Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.

Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.

Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.

With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE. 

“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.” 

Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem. 

Windows Server running on-prem with Anthos 

The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.

Get started today

When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.

Weather forecasting and climate modeling are two of the world’s most computationally complex and demanding tasks. Further, they’re extremely time-sensitive and in high demand — everyone from weekend travelers to large-scale industrial farming operators wants up-to-date weather predictions. To provide timely and meaningful predictions, weather forecasters usually rely on high performance computing (HPC) clusters hosted in an on-premises data center. These on-prem HPC systems require significant capital investment and have high long-term operational costs. They consume a lot of electricity, have largely fixed configurations, and the underlying computer hardware is replaced infrequently.

Using the cloud instead offers increased flexibility, constantly refreshed hardware, high reliability, geo-distributed compute and networking, and a “pay for what you use” pricing model. Ultimately, cloud computing allows forecasters and climate modelers to provide timely and accurate results on a flexible platform using the latest hardware and software systems, in a cost effective manner. This is a big shift compared with traditional approaches to weather forecasting, and can appear challenging. To help, weather forecasters can now run the Weather Research and Forecasting (WRF) modeling system easily on Google Cloud using the new WRF VM image from Fluid Numerics, and achieve the performance of an on-premises supercomputer for a fraction of the price. With this solution, weather forecasters can get a WRF simulation up and running on Google Cloud in less than an hour!

A closer look at WRF
Weather Research and Forecasting (WRF) is a popular open-source numerical weather prediction modeling system used by both researchers and operational organizations. While WRF is primarily used for weather and climate simulation, teams have extended it to support interactions with chemistry, forest fire modeling, and other use cases. WRF development began in the late 1990s through a collaboration between the National Center for Atmospheric Research (NCAR), National Oceanic and Atmospheric Administration (NOAA), U.S. Air Force, Naval Research Laboratory, University of Oklahoma, and the Federal Aviation Administration. The WRF community comprises more than 48,000 users spanning over 160 countries, with the shared goal of supporting atmospheric research and operational forecasting.

The Google Cloud WRF image is built using Google’s MPI best practices for HPC, with the exception that hyperthreading is not disabled by default, and is easily integrated with other HPC solutions on Google Cloud, including SchedMD’s Slurm-GCP. Normally, installing WRF and its dependencies is a time consuming process. With these new WRF VM images, deploying a scalable HPC cluster with WRF v4.2 pre-installed is quick and easy with our Codelab. OpenMPI 4.0.2 was used throughout this work. Google has had good success with Intel MPI, and we intend to study whether further performance gains can be achieved in this context.

Optimizing WRF

Determining the optimal architecture and build settings for performance and cost was a key part of the process in developing the WRF images. We evaluated how to select the ideal compiler, right CPU platform, and the best file system for handling file IO, so you don’t have to. As a test case for assessing performance, we used the CONUS 2.5km benchmark.

Below, the CONUS 2.5km runtime and cost figure shows the run time required for simulating WRF over a two-hour forecast using 480 MPI ranks (a way of numbering processes) for different machine types available on Google Cloud. For each machine type, we’re showing the lowest measured run time from a suite of tests that varied compiler, compiler optimizations, and task affinity.

We found that compute-optimized c2 instances provided the shortest run time. The Slurm job scheduler allows you to map the MPI tasks to compute hardware using task affinity flags. When optimizing the runtime and cost for each machine type, we compared using srun –map-by core –bind-to core to launch WRF, which maps each MPI process to a physical core (two vCPU per MPI rank), and srun –map-by thread –bind-to thread, which maps each MPI process to a single vCPU. Mapping by core and binding MPI ranks to cores is akin to disabling hyperthreading.

The ideal simulation cost and runtime for CONUS 2.5km for each platform is found when each MPI rank is subscribed to each vCPU. When binding to vCPUs, half as many compute resources are needed when compared to binding to physical cores lowering the per-second cost for the simulation. For CONUS 2.5km, we also found that although mapping MPI ranks to cores results in reduced runtime for the same number of MPI ranks, the performance gains are not significant enough to outweigh the cost savings. For this reason, the WRF-GCP solution does not disable hyperthreading by default.

Runtime and simulation cost can be further reduced by selecting an ideal compiler: the figure below (CONUS 2.5km Compiler Comparisons) shows the simulation runtime for the WRF CONUS 2.5km benchmark on eight c2-standard-60 instances, using GCC 10.30, GCC 11.2.0 and the Intel® OneAPI® compilers (v2021.2.0). In all cases, WRF is built using level 3 compiler optimizations and Cascade Lake target architecture flags. By compiling WRF with the Intel® OneAPI® compilers, the WRF simulation runs about 47% faster than the GCC builds, and at about 68% of the cost, on the same hardware. We’ve used OpenMPI 4.0.2 with each of the compilers as the MPI implementation in this work. With other applications, Google has seen good performance with Intel MPI 2018, and we intend to investigate performance comparisons with this and other MPI implementations.

File IO in WRF can become a significant bottleneck as the number of MPI ranks increases. Obtaining the optimal file IO performance requires using parallel file IO in WRF and leveraging a parallel file system such as Lustre.

