubernetes Engine earlier this fall with our alpha launch of regional clusters. Today, regional clusters are in beta and ready to use at scale in Kubernetes Engine.

Regional clusters allow you to create a Kubernetes Engine cluster with a multi-master, highly available control plane that helps ensure higher cluster uptime. With regional clusters in Kubernetes Engine, you gain:
  • Resilience from single zone failure - Because your masters and nodes are available across a region rather than a single zone, your Kubernetes cluster is still fully functional if a zone goes down.
  • No downtime during master upgrades - Kubernetes Engine minimizes downtime during all Kubernetes master upgrades, but with a single master, some downtime is inevitable. By using regional clusters, the control plane remains online and available, even during upgrades.

How regional clusters work


When you create a regional cluster, Kubernetes Engine spreads your masters and nodes across three zones in a region, ensuring that you can experience a zonal failure and still remain online.

By default, Kubernetes Engine creates three nodes in each zone (giving you nine total nodes), but you can change the number of nodes in your cluster with the --num-nodes flag.
Creating a Kubernetes Engine regional cluster is simple. Let’s create a regional cluster with two nodes in each zone.

$ gcloud beta container clusters create my-regional-cluster --region=us-central1 --num-nodes=2

Or you can use the Cloud Console to create a regional cluster:
For a more detailed explanation of the regional clusters feature along with additional flags you can use, check out the documentation.

Kubernetes Engine regional clusters are offered at no additional charge during the beta period. We will announce pricing as part of general availability. Until then, please send any feedback to gke-regional-clusters-feedback@google.com.


Meet the Kubernetes Engine team at #KubeCon


This week the Kubernetes community gathers in Austin for the annual #KubeCon conference. The Google Cloud team will host various activities throughout the week. Join us for parties, workshops, and more than a dozen talks by experts. More info and ways to RSVP at g.co/kubecon.