Below, we show the speedup in file IO activities relative to serial IO on an NFS file system. For this example, we are running the CONUS 2.5km benchmark on c2-standard-60 instances with 960 MPI ranks. By changing WRF’s file IO strategy to parallel IO, we accelerate file IO time by a factor of 60.

We further speed up IO and reduce simulation costs by using a Lustre parallel file system deployed from open-source Lustre Terraform infrastructure-as-code from Fluid Numerics. Lustre is also available with support from DDN’s EXAScaler solution in the Google Cloud Marketplace. In this case, we use four n2-standard-16 instances for the Lustre Object Storage Server (OSS) instances, each with 3TB of Local SSD. The Lustre Metadata Server (MDS) is an n2-standard-16 instance with a 1TB PD-SSD disk. After mounting the Lustre file system to the cluster, we set the Lustre stripe count to 4 so that file IO can be distributed across the four OSS instances. By switching to the Lustre file system for IO, we speed up file IO by an additional factor of 193, which is orders of magnitude faster than a single NFS server with serial IO.

Adding compute resources and increasing the number of MPI ranks reduces the simulation run time. Ideally, with perfect linear scaling, doubling the number of MPI ranks would cut the simulation time in half. However, adding MPI ranks also increases communication overhead, which can increase the cost per simulation. The communication overhead is due to the increased amount of communication necessitated by splitting the problem more finely across more machines.

To assess the scalability of WRF for the CONUS 2.5km benchmark, we can execute a series of model forecasts where we successively double the number of MPI ranks. Below, we show two- hour forecasts on the c2-standard-60 instances with the Lustre file system, varying the number of MPI ranks from 480 to 1920. In all of these runs, MPI ranks are bound to vCPUs so that the number of vCPUs dedicated to each simulation increases with the increase in MPI ranks. While many HPC workloads run best with simultaneous multithreading (SMT) disabled, we find the best performance for CONUS 2.5km with SMT enabled. Thus, the number of MPI ranks in our runs equals the total number of vCPUs.

As you can see, the CONUS 2.5km Runtime & Cost Scaling figure shows that the run time (blue bars) decreases as the number of MPI ranks and the amount of compute resources increases, at least up to 1920 ranks. When transitioning from 480 to 960 MPI ranks, the run time drops, yielding a speedup of about 1.8x. Doubling again to 1920 MPI ranks, though, we obtained an additional speedup of just 1.5x. This declining trend in the speedup with increasing MPI ranks is a signature of MPI overhead, which increases with more MPI ranks.

Determining your best fit

Most tightly-coupled MPI applications such as WRF exhibit this kind of scaling behavior, where scaling efficiency decreases with increasing MPI ranks. This makes assessing cost-scaling alongside performance-scaling critical when considering Total Cost of Ownership (TCO). Thankfully, per-second billing on Google Cloud makes this kind of analysis a little bit easier. As shown above, a second doubling of the count from 960 cores to 1920 cores can provide an additional 1.5x speedup, but at a 32% higher cost. In some circumstances, this faster turnaround may be needed and worth the extra cost.

If you want to get started with WRF quickly and experiment with the CONUS 2.5km benchmark, we’ve encapsulated this deployment in Terraform scripts and prepared an accompanying codelab.

You can learn more about Google Cloud’s high performance computing offerings at https://cloud.google.com/hpc, and you can find out more about Google’s partner Fluid Numerics at https://www.fluidnumerics.com.

Welcome to a new way of exploring Google Cloud products. Finding your favorite products and discovering new ones requires a user interface that’s easy-to-use, clear, informative, and delightful. Google Cloud users have primarily used our side menu to navigate, but with almost one hundred products and growing, it’s safe to say our product list has outgrown the side menu. Over time, we have listened long and hard to feedback from Google Cloud users, who have highlighted challenges navigating the console to explore our products. We’ve heard over and over that it’s difficult and time consuming to scroll through the long list of products and remember what each one offers. 

That’s why we created a new All products page to help you easily navigate to your favorite Google Cloud products. This page showcases all of the Google Cloud products as well as our key partner products in one, easy-to-navigate place. With one click, you can discover the right product that is right for your solution.

Explore all Google Cloud products

The page is organized into different categories, including Management (ie. IAM, Billing), Compute, Storage, Operations, Security, CI/CD, Artificial Intelligence, Support, and more. Quickly jump to the category of interest through the panel on the left, or you can scroll the entire page. Then you can click each product name to navigate directly to the product homepage. Each product listing also includes a short and long description so you can quickly understand what a product does and whether it fits your needs. This lets you compare categories at-a-glance, saving you the hassle of digging up product overviews elsewhere. 

Under each product, you’ll also find a link to documentation and Quickstarts so you can understand it in more depth and try it out right away, removing the extra step of navigating to documentation in another tab.

Customize your navigation

To make navigation even easier, you can pin products directly from the All products page, and they will show up at the top of your side menu. You can also customize your navigation by reordering your pins in the side menu. That way, you can quickly access your most-used products directly from the side menu instead of scrolling through the panel or the All products page. 

Save time and get more done faster

With the new All products page you can save time scrolling and cut straight to the good stuff – finding your products, discovering new ones, learning, and getting hands on. Try it out for yourself by heading to the Google Cloud Console. Click the side panel and click “View All products,” or on the home dashboard you’ll see a call out to try out the All products page.

If you have any feedback about this new experience, I want to hear! Reach out to me on Twitter at @stephr_wong or on Linkedin at stephrwong.

Engineers that build last-mile delivery services belong to an elite order, a hallowed subcategory. Delivery customers are incredibly demanding when it comes to speed and convenience, and the services they use must take variables like increased traffic, road conditions, human error, and even driver availability into account every day.

Nuro is a company with a new approach to delivery services. Nuro has a fleet of autonomous vehicles designed to address many of the problems related to last-mile delivery. And every day, these vehicles — and their sensors — generate a lot of data before parking for the night. For Nuro engineers, that data can help them understand the impact of new on-road features, make improvements to their vehicles’ software, and ensure even better deliveries for their customers.

For Nuro, the key challenge is how to move petabytes of data as quickly, securely, and easily as possible from their edge environments, like vehicle depots, to Google’s Cloud Storage. For this delivery effort, Nuro selected Google’s Transfer Appliance with its new online transfer capability, now generally available.

Helping Nuro to speed up data delivery from the edge to the cloud

Like many Google Cloud customers, Nuro collects data from remote environments, like vehicle depots, that have different networking and storage capabilities when compared to a traditional data center. For a transfer solution to be effective moving unstructured data from these environments to the cloud, the solution needs to be easy to deploy and automate, while still providing similar performance as a more complicated alternative.

The Transfer Appliance was built for this use case. It arrives to customers as a physical appliance with a preconfigured version of Google’s Storage Transfer Service software already installed. Customers can move files to the appliance by using SFTP or SCP, or, alternately, can mount the appliance as an NFS share and copy target. Data can be stored locally on the appliance or transferred over the network, and secure encryption — at-rest and in-flight — is enabled by default.

With these new appliances, Nuro will be able to automate much of their storage transfer needs. When their autonomous vehicles return to the depot, they can move data like software logs, LIDAR data, and sensor data — all ideal fits for Google’s Cloud Storage — from parked vehicles to the Transfer Appliance. Online transfers can then be performed throughout the day, ensuring a steady stream of valuable data in the cloud for developers to analyze and use in their nightly builds. All of this will help Nuro’s engineering leaders like Jie Pan to run more productive development teams with less operational overhead.

“Our autonomous vehicles generate a tremendous amount of useful data, and our goal is to get that data to our engineers as soon as possible,” said Jie Pan, Engineering Manager at Nuro. “When vehicles return to the depot, we can move data hourly into Cloud Storage over the network. We also have the flexibility to return the Transfer Appliance back to Google Cloud. Most importantly, this rapid transfer architecture gives a meaningful boost to engineering productivity and development velocity.”

Going the extra mile

Engineering and infrastructure leaders understand the value of delivering the right data to the right teams, as fast as possible. By adding preconfigured, over-the-network transfer into a turnkey Transfer Appliance, Google Cloud customers can more easily automate these data deliveries by scheduling regular migrations of on-premises files, objects, and other unstructured data to our Cloud Storage.

As Nuro continues to grow their manufacturing and testing footprint, they plan to use Transfer Appliances to further scale and simplify their data migration from on-premises to Google Cloud. Cutting the time to migrate their data by more than half will make for happier, more productive developers, and that will help Nuro bring us all the future of delivery a little faster.

If you’d like to learn more about Transfer Appliance and its new online transfer capability, click here or reach out to your Google Cloud account team.

Digital transformation and migration to the cloud are top priorities for a lot of enterprises. At Google Cloud, we’re working hard to make this journey easier. For example, we recently launched Migrate for Compute Engine and Migrate for Anthos to simplify cloud migration and modernization. These services have helped customers like Cardinal Health perform successful, large-scale migrations to GCP. 

But we understand that the migration journey can be daunting. To make things easier, we developed a whitepaper on application migration featuring investigative processes and advice to help you design an effective migration and modernization strategy. This guide outlines the four basic steps you need to follow to migrate successfully and efficiently:  

1. Build an inventory of your applications and infrastructure: Understanding how many items, such as applications and hardware appliances, exist in your current environment is an important first step.
2. Categorize your applications: Analyze the characteristics of all of your applications and evaluate them across two dimensions: migration to cloud, and modernization.
3. Decide whether or not to migrate an application to the cloud: Not all applications should move to the cloud quite yet. The whitepaper lists the questions to ask to determine whether or not to migrate a given application.
4. Pick your migration strategy: For the applications you decided to migrate, decide on your ideal strategy—pure lift and shift, containers, cloud managed services, or a combination thereof.

There’s a lot to consider when you start thinking about digital transformation, and every cloud modernization project has its nuances and unique considerations. The secret to success is understanding the advantages and disadvantages of the options at your disposal, and weighing them against what you want to transform and why. To learn how to migrate and modernize your applications with Google Cloud, download this whitepaper